From 053aea1b845c584656c91c50a1014b900ca391cd Mon Sep 17 00:00:00 2001
From: Ali Pirani <alipirani88@gmail.com>
Date: Wed, 19 Dec 2018 15:50:47 -0500
Subject: [PATCH] Matrix changes and GATK Indel integration

---
 .gitignore                                    |  18 +
 .idea/workspace.xml                           | 194 ++--
 README.md                                     | 207 ++--
 barplot.png                                   | Bin 442748 -> 0 bytes
 barplot_DP.png                                | Bin 452495 -> 0 bytes
 config_ali                                    |  49 +-
 config_gatk                                   | 237 +++++
 modules/beast/SCOTTI_generate_xml.py          | 412 ++++++++
 .../fasta_functions.cpython-35.pyc            | Bin 0 -> 8056 bytes
 .../scotti_functions.cpython-35.pyc           | Bin 0 -> 31536 bytes
 modules/beast/add_invariant_sites_beast.sh    |  14 +
 modules/beast/beast.py                        | 102 ++
 modules/beast/beast_pbs_flux.pl               | 100 ++
 modules/beast/change_prefixes_beast_xml.sh    |  24 +
 modules/beast/countInvarSites.py              |  56 ++
 modules/beast/fasta_functions.py              | 275 +++++
 modules/beast/fasta_functions.pyc             | Bin 0 -> 1435 bytes
 .../beast/generate_input_file_beast_pbs.sh    |  12 +
 modules/beast/get_num_invariant_sites_v2.py   |  89 ++
 modules/beast/insert_starting_tree_beast.sh   |  17 +
 modules/beast/scotti.py                       | 108 ++
 modules/beast/scotti_functions.py             | 759 ++++++++++++++
 modules/beast/scotti_functions.py.bak         | 408 ++++++++
 modules/beast/scotti_functions.pyc            | Bin 0 -> 30618 bytes
 modules/beast/test.csv                        |   2 +
 modules/beast/testfn.py                       |  17 +
 modules/cluster_based_prep/log_modules.py     |  30 -
 .../cluster_based_prep/logging_subprocess.py  |  41 -
 .../prep_cluster_calling.py                   |  97 --
 modules/core_prep_sanity_checks.py            |  34 +-
 modules/core_prep_sanity_checks.pyc           | Bin 3934 -> 4050 bytes
 modules/gatk.py                               | 318 +++---
 modules/gatk.pyc                              | Bin 12873 -> 13835 bytes
 modules/phage_detection.py                    |  22 +-
 modules/phage_detection.pyc                   | Bin 5203 -> 5607 bytes
 modules/remove_5_bp_snp_indel.py              | 288 ++++--
 modules/remove_5_bp_snp_indel.pyc             | Bin 3715 -> 6467 bytes
 modules/stages.py                             |  36 +-
 modules/stages.pyc                            | Bin 10124 -> 9287 bytes
 modules/variant_diagnostics/DP_analysis.py    | 102 +-
 .../R_scripts/non_core_variant_qc.R           | 122 +++
 .../R_scripts/render_non_core_variant_qc.R    |  12 +
 modules/variant_diagnostics/core_pipeline.py  | 939 +++++++++++++-----
 ...only_ref_variant_fasta_unique_positions.py |  22 +-
 .../mask_gubbins_variants.py                  |  10 +-
 .../reason_job_indel_debug.py                 |   4 +-
 .../reason_job_indel_debug_gatk.py            | 173 ++++
 .../scripts/gubbins_iqtree_raxml.sh           |  52 +-
 .../gubbins_iqtree_raxml_backup_2018_10_31.sh |  82 ++
 other/FQ_analysis.py                          |  78 --
 other/README.md                               |   2 -
 other/__init__.py                             |   1 -
 other/allele_frequency_analysis.py            |  93 --
 other/config_backup                           | 160 ---
 other/config_default_21dec                    | 172 ----
 other/config_gatk                             |  93 --
 other/pipeline.py.save                        | 139 ---
 other/pipeline_gatk.py                        | 101 --
 pipeline.png                                  | Bin 229170 -> 0 bytes
 pipeline.py                                   |  67 +-
 plots/Sample_barplot.png                      | Bin 227887 -> 0 bytes
 variant_call.py                               | 528 ++++++++--
 variant_call_backup.py                        | 643 ------------
 variant_call_debug.py                         | 651 ------------
 variant_calling_pipeline_dev/other            |   1 -
 65 files changed, 5021 insertions(+), 3192 deletions(-)
 create mode 100644 .gitignore
 delete mode 100644 barplot.png
 delete mode 100644 barplot_DP.png
 create mode 100755 config_gatk
 create mode 100644 modules/beast/SCOTTI_generate_xml.py
 create mode 100644 modules/beast/__pycache__/fasta_functions.cpython-35.pyc
 create mode 100644 modules/beast/__pycache__/scotti_functions.cpython-35.pyc
 create mode 100755 modules/beast/add_invariant_sites_beast.sh
 create mode 100644 modules/beast/beast.py
 create mode 100755 modules/beast/beast_pbs_flux.pl
 create mode 100755 modules/beast/change_prefixes_beast_xml.sh
 create mode 100644 modules/beast/countInvarSites.py
 create mode 100755 modules/beast/fasta_functions.py
 create mode 100644 modules/beast/fasta_functions.pyc
 create mode 100755 modules/beast/generate_input_file_beast_pbs.sh
 create mode 100755 modules/beast/get_num_invariant_sites_v2.py
 create mode 100755 modules/beast/insert_starting_tree_beast.sh
 create mode 100644 modules/beast/scotti.py
 create mode 100644 modules/beast/scotti_functions.py
 create mode 100644 modules/beast/scotti_functions.py.bak
 create mode 100644 modules/beast/scotti_functions.pyc
 create mode 100644 modules/beast/test.csv
 create mode 100644 modules/beast/testfn.py
 delete mode 100644 modules/cluster_based_prep/log_modules.py
 delete mode 100644 modules/cluster_based_prep/logging_subprocess.py
 delete mode 100644 modules/cluster_based_prep/prep_cluster_calling.py
 create mode 100755 modules/variant_diagnostics/R_scripts/non_core_variant_qc.R
 create mode 100755 modules/variant_diagnostics/R_scripts/render_non_core_variant_qc.R
 create mode 100755 modules/variant_diagnostics/reason_job_indel_debug_gatk.py
 create mode 100755 modules/variant_diagnostics/scripts/gubbins_iqtree_raxml_backup_2018_10_31.sh
 delete mode 100755 other/FQ_analysis.py
 delete mode 100755 other/README.md
 delete mode 100755 other/__init__.py
 delete mode 100755 other/allele_frequency_analysis.py
 delete mode 100755 other/config_backup
 delete mode 100755 other/config_default_21dec
 delete mode 100755 other/config_gatk
 delete mode 100755 other/pipeline.py.save
 delete mode 100755 other/pipeline_gatk.py
 delete mode 100644 pipeline.png
 delete mode 100644 plots/Sample_barplot.png
 delete mode 100644 variant_call_backup.py
 delete mode 100644 variant_call_debug.py
 delete mode 160000 variant_calling_pipeline_dev/other

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..98fc96d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,18 @@
+modules/beast/test/2018_08_15_10_45_01_KPNIH1_ref_allele_unmapped_consensus_gubbins_masked.fa
+modules/beast/test/2018_08_15_10_45_01_KPNIH1_ref_allele_unmapped_consensus_gubbins_masked_invar_site_counts.txt
+modules/beast/test/2018_08_15_10_45_01_KPNIH1_ref_allele_unmapped_consensus_gubbins_masked_snp-sites.vcf
+modules/beast/test/2018_08_15_10_45_01_KPNIH1_ref_allele_unmapped_consensus_gubbins_masked_var_sites.fa
+modules/beast/test/2018_08_15_10_45_01_KPNIH1_ref_allele_unmapped_consensus_invar_site_counts.txt
+modules/beast/test/2018_08_15_10_45_01_KPNIH1_ref_allele_unmapped_consensus_masked_recomb_positions.txt
+modules/beast/test/2018_08_15_10_45_01_KPNIH1_ref_allele_unmapped_consensus_snp-sites.vcf
+modules/beast/test/input_beast.txt
+modules/beast/test/invar_base_counts.txt
+modules/beast/test/model_finder_IQTREE_noDec_wDates_LA.tree
+modules/beast/test/penn-st258_LA_bmt_ucln_bs_dta2_renamed.xml
+modules/beast/test/penn-st258_LA_bmt_ucln_bs_dta2.xml
+modules/beast/test/penn-st258_LA_bmt_ucln_bs_dta_renamed_st_invSites.xml
+modules/beast/test/penn-st258_LA_bmt_ucln_bs_dta_renamed.xml
+modules/beast/test/penn-st258_LA_bmt_ucln_bs_dta.xml
+modules/beast/test/test_commands.sh
+modules/beast/test/test_fasta_path.txt
+modules/beast/test/test_gff_path.txt
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index b54d3a9..e2df7eb 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -2,103 +2,7 @@
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@@ -162,10 +83,10 @@
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@@ -181,10 +101,22 @@
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+              <item name="variant_calling_pipeline" type="462c0819:PsiDirectoryNode" />
+              <item name="modules" type="462c0819:PsiDirectoryNode" />
+            </path>
+            <path>
+              <item name="variant_calling_pipeline" type="b2602c69:ProjectViewProjectNode" />
+              <item name="variant_calling_pipeline" type="462c0819:PsiDirectoryNode" />
+              <item name="modules" type="462c0819:PsiDirectoryNode" />
+              <item name="variant_diagnostics" type="462c0819:PsiDirectoryNode" />
+            </path>
           </expand>
           <select />
         </subPane>
       </pane>
+      <pane id="Scope" />
     </panes>
   </component>
   <component name="PropertiesComponent">
@@ -383,9 +315,9 @@
     <servers />
   </component>
   <component name="ToolWindowManager">
-    <frame x="0" y="24" width="2560" height="1321" extended-state="6" />
+    <frame x="0" y="23" width="2560" height="1323" extended-state="1" />
     <layout>
-      <window_info active="true" content_ui="combo" id="Project" order="0" visible="true" weight="0.098490864" />
+      <window_info active="true" content_ui="combo" id="Project" order="0" visible="true" weight="0.09928515" />
       <window_info anchor="bottom" id="TODO" order="6" />
       <window_info anchor="right" id="R Packages" order="3" />
       <window_info anchor="right" id="R Graphics" order="3" />
@@ -420,6 +352,23 @@
     <option name="myLimit" value="2678400000" />
   </component>
   <component name="editorHistoryManager">
+    <entry file="file://$PROJECT_DIR$/variant_call.py">
+      <provider selected="true" editor-type-id="text-editor">
+        <state relative-caret-position="4872">
+          <caret line="249" column="48" lean-forward="true" selection-start-line="249" selection-start-column="48" selection-end-line="249" selection-end-column="48" />
+        </state>
+      </provider>
+    </entry>
+    <entry file="file://$PROJECT_DIR$/README.md">
+      <provider selected="true" editor-type-id="split-provider[text-editor;markdown-preview-editor]">
+        <state split_layout="SPLIT">
+          <first_editor relative-caret-position="6069">
+            <caret line="289" selection-start-line="289" selection-end-line="289" />
+          </first_editor>
+          <second_editor />
+        </state>
+      </provider>
+    </entry>
     <entry file="file://$PROJECT_DIR$/variant_call.py">
       <provider selected="true" editor-type-id="text-editor">
         <state relative-caret-position="5229">
@@ -508,25 +457,36 @@
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     <entry file="file://$PROJECT_DIR$/config" />
     <entry file="file://$PROJECT_DIR$/modules/variant_diagnostics/core_pipeline.py" />
-    <entry file="file://$PROJECT_DIR$/pipeline.py" />
-    <entry file="file://$PROJECT_DIR$/modules/variant_diagnostics/core_pipeline.py" />
-    <entry file="file://$PROJECT_DIR$/variant_call.py">
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     <entry file="file://$PROJECT_DIR$/config_ali" />
     <entry file="file://$PROJECT_DIR$/README.md">
       <provider selected="true" editor-type-id="split-provider[text-editor;markdown-preview-editor]">
         <state split_layout="SPLIT">
-          <first_editor relative-caret-position="1117">
+          <first_editor relative-caret-position="990">
             <caret line="289" selection-start-line="289" selection-end-line="289" />
           </first_editor>
           <second_editor />
         </state>
       </provider>
     </entry>
+    <entry file="file://$PROJECT_DIR$/pipeline.py">
+      <provider selected="true" editor-type-id="text-editor" />
+    </entry>
+    <entry file="file://$PROJECT_DIR$/variant_call.py">
+      <provider selected="true" editor-type-id="text-editor">
+        <state relative-caret-position="-6532">
+          <caret line="249" column="48" selection-start-line="249" selection-start-column="48" selection-end-line="249" selection-end-column="48" />
+        </state>
+      </provider>
+    </entry>
+    <entry file="file://$PROJECT_DIR$/modules/variant_diagnostics/core_pipeline.py">
+      <provider selected="true" editor-type-id="text-editor">
+        <state relative-caret-position="167">
+          <caret line="8" column="73" lean-forward="true" selection-start-line="8" selection-start-column="73" selection-end-line="8" selection-end-column="73" />
+          <folding>
+            <element signature="e#0#31#0" expanded="true" />
+          </folding>
+        </state>
+      </provider>
+    </entry>
   </component>
 </project>
\ No newline at end of file
diff --git a/README.md b/README.md
index 65e139f..f344cbc 100644
--- a/README.md
+++ b/README.md
@@ -74,27 +74,17 @@ You can run a part of the pipeline by customizing the order of the -steps argume
 
 Note: The order of variant calling steps needs to be sequential. If skipping any of the steps, make sure those skipped steps had previously finished without any errors.
 
+**2. Generate various Core/Non-core SNP consensus, SNP/Indel Matrices and recombination filtering with Gubbins:**
 
-**2. Preparing files for Core SNP extraction and diagnostics purposes:**
+Option ***core_All***: This will run all the core SNP consensus/matrix generating steps.
 
+This step will run in sequential order and generate:
 
-Option ***core_prep***: Run this step before running the last core steps. This will generate all the intermediate data files required for core SNP matrix/consensus.
+- intermediate data files required for generating core SNP matrix/consensus.
+- Various data matrices will be generated during this step that can be used for diagnosing variant filter criterias and their impact on overall distribution of core variants.
+- prefix_core_results directory under the output directory which will be the final results folder.
+- Gubbins recombination filtered consensus fasta files and RaxML/Iqtree trees generated from this recombination filtered consensus.
 
-**3. Generate core SNP consensus and data matrix for diagnostics plots:**
-
-Option ***core***: This step will generate core SNP/Indel Matrix and different types of consensus fasta files. Various data matrices will be generated during this step that can be used for diagnosing variant filter criterias and their impact on the overall distribution core variants. 
-
-**4. Generate report and aggregate results for the pipeline:**
-
-Option ***report*** : This step will aggregate the results in prefix_core_results directory under the output directory.
-
-**4. Generate report and aggregate results for the pipeline:**
-
-Option ***report***: This step will aggregate and move final results to prefix_core_results directory under the output directory.
-
-**5. Phylogenetic reconstruction and recombination filtering using FastTree/RAxML/Gubbins:**
-
-Option ***tree***: This step will generate a FastTree and a RAxML tree from pre-recombination filtered consensus files. If the -gubbins option is set to yes, it will run gubbins on all final consensus files.
 
 ## Command line options
 
@@ -103,10 +93,10 @@ Option ***tree***: This step will generate a FastTree and a RAxML tree from pre-
 usage: variant_call.py [-h] -type TYPE -readsdir DIR -outdir OUTPUT_FOLDER
                        -index INDEX [-steps STEPS] -analysis ANALYSIS_NAME
                        [-config CONFIG] [-suffix SUFFIX]
-                       [-filenames FILENAMES] [-cluster CLUSTER]
-                       [-clean CLEAN] [-extract_unmapped EXTRACT_UNMAPPED]
-                       [-datadir DATADIR] [-snpeff_db SNPEFF_DB]
-                       [-debug_mode DEBUG_MODE] [-gubbins GUBBINS]
+                       [-filenames FILENAMES] [-cluster CLUSTER] [-clean]
+                       [-extract_unmapped EXTRACT_UNMAPPED] [-datadir DATADIR]
+                       [-snpeff_db SNPEFF_DB] [-debug_mode DEBUG_MODE]
+                       [-gubbins GUBBINS]
 
 Variant Calling pipeline for Illumina PE/SE data.
 
@@ -115,39 +105,33 @@ optional arguments:
 
 Required arguments:
   -type TYPE            Type of reads: SE or PE
-  -readsdir DIR         Path to sequencing reads data directory. NOTE: Provide full/absolute path.
-  -outdir OUTPUT_FOLDER Output folder path ending with output directory name to save the results. Creates a new output directory path if it doesn't exist. NOTE: Provide full/absolute path.
-  -index INDEX          Reference index name. Most frequently used reference genomes index options: KPNIH1 | MRSA_USA_300 | MRSA_USA_100 | CDIFF_630 | paris. Make sure the paths are properly set in config file.
-  -steps STEPS          Variant calling steps in sequential order.
-                        1.   All: This will run all the steps starting from cleaning the reads to variant calling;
-                        2.   clean,align,post-align,varcall,filter,stats : This will also run all steps from cleaning to variant calling. 
-                        You can also run part of the pipeline by giving, for example, "align,post-align,varcall,filter,stats" which will skip the cleaning part.
-                        The order must be sequential while using this option. Also, if skipping any of the steps, make sure you have the results already present in your output folder.
-                        3.   coverage_depth_stats: Run only depth of coverage stats module after the cleaning and read mapping steps
-                        4.   core_prep: Run this step before running the core steps. This will prepare the data required for generating core SNPs
-                        5.   core: extract core snps and generate diagnostics plot data matrices to explore filtered snps.
-  -analysis ANALYSIS_NAME Unique analysis name that will be used as a prefix when saving results and log files.
+  -readsdir DIR         Path to Sequencing Reads Data directory. NOTE: Provide full/absolute path.
+  -outdir OUTPUT_FOLDER
+                        Output Folder Path ending with output directory name to save the results. Creates a new output directory path if it doesn't exist. NOTE: Provide full/absolute path.
+  -index INDEX          Reference Index Name. Most Frequently used reference genomes index options: KPNIH1 | MRSA_USA_300 | MRSA_USA_100 | CDIFF_630 | paris Make sure the paths are properly set in config file
+  -steps STEPS          Variant Calling Steps in sequential order.
+                        1.   All: Run all variant calling  steps starting from trimming the reads, mapping, post-processing the alignments and calling variants;
+                        2.   core_All: Extract core snps and generate different types of alignments, SNP/Indel Matrices and diagnostics plots to explore filtered snps.
+  -analysis ANALYSIS_NAME
+                        Unique analysis name that will be used as prefix to saving results and log files.
 
 Optional arguments:
-  -config CONFIG        Path to config file. Make sure to check config settings before running the pipeline
+  -config CONFIG        Path to Config file, Make sure to check config settings before running pipeline
   -suffix SUFFIX        Fastq reads suffix such as fastq, fastq.gz, fq.gz, fq; Default: fastq.gz
   -filenames FILENAMES  fastq filenames with one single-end filename per line. 
-                        If the type is set to PE, it will detect the second paired-end filename with the suffix from the first filename. 
-                        Useful for running variant calling pipeline on selected files in a reads directory or extracting core snps for selected samples in the input reads directory. 
-                        Otherwise the pipeline will consider all the samples available in the reads directory.
-  -cluster CLUSTER      Run variant calling pipeline in one of the four modes. Default: local. The suggested mode for core snp is a cluster that will run all the steps in parallel with the available cores. Make sure to provide a large memory node for this option
-                        The possible modes are: cluster/parallel-cluster/parallel-local/local
-                        cluster: Runs all the jobs on a single large cluster. This will mimic the local run but rather on a large compute node.
-                        parallel-cluster: Submit variant call jobs for each sample in parallel on compute nodes. This mode is no available for core snp extraction step.
-                        parallel-local: Run variant call jobs for each sample in parallel locally.
-                        local: Run variant call jobs locally.
-                        Make sure to check if the [scheduler] section in the config file is set up correctly for your cluster.
-  -clean CLEAN          Clean up intermediate files. Default: OFF
-  -extract_unmapped     EXTRACT_UNMAPPED
-                        Extract unmapped reads, assemble them and detect antimicrobial resistance (AMR) genes using ariba
+                        If the type is set to PE, it will detect the second paired-end filename with the suffix from first filename. 
+                        Useful for running variant calling pipeline on selected files in a reads directory or extracting core snps for selected samples in input reads directory. 
+                        Otherwise the pipeline will consider all the samples available in reads directory.
+  -cluster CLUSTER      Run variant calling pipeline in one of the four modes. Default: local. Suggested mode for core snp is "cluster" that will run all the steps in parallel with the available cores. Make sure to provide a large memory node for this option
+                        The possible modes are: cluster/parallel-local/local
+                        Set your specific hpc cluster parameters in config file under the [scheduler] section. Supports only PBS scheduling system. 
+  -clean                clean up intermediate files. Default: OFF
+  -extract_unmapped EXTRACT_UNMAPPED
+                        Extract unmapped reads, assemble it and detect AMR genes using ariba
   -datadir DATADIR      Path to snpEff data directory
-  -snpeff_db SNPEFF_DB  Name of pre-built snpEff database to use for annotation
-  -debug_mode DEBUG_MODE yes/no for debug mode
+  -snpeff_db SNPEFF_DB  Name of pre-build snpEff database to use for Annotation
+  -debug_mode DEBUG_MODE
+                        yes/no for debug mode
   -gubbins GUBBINS      yes/no for running gubbins
 
 ```
@@ -166,15 +150,8 @@ Possible options for the -cluster option (Supported system: pbs):
 
 ***parallel-local***:  This option will run the pipeline and analyze samples in parallel but on a local system. This is preferred for an input sample size of less than 20  and a multiple core local system.
 
-***cluster***: This option will run the pipeline on a single cluster. This option is similar to local but will rather run on a cluster node. It will not make use of multiple cores present on a cluster. Use this option for small numbers of samples or for testing purposes or for small numbers of large size samples which require multiple cores to analyze individual samples.
-
-***parallel-cluster***: The variant call step (All) is optimized for this option. When this option is set, the pipeline will run variant call jobs for each sample on an individual compute cluster. When you set this option for each step in the pipeline, make sure all the jobs submitted by the pipeline at each step is completed before proceeding to another step. You can check the status of the job with: 
-
-```
-qstat -u USERNAME  
-```
+***cluster***: The pipeline is optimized for this option. When this option is set, the pipeline will run variant call jobs for each sample on individual small compute clusters or on a single large cluster for core snp generation step. 
 
-Note: Use parallel-cluster mode for All/clean,align,post-align,varcall,filter,stats steps. Use cluster/parallel-local mode for core_prep and core steps. parallel-cluster and cluster will be merged in the next release.
 
 ## Quick Start
 
@@ -188,7 +165,7 @@ See option resources in the scheduler section of the [config](https://github.com
 - Run variant calling step (All) on a set of PE reads with default parameters
 
 ```
-python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps All -cluster parallel-cluster
+python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps All -cluster cluster
 
 ```
 
@@ -196,123 +173,57 @@ The above command will run the variant calling (step 1) pipeline on a set of PE
 
 The results of variant calling will be placed in an individual folder generated for each sample in the output directory. A log file for each sample will be generated and can be found in each sample folder inside the output directory. A single log file of this step will be generated in the main output directory. For more information on log file prefix and convention, please refer to the [log](#log) section below.
 
-- Run the core_prep step to generate files for core SNP calling.
-
-Run this steps to generate various intermediate files that will be used for generating core SNPs.
-
-```
-python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps core_prep -cluster cluster
-
-```
-
-- Run the core step to generate final core SNP consensus fasta files.
+- Generate core SNPs/Matrices from variant calling results 
 
 Since this step compares multiple files simultaneously and involves multiple I/O operations, it is recommended to provide higher memory compute resources. 
 
 example:
 
 ```
-nodes=1:ppn=4,mem=47000mb,walltime=24:00:00
+nodes=1:ppn=12,mem=47000mb,walltime=250:00:00
 ```
 
 Replace the resources option in the scheduler section of the config file with the above line before running the command.
 
 ```
-python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps core -cluster cluster
-
-```
-
-- Run the report step to aggregate results under the \*_core_results folder
-
-```
-python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps report -cluster cluster
-
-```
-
-- Run the tree step to generate FastTree and RAxML phylogenetic trees and recombination filtering using Gubbins
-
-```
-python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps tree -cluster cluster -gubbins yes
+python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps core_All -cluster cluster -gubbins yes
 
 ```
 
 ## Output Files
 
-All the final results from the report step will be saved under the date_time_core_results directory under the output folder. 
-
+All the final results will be saved under the date_time_core_results directory under the output folder. There will be three directories with different types of results.
 
 ```
 
-2018_01_13_13_18_03_core_results
+2018_10_23_17_25_08_core_results
 ├── core_snp_consensus
-│   ├── consensus_allele_variant_positions
-│   ├── consensus_ref_allele_unmapped_variant
-│   ├── consensus_ref_allele_variant_positions
-│   ├── consensus_ref_variant_positions
-│   ├── consensus_variant_positions
-│   └── core_vcf
 ├── data_matrix
-│   ├── Indel_matrix_allele.csv
-│   ├── Indel_matrix_code.csv
-│   ├── SNP_matrix_allele.csv
-│   ├── SNP_matrix_allele_new.csv
-│   ├── SNP_matrix_code.csv
-│   ├── phage_region_positions.txt
-│   ├── repeat_region_positions.txt
-│   ├── mask_positions.txt
-│   ├── Functional_class_filter_positions.txt
-│   ├── bargraph_counts.txt
-│   ├── bargraph_indel_counts.txt
-│   ├── bargraph_indel_percentage.txt
-│   ├── bargraph_percentage.txt
-│   ├── snpEff_results
-│   └── temp
-├── gubbins
-│   ├── 2018_07_11_14_45_01_KPNIH1_allele_var_consensus.fa
-│   ├── 2018_07_11_14_45_01_KPNIH1_ref_allele_unmapped_consensus.fa
-│   ├── 2018_07_11_14_45_01_KPNIH1_ref_allele_var_consensus.fa
-│   ├── 2018_07_11_14_45_01_KPNIH1_ref_var_consensus.fa
-│   └── 2018_07_11_14_45_01_KPNIH1_var_consensus.fa
-└── trees
+└── gubbins
 
 ```
 
-Four directories will be created after the report step where input for different downstream analyses and matrices for QC will be generated.
-
-### 1. core_snp_consensus
-This folder contain different combinations of core/non-core consensus fasta files for individual samples. The core_vcf folder contains annotated vcf files used for generating the consensus. 
-
-### 2. gubbins
-This folder contains different combinations of core/non-core multi-fasta consensus files generated by merging individual consensus files in core_snp_consensus folder. These multi-fasta consensus will be used as input for Gubbins recombination filtering and RAxML/FatTree phylogenetic tree construction.
-Output files from gubbins will be saved in this directory.
-
-**var_consensus.fa:** consensus generated from core variant positions.
-
-**ref_var_consensus.fa** full consensus relative to the reference genome generated from core variant positions (final core variants + reference alleles + non-core variants).
+**core_snp_consensus** The core_vcf folder contains annotated core vcf files used for generating the core SNP consensus. Other folders contains different combination of core/non-core consensus fasta files for individual samples.
 
-Note: non-core variant in this consensus refers to positions where a variant was observed but was filtered out for not meeting one of three criteria. Therefore, it will be replaced by the reference allele observed at this position.
+**gubbins** contains different combinations of core/non-core multi-fasta alignments. Alignments with "gubbins.fa" extension will be used as an input for Gubbins. The pipeline will run RaxML and Iqtree on gubbins recombination filtered consensus fasta and the final output from all the three three programs will be saved under this folder.
 
-- core variant criteria: Since this position was filtered out in another sample, this position will be regarded as a non-core position.
-- hard variant filters: If the variant called at this position didn't meet hard variant filters such as DP (depth), MQ (mapping quality), FQ (all reads supporting one variant), QUAL (variant quality), proximate_snp (the variant was in close proximity to another variant by 10 bp), they would be filtered out from the final core SNPs.
-- functional filters: If the position falls in one of the three functional class (repetitive, masked or phage region), they would be filtered out from final core SNPs.
+- core_var_aln: alignment generated from only core variant positions.
 
-**allele_var_consensus.fa:** consensus generated from all unique variant positions that were called in any sample regardless of it being a core/non-core position. This will contain all core variant positions, non-core variant positions will be substituted with a dash (denoting unmapped) and N’s for not meeting hard variant filters or falling under a functional class filter. Non-core variants that meet all filter parameters will be substituted with the variant allele.
+- genome_aln_w_ref_allele_gubbins: genome alignment relative to the reference genome generated from core variant positions (final core variants + reference alleles).
 
-**ref_allele_var_consensus.fa** full consensus relative to the reference genome generated from all unique variant positions that were called in any sample regardless of it being a core/non-core position. This will contain all core variant positions, non-core variant positions will be substituted with a dash (denoting unmapped) and N’s for not meeting hard variant filters or falling under a functional class filter. Non-core variants that meet all filter parameters will be substituted with the variant allele. Positions that were unmapped in all samples relative to the reference genome will be denoted by the reference allele.
+Note: non-core variants will be replaced by reference allele.
 
-**ref_allele_unmapped_consensus.fa:** full consensus relative to the reference genome generated from all unique variant positions that were called in any sample. This will contain all core variant positions, non-core variant positions will be substituted with a dash (denoting unmapped) and N’s for not meeting hard variant filters or falling under functional class filters. Non-core variants that meet all filter parameters will be substituted with the variant allele. Positions that were unmapped in all samples relative to the reference genome will be denoted by dashes.
+- noncore_plus_core_variants_aln: alignment contains only non-core and core variant positions. Unmapped position will be denoted by a dash(-), variant position (uncertain) that didn't meet the hard variant filter threshold or fell under functional class filter will be denoted by "N". Variant position that met all filter threshold will be substituted with the variant allele.
 
+- genome_aln_w_alt_allele_unmapped_gubbins: genome alignment relative to the reference genome with positions that were unmapped in all samples relative to the reference genome will be denoted by dashes. It contains all core variant positions, non-core variant positions will be substituted with a dash (denoting unmapped) and N’s for not meeting hard variant filter thresholds or falling under functional class filters. Non-core variants that met all filter thresholds will be substituted with variant allele. 
 
-### 3. data_matrix
-This folder contains different types of data matrices and reports that can be queried for variant diagnostics/QC plots. 
+**data_matrix** This folder contains different types of data matrices and reports that can be queried for variant diagnostics/QC plots. 
 
-**SNP/Indel Matrix:**
+- matrices/SNP_matrix_allele.csv and Indel_matrix_allele.csv: contain allele information for each unique variant position (rownames) called in each individual sample (columns). Positions that were unmapped and filtered out are replaced by the reference allele.
 
-SNP_matrix_allele.csv and Indel_matrix_allele.csv: contain allele information for each unique variant position (rownames) called in each individual sample (columns). Positions that were unmapped and filtered out are replaced by the reference allele.
+- matrices/SNP_matrix_allele_new.csv will also contain allele information for each unique variant position (rownames) called in each individual sample (columns) except the positions that were unmapped and filtered out will be substituted with dash (-) and N's.
 
-SNP_matrix_allele_new.csv will also contain allele information for each unique variant position (rownames) called in each individual sample (columns) except the positions that were unmapped and filtered out will be substituted with dash (-) and N's.
-
-SNP_matrix_code.csv and Indel_matrix_code.csv: contain one of the five status codes for each unique variant position (rownames) called in individual sample (columns). 
+- matrices/SNP_matrix_code.csv and Indel_matrix_code.csv: contain one of the five status codes for each unique variant position (rownames) called in individual sample (columns). 
 
 The different status codes are: 
 
@@ -325,15 +236,13 @@ The different status codes are:
 | 3 | non-core or true variant but filtered out due to another sample |
 
 
-**Functional class:**
-
-phage_region_positions.txt contains positions identified by Phaster as phage regions.
+- Functional_annotation_results/phage_region_positions.txt contains positions identified by Phaster as phage regions.
 
-repeat_region_positions.txt contains positions identified by nucmer as tandem repeats.
+- Functional_annotation_results/repeat_region_positions.txt contains positions identified by nucmer as tandem repeats.
 
-mask_positions.txt contains positions set by the user to mask from the final core position list.
+- Functional_annotation_results/mask_positions.txt contains positions set by the user to mask from the final core position list.
 
-Functional_class_filter_positions.txt is an aggregated unique list of positions that fall under repeat, mask and phage region (REPEAT_MASK_PHAGE).
+- Functional_annotation_results/Functional_class_filter_positions.txt is an aggregated unique list of positions that fall under repeat, mask and phage region (REPEAT_MASK_PHAGE).
 
 **Bargraph matrices**
 
@@ -375,10 +284,6 @@ Rscript generate_diagnostics_plots.R
 ![alt tag](https://github.com/alipirani88/variant_calling_pipeline/blob/master/img/barplot_DP.png)
 
 
-### 4. trees
-All the scripts used for running RAxML/FastTree and their output are saved in this directory. Multi-fasta consensus files (pre-recombination filtered) residing in the gubbins folder will be used for constructing RAxML/FastTree trees.
-
-
 <!-- 
 barplot.pdf
 
diff --git a/barplot.png b/barplot.png
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diff --git a/config_ali b/config_ali
index 39e946b..934f3f5 100644
--- a/config_ali
+++ b/config_ali
@@ -154,9 +154,31 @@ fq2: 0.025
 mq: 50
 # Filter variants with Variant QUAL less than the below threshold
 qual: 100
+# Filter variants with GATK QualbyDepth QD parameter; filter less than the below threshold. Currently, being used for Indel SNPS only.
+qd: 2.00
+# Filter variants with AF1 less than the below threshold
+af: 0.900
+# Filter Variants that are proximate to each other within this number of range. To turn this off, use 0(zero).
+prox: 0
+
+
+[gatk_haplotypecaller_filters]
+avg_depth: no
+# If AVG_DEPTH is yes, the below DP threshold will be ignored. Instead, LOW_DEPTH and HIGH_DEPTH filter parameter will be used.
+# Filter variants with Depth less than the below threshold
+dp: 9
+# A value of 2 means that regions with less than half of the average coverage of the entire genome will fail
+low_depth: 2
+# A value of 5 means that regions with 5x depth greater than the average coverage will fail
+high_depth: 5
+# FQ not represented in GATK Haplotype caller vcf format. Instead use AF.
+# Filter variants with MQ(Root Mean Square Quality) less than the below threshold
+mq: 50
+# Filter variants with Variant QUAL less than the below threshold
+qual: 2
 # Filter variants with AF1 less than the below threshold
 af: 0.9
-# Filter Variants that are proximate to each other
+# Filter Variants that are proximate to each other within this number of range. To turn this off, use 0(zero).
 prox: 0
 
 [rna_filters]
@@ -274,6 +296,12 @@ Ref_Name: cdiff_630.fasta
 # path to the reference genome fasta file.
 Ref_Path: /nfs/esnitkin/bin_group/variant_calling_bin/reference/CDIFF_630/
 
+[CDIFF_630_ncbi]
+# Name of reference genome fasta file.
+Ref_Name: cdiff_630.fasta
+# path to the reference genome fasta file.
+Ref_Path: /nfs/esnitkin/bin_group/variant_calling_bin/reference/CDIFF_630_ncbi/
+
 [Cdiff_VPI10463]
 # Name of reference genome fasta file.
 Ref_Name: Cdiff_VPI10463.fasta
@@ -974,3 +1002,22 @@ Ref_Path: /nfs/esnitkin/bin_group/variant_calling_bin/reference/MRSA_Newman/
 Ref_Name: AB030.fasta
 # path to the reference genome fasta file.
 Ref_Path: /nfs/esnitkin/bin_group/variant_calling_bin/reference/AB030/
+
+
+[Steno_K279a]
+# Name of reference genome fasta file.
+Ref_Name: Steno_K279a.fasta
+# path to the reference genome fasta file.
+Ref_Path: /nfs/esnitkin/bin_group/variant_calling_bin/reference/Steno_K279a/
+
+[lactobacillus_crispatus_ST1]
+# Name of reference genome fasta file.
+Ref_Name: lactobacillus_crispatus_ST1.fasta
+# path to the reference genome fasta file.
+Ref_Path: /nfs/esnitkin/Project_Crispatus/Sequence_data/Project_mother_daughter/reference_genome/lactobacillus_crispatus_ST1/
+
+[USA500-2395]
+# Name of reference genome fasta file.
+Ref_Name: USA500-2395.fasta
+# path to the reference genome fasta file.
+Ref_Path: /nfs/esnitkin/bin_group/variant_calling_bin/reference/USA500-2395/
diff --git a/config_gatk b/config_gatk
new file mode 100755
index 0000000..6004861
--- /dev/null
+++ b/config_gatk
@@ -0,0 +1,237 @@
+## Set which tools to use in pipeline:
+[pipeline]
+# Options for Aligner:bwa / smalt / bowtie
+aligner: bwa
+# Options for variant_caller: samtoolswithpostalignbam / gatkhaplotypecaller /samtools
+variant_caller: samtools
+
+## Set bin folder path. Please make sure all the executables are placed in bin folder. Also make sure the path for individual tools are correct.
+[bin_path]
+binbase: /nfs/esnitkin/bin_group/variant_calling_bin/
+
+## Set PBS scheduler fields.
+[scheduler]
+# This small resource will be used for smaller jobs
+resources: nodes=1:ppn=4,pmem=4000mb,walltime=250:00:00
+# Large cluster resources in case of large sample size/variant call sets
+large_resources: nodes=1:ppn=12,mem=47gb,walltime=250:00:00
+email: apirani@med.umich.edu
+queue: flux
+flux_account: esnitkin_flux
+notification: ae
+
+## Tools/Module Parameters
+# Set Parameters for individual tools. Set the binbase of each tool: This should be the folder name of respective tools where the executables for each resp. tool resides.
+# Set parameter for Trimmomatic
+[Trimmomatic]
+trimmomatic_bin: /Trimmomatic/
+adaptor_filepath: adapters/TruSeq3-Nextera_PE_combined.fa
+seed_mismatches: 2
+palindrome_clipthreshold: 30
+simple_clipthreshold: 10
+minadapterlength: 8
+#change this to true and see the effect on alignment
+keep_both_reads: true
+window_size: 4
+window_size_quality: 20
+minlength: 40
+headcrop_length: 0
+colon: :
+targetlength: 125
+crop_length: 40
+f_p: forward_paired.fq.gz
+f_up: forward_unpaired.fq.gz
+r_p: reverse_paired.fq.gz
+r_up: reverse_unpaired.fq.gz
+
+[bwa]
+bwa_bin: /bwa-0.7.12/
+cores: 8
+base_cmd: bwa
+algorithm: mem
+index: index
+RG_header: -R
+Mark_splithits: -M
+
+[bowtie]
+bowtie_bin: /bowtie2-2.2.6/
+cores: 8
+build_cmd: bowtie2-build
+align_cmd: bowtie2
+parameters: -k 1 --non-deterministic --end-to-end
+
+[samtools]
+samtools_bin: /samtools-1.2/
+base_cmd: samtools
+#minimum mapping quality
+#change parameter S to -t SP and D to -t DP
+mpileup_parameters: -ug -f
+faiindex: faidx
+#-q30 -B -E -C50
+
+[bcftools]
+bcftools_bin: /bcftools-1.2/
+base_cmd: bcftools
+call_parameters: -vg
+
+[picard]
+picard_bin: /picard-tools-2.5.0/
+base_cmd: picard.jar
+
+[gatk]
+gatk_bin: /GenomeAnalysisTK-3.3-0/
+base_cmd: GenomeAnalysisTK.jar
+haplotype_parameters: -T HaplotypeCaller
+#gatk_filter1_parameter_expression: FQ < 40.00 && MQ > 20 && QUAL > 50 && DP > 15
+#gatk_filter2_parameter_expression: FQ < 0.025 && MQ > 50 && QUAL > 100 && DP > 15
+#Deprecated. gatk_haplotypecaller Integration Pending for SNP calling.
+#gatk_haplotypecaller_specific_filter2_parameter_expression: FQ < 0.025 && MQ > 50 && QUAL > 100 && DP > 15
+
+[vcftools]
+#vcftools_perl_bin: /vcftools_0.1.12b/perl/
+vcftools_bin: /vcftools_0.1.12b/bin/
+tabix_bin: /tabix-0.2.6/
+#vcftools_bin: /vcftools_0.1.12b/bin/
+vcftools_perl_bin: /vcftools_0.1.12b/perl/
+
+[qualimap]
+qualimap_bin: /qualimap_v2.1/
+base_cmd: qualimap
+
+[bedtools]
+bedtools_bin: /bedtools2-master/bin/
+base_cmd: bedtools
+version_for_coverage: /version_for_coverage/
+
+[bioawk]
+bioawk_bin: /bioawk-master/
+base_cmd: bioawk
+
+[fasttree]
+fasttree_bin: /Fasttree_2.1.10/
+#For Multithread fasttree; use FastTreeMP executable file
+base_cmd: FastTree
+
+[raxml]
+raxml_bin: /raxml/
+openmpi_bin: /openmpi/bin/
+# Other raxml executable available to use: raxmlHPC-PTHREADS,raxmlHPC-PTHREADS-SSE3,raxmlHPC-SSE3
+base_cmd: raxmlHPC-HYBRID-SSE3
+parameters: -f a -x 12345 -p 12345 -N autoMRE -m GTRCAT -T 20
+
+[iqtree]
+iqtree_bin: /nfs/esnitkin/bin_group/anaconda3/bin/
+base_cmd: iqtree
+parameters: -nt AUTO -bb 1000 -m GTR+G+ASC
+
+[gubbins]
+# Change this path to wherever gubbins is located/installed. Right now, installed using conda from anaconda3 package installed in bin_group.
+gubbins_bin: /nfs/esnitkin/bin_group/anaconda3/bin/
+base_cmd: run_gubbins.py
+
+[mummer]
+mummer_bin: /MUMmer3.23/
+nucmer_base_cmd: nucmer
+min_tandem_repeat_length: 20
+percent_id: 95
+
+## Variant Filters
+# Select which type of filters to use. Default is snitkin_filters. See Below snitkin_filters for more information about each filter criterias.
+[SNP_filters]
+filter_criteria: snitkin_filters
+# Other types of filters: SPANDx_filters, loose_filters, snitkin_filters, contamination_filters
+
+## Filters used for most of the Microbial Variant calling Projects.
+[snitkin_filters]
+avg_depth: no
+# If AVG_DEPTH is yes, the below DP threshold will be ignored. Instead, LOW_DEPTH and HIGH_DEPTH filter parameter will be used.
+# Filter variants with Depth less than the below threshold
+dp: 9
+# A value of 2 means that regions with less than half of the average coverage of the entire genome will fail
+low_depth: 2
+# A value of 5 means that regions with 5x depth greater than the average coverage will fail
+high_depth: 5
+# Filter variants with FQ(Consensus Quality) greater than the below threshold
+fq: 0.025
+fq2: 0.025
+# Filter variants with MQ(Root Mean Square Quality) less than the below threshold
+mq: 50
+# Filter variants with Variant QUAL less than the below threshold
+qual: 100
+# Filter variants with GATK QualbyDepth QD parameter; filter less than the below threshold. Currently, being used for Indel SNPS only.
+qd: 2.00
+# Filter variants with AF1 less than the below threshold
+af: 0.900
+# Filter Variants that are proximate to each other within this number of range. To turn this off, use 0(zero).
+prox: 0
+
+[contamination_filters]
+avg_depth: no
+# If AVG_DEPTH is yes, the below DP threshold will be ignored. Instead, LOW_DEPTH and HIGH_DEPTH filter parameter will be used.
+# Filter variants with Depth less than the below threshold
+dp: 3
+# A value of 2 means that regions with less than half of the average coverage of the entire genome will fail
+low_depth: 2
+# A value of 5 means that regions with 5x depth greater than the average coverage will fail
+high_depth: 5
+# Filter variants with FQ(Consensus Quality) greater than the below threshold
+fq: -20.00
+fq2: -20.00
+# Filter variants with MQ(Root Mean Square Quality) less than the below threshold
+mq: 50
+# Filter variants with Variant QUAL less than the below threshold
+qual: 0
+# Filter variants with AF1 less than the below threshold
+af: 1
+# Filter Variants that are proximate to each other
+prox: 1
+
+## Functional class filters. Find Phage and repetitive regions in reference genome, Mask regions of reference genome and dont call variants against it, Mask features with mobile element annotations in it. 
+[functional_filters]
+# If apply_functional_filters is set to no, the pipeline will not find Phage/Repeat regions and Mask positions provided in mask_file.
+apply_functional_filters: yes
+find_phage_region: yes
+find_repetitive_region: yes
+mask_region: no
+mask_file: /nfs/esnitkin/bin_group/variant_calling_bin/reference/test_file.txt
+mobile_elements: yes
+# If apply_to_calls is set to no, PHAGE/REPEAT/MASK will still run but will not remove calls falling in this region.
+apply_to_calls: yes
+
+##SNP annotations
+[snpeff]
+snpeff_bin: /snpEff/
+base_cmd: snpEff.jar
+snpeff_parameters: -d -no-downstream -no-upstream
+prebuild: no
+db: Staphylococcus_aureus_subsp_aureus_usa300_tch1516
+dataDir: /data/
+
+#CLUSTER_SNP: 3
+#CLUSTER_WINDOW_SNP: 10
+#MLEAF_SNP: 0.95
+#QD_SNP: 10.0
+#FS_SNP: 10.0
+#HAPLO_SNP: 20.0
+
+########################################################################################################################
+
+# Reference Genome to be used for pipeline
+# Set path for already indexed reference genome
+
+# Most Frequently used reference genomes
+
+# Name of the reference genome. Provide this value with -index argument.
+[KPNIH1]
+# Name of reference genome fasta file.
+Ref_Name: KPNIH1.fasta
+# path to the reference genome fasta file.
+Ref_Path: /nfs/esnitkin/bin_group/variant_calling_bin/reference/KPNIH1_test/
+
+
+[CFT073]
+# Name of reference genome fasta file.
+Ref_Name: EscherichiacoliCFT073.fna
+# path to the reference genome fasta file.
+Ref_Path: /nfs/esnitkin/bin_group/variant_calling_bin/reference/CFT073
+
diff --git a/modules/beast/SCOTTI_generate_xml.py b/modules/beast/SCOTTI_generate_xml.py
new file mode 100644
index 0000000..49dfc07
--- /dev/null
+++ b/modules/beast/SCOTTI_generate_xml.py
@@ -0,0 +1,412 @@
+import sys
+import os
+import math
+import argparse
+#import random
+#import re
+#import os.path
+#import math
+
+#does it represent an integer?
+def IsInt(s):
+    try: 
+        int(s)
+        return True
+    except ValueError:
+        return False
+
+#does it represent an integer?
+def IsFloat(s):
+    try: 
+        float(s)
+        return True
+    except ValueError:
+        return False
+
+#make tree string into format for mugration
+def removeStates(treeString):
+	l=len(treeString)
+	newString=""
+	i=0
+	while i < l :
+		if treeString[i]=="[":
+			while treeString[i]!="]":
+				i+=1
+			i+=1
+		newString+=treeString[i]
+		i+=1
+	return newString		
+
+#scale branch lengths 
+def scaleTreeString(treeString,scale):
+	l=len(treeString)
+	newString=""
+	i=0
+	while i < l :
+		if treeString[i]==":":
+			newString+=":"
+			numb=""
+			i+=1
+			while treeString[i]!="," and treeString[i]!=")" and treeString[i]!=";":
+				numb+=treeString[i]
+				i+=1
+			newString+=str(float(numb)*scale)
+		newString+=treeString[i]
+		i+=1
+	return newString
+
+
+parser = argparse.ArgumentParser()
+#parser.add_argument('--template',"-t", help='the file SCOTTI_template.xml with path. It will be used as template to generate the xml file.',default=os.path.dirname(os.path.abspath(__file__))+"SCOTTI_template.xml")
+parser.add_argument('--fasta',"-f", help='Input fasta alignment. If not specified, looks in th working directory for SCOTTI_aligment.fas',default=os.getcwd()+"SCOTTI_alignment.fas")
+parser.add_argument('--output',"-o", help='output file that will contain the newly created SCOTTI xml to be run with BEAST2.',default=os.getcwd()+"SCOTTI_analysis")
+parser.add_argument('--overwrite',"-ov", action='store_true', help="Overwrite output files.")#, dest='overW'
+parser.set_defaults(overwrite=False)
+parser.add_argument('--dates',"-d", help='Input csv file with sampling dates associated to each sample name (same name as fasta file). If not specified, looks for SCOTTI_dates.csv',default=os.getcwd()+"SCOTTI_dates.csv")
+parser.add_argument('--hosts',"-ho", help='Input csv file with sampling hosts associated to each sample name (same name as fasta file). If not specified, looks for SCOTTI_hosts.csv',default=os.getcwd()+"SCOTTI_hosts.csv")
+parser.add_argument('--hostTimes',"-ht", help='Input csv file with the earliest times in which hosts are infectable, and latest time when hosts are infective. Use same host names as those used for the sampling hosts file. If not specified, looks for SCOTTI_hostTimes.csv',default=os.getcwd()+"SCOTTI_hostTimes.csv")
+parser.add_argument('--maxHosts',"-m", help='Maximum number of hosts (between sampled, non-sampled, and non-observed) allowed.', type=int, default=-1)
+parser.add_argument('--unlimLife',"-u", action='store_true', help="non-sampled, non-observed hosts have unlimited life span (unlimited contribution in time to the outbreak). By default non-sampled non-observed hosts have limited duration. Usually asymptomaic patients have limited contributions, while to model environmental contamination it's better to use unlimited hosts. Unlimited life span can also decrease computational demand.")#, dest='limL', action='store_true'
+parser.set_defaults(unlimLife=False)
+parser.add_argument('--penalizeMigration',"-p", action='store_true', help="penalize lineages that are still in a deme after deme closure. By default, don't (original version).")#, dest='limL', action='store_true'
+parser.set_defaults(penalizeMigration=False)
+parser.add_argument('--numIter',"-n", help='Number of iterations of the MCMC. Default=100,000,000.', type=int, default=100000000)
+parser.add_argument('--mutationModel',"-mu", help='String represnting the mutation model to be used. Possibilities are \"HKY\" or \"JC\". Further alternaties can be specified by modifying the output xml manually.', default="HKY")
+parser.add_argument('--fixedAs', "-fA", type=int, help = 'Number of sites fixed for A in the genome and not included in the alignment.',default=0)
+parser.add_argument('--fixedCs', "-fC", type=int, help = 'Number of sites fixed for C in the genome and not included in the alignment.',default=0)
+parser.add_argument('--fixedGs', "-fG", type=int, help = 'Number of sites fixed for G in the genome and not included in the alignment.',default=0)
+parser.add_argument('--fixedTs', "-fT", type=int, help = 'Number of sites fixed for T in the genome and not included in the alignment.',default=0)
+
+args = parser.parse_args()
+
+
+#if !(os.path.isfile(args.template)):
+#    print "Error: template file SCOTTI_template.xml not found, please specify it correctly in input using the option -t .\n"
+#    exit()
+if (not (os.path.isfile(args.fasta))):
+    print "Error: input fasta file not found, please specify it correctly in input using the option -f .\n"
+    exit()
+if (os.path.isfile(args.output + ".xml")) and (not args.overwrite):
+    print "Output xml file already exists, to allow overwriting of output file use option -ov, otherwise specify a different output file.\n"
+    exit()
+if not (os.path.isfile(args.dates)):
+    print "Error: input dates csv file not found, please specify it correctly in input using the option -d .\n"
+    exit()
+if not (os.path.isfile(args.hosts)):
+    print "Error: input hosts csv file not found, please specify it correctly in input using the option -s .\n"
+    exit()
+if not (os.path.isfile(args.hostTimes)):
+    print "Error: input hosts csv file not found, please specify it correctly in input using the option -i .\n"
+    exit()
+if (args.numIter<=0):
+    print "Error: the number of MCMC iterations specified is negative or null.\n"
+    exit()
+elif (args.numIter<=1000):
+    print "Very small number of MCMC iterations, might want to specifiy something bigger.\n"
+if (args.mutationModel!="HKY" and args.mutationModel!="JC"):
+    print "Mutation model not supported. Please specify something among \"HKY\" or \"JC\".\n"
+    exit()
+if (args.fixedAs<0 or args.fixedCs<0 or args.fixedGs<0 or args.fixedTs<0):
+    print "Error: the number of fixed sites for each base type must be positive.\n"
+    exit()
+
+#read fasta file
+sqFile=open(args.fasta)
+seqs={}
+line=sqFile.readline()
+linesplit=line.split()
+while line!="" and (len(linesplit)<1 or linesplit[0][0]!=">"):
+    line=sqFile.readline()
+    linesplit=line.split()
+if line=="":
+    print "Wrong format in fasta file\n"
+    exit()
+while line!="":
+    if len(linesplit[0])>1:
+        name=linesplit[0].replace(">","")
+    else:
+        name=linesplit[1]
+    seq=""
+    line=sqFile.readline()
+    linesplit=line.split()
+    while line!="" and (len(linesplit)>0  and linesplit[0][0]!=">") and line!="\n":
+        seq+=linesplit[0]
+        line=sqFile.readline()
+        linesplit=line.split()
+    if name in seqs.keys():
+        print "Error, multiple entries with same name "+name+" in fasta file. Only first part of the name line is used for the name."
+        exit()
+    seqs[name]=seq
+    while line!="" and  (len(linesplit)<1 or linesplit[0][0]!=">"):
+        line=sqFile.readline()
+        linesplit=line.split()
+sqFile.close()
+
+#read in file with host-sample information
+hFile=open(args.hosts)
+hosts={}
+line=hFile.readline()
+linesplit=("".join(line.split())).split(",")  
+while line!="" and len(linesplit)>1: 
+    sam=linesplit[0]
+    hos=linesplit[1]
+    if not (sam in seqs.keys()):
+        print "Error: sample "+sam+" found in sample-host file but not found in the fasta file."
+        exit()
+    if sam in hosts.keys():
+        print "Error, multiple entries with same name "+sam+" in hosts file. "
+        exit()
+    hosts[sam]=hos
+    line=hFile.readline()
+    linesplit=("".join(line.split())).split(",")
+    while line=="\n":
+        line=hFile.readline()
+        linesplit=("".join(line.split())).split(",")
+hFile.close()
+
+#read in sampling dates
+dFile=open(args.dates)
+dates={}
+line=dFile.readline()
+linesplit=("".join(line.split())).split(",")  
+while line!="" and len(linesplit)>1:  
+    sam=linesplit[0]
+    dat=linesplit[1]
+    if not (IsFloat(dat)):
+        print "Error: date "+str(dat)+" for sample "+sam+" cannot be converted to a number"
+        exit()
+    if not (sam in seqs.keys()):
+        print "Error: sample "+sam+" found in sampling dates file but not found in the fasta file."
+        exit()
+    if sam in dates.keys():
+        print "Error, multiple entries with same name "+sam+" in dates file. "
+        exit()
+    dates[sam]=float(dat)
+    line=dFile.readline()
+    linesplit=("".join(line.split())).split(",")
+    while line=="\n":
+        line=dFile.readline()
+        linesplit=("".join(line.split())).split(",")
+dFile.close()
+    
+#read in file with host times information
+hFile=open(args.hostTimes)
+hostT={}
+line=hFile.readline()
+linesplit=("".join(line.split())).split(",")  
+while line!="" and len(linesplit)>2: 
+    hos=linesplit[0]
+    dat=float(linesplit[1])
+    dat2=float(linesplit[2])
+    if (not (IsFloat(dat))) or (not (IsFloat(dat2))):
+        print "Error: date "+str(dat)+" or "+str(dat2)+" for sample "+sam+" cannot be converted to a number"
+        exit()
+    #if not (hos in seqs.keys()):
+    #    print "Error: sample "+hos+" found in host times file but not found in the fasta file."
+    #    exit()
+    if hos in hostT.keys():
+        print "Error, multiple entries with same name "+hos+" in hosts-times file. "
+        exit()
+    hostT[hos]=[dat, dat2]
+    line=hFile.readline()
+    linesplit=("".join(line.split())).split(",")
+    while line=="\n":
+        line=hFile.readline()
+        linesplit=("".join(line.split())).split(",")
+hFile.close()
+
+
+    
+#check that the details are correct
+for s in seqs.keys():
+    if not (s in dates.keys()):
+        print "Error, no date specified for sample "+s
+        exit()
+    if not (s in hosts.keys()):
+        print "Error, no host specified for sample "+s
+        exit()
+for s in hosts.keys():
+    if not (s in seqs.keys()):
+        print "Error, no sequence specified for sample "+s
+        exit()
+    if not (s in dates.keys()):
+        print "Error, no date specified for sample "+s
+        exit()
+for s in dates.keys():
+    if not (s in seqs.keys()):
+        print "Error, no sequence specified for sample "+s
+        exit()
+    if not (s in hosts.keys()):
+        print "Error, no host specified for sample "+s
+        exit()
+for s in hosts.keys():
+    if not (hosts[s] in hostT.keys()):
+        print "Warning: no dates specified for host "+hosts[s]+". I will assume this host is available throghtout"
+        hostT[hosts[s]]=[-1000000000000,1000000000000]
+
+
+for h in hostT.keys():
+    found=0
+    for s in hosts.keys():
+        if hosts[s]==h:
+            found=1
+    if found==0:
+        print "Warning: no sample specified for host "+h+". I will assume this host could have been infected or not. If you are sure this host was infected, then please include a non-informative sample (all \"N\"s) from it."
+
+    
+    
+    
+    
+xml=open(args.output+".xml","w")
+xml.write("<beast version=\'2.0\' namespace=\'beast.evolution.alignment:beast.core:beast.core.parameter:beast.evolution.tree:beast.evolution.tree.coalescent:beast.core.util:beast.evolution.operators:beast.evolution.sitemodel:beast.evolution.substitutionmodel:beast.evolution.likelihood:beast.evolution.tree:beast.math.distributions:multitypetreeVolz.distributions:multitypetreeVolz.operators:multitypetreeVolz.util\'>\n\n"+"  <data id=\"alignmentVar\" dataType=\"nucleotide\">\n")
+for s in seqs.keys():
+    xml.write("    <sequence taxon=\'"+s+"\' value=\'"+seqs[s]+"\'/>\n")
+xml.write("  </data>\n")
+st=""
+if args.fixedAs>0 and args.fixedCs>0 and args.fixedGs>0 and args.fixedTs>0:
+    st="constantSiteWeights=\'"+str(args.fixedAs)+" "+str(args.fixedCs)+" "+str(args.fixedGs)+" "+str(args.fixedTs)+"\'"
+xml.write("  <data id=\'alignment\' spec=\'FilteredAlignment\' filter=\'-\' data=\'@alignmentVar\' "+st+"/>\n\n")
+
+#hosts
+xml.write("   <typeTraitSet spec=\'TraitSetWithLimits\' id=\'typeTraitSet\' traitname=\"type\" value=\"")
+hL=hosts.keys()
+for s in hL:
+    xml.write(s+"="+hosts[s])
+    if s!=hL[len(hL)-1]:
+        xml.write(",")
+xml.write("\">\n     <taxa spec=\'TaxonSet\' alignment=\'@alignment\'/>\n   </typeTraitSet>\n\n")
+
+#dates
+xml.write("   <timeTraitSet spec=\'TraitSetWithLimits\' id=\'timeTraitSet\' traitname=\"date-forward\" value=\"")
+dL=dates.keys()
+for s in range(len(dL)):
+    xml.write(dL[s]+"="+str(dates[dL[s]]))
+    if (s!=(len(dL)-1)):
+        xml.write(",")
+xml.write("\">\n     <taxa spec=\'TaxonSet\' alignment=\'@alignment\'/>\n   </timeTraitSet>\n\n")
+
+#end infectability
+xml.write("   <endInfectionsTraitSet spec=\'InfectionTraitSet\' id=\'endInfectionsTraitSet\' type=\'end\' traitname=\"date-forward\" value=\"")
+dL=hostT.keys()
+for s in dL:
+    xml.write(s+"="+str(hostT[s][1]))
+    if s!=dL[len(dL)-1]:
+        xml.write(",")
+xml.write("\">\n 	  <taxaDates idref=\'timeTraitSet\'/>\n 	  <taxaTypes idref=\'typeTraitSet\'/>\n   </endInfectionsTraitSet>\n\n")
+
+#start infectability
+xml.write("   <startInfectionsTraitSet spec=\'InfectionTraitSet\' id=\'startInfectionsTraitSet\' type=\'start\' traitname=\"date-forward\" value=\"")
+dL=hostT.keys()
+for s in dL:
+    xml.write(s+"="+str(hostT[s][0]))
+    if s!=dL[len(dL)-1]:
+        xml.write(",")
+xml.write("\">\n 	  <taxaDates idref=\'timeTraitSet\'/>\n 	  <taxaTypes idref=\'typeTraitSet\'/>\n   </startInfectionsTraitSet>\n\n")
+
+#check that times fit
+for s in seqs.keys():
+    h=hosts[s]
+    if dates[s]<=hostT[hosts[s]][0]:
+        print "Error, sampling time "+str(dates[s])+" of sample "+s+" must be after start of infectability time "+str(hostT[hosts[s]][0])+" of host "+h
+        exit()
+    if dates[s]>=hostT[hosts[s]][1]:
+        print "Error, sampling time "+str(dates[s])+" of sample "+s+" must be before end of infectiousness time "+str(hostT[hosts[s]][0])+" of host "+h
+        exit()
+
+#include mutation model
+xml.write("   <siteModel spec=\"SiteModel\" id=\"siteModel\">\n     <mutationRate spec=\'RealParameter\' id=\"mutationRate\" value=\"1.0\"/>\n     <substModel spec=\"")
+if args.mutationModel=="HKY":
+   xml.write("HKY\">\n       <kappa spec=\'RealParameter\' id=\"hky.kappa\" value=\"1.0\"/>\n")
+elif args.mutationModel=="JC":
+   xml.write("jc69\">\n")
+elif args.mutationModel=="GTR":
+   xml.write("GTR\" rateAC=\"@rateAC\" rateAG=\"@rateAG\" rateAT=\"@rateAT\" rateCG=\"@rateCG\" rateGT=\"@rateGT\" >\n       <parameter estimate=\'false\' name=\"rateCT\" lower=\"0.0\" value=\"1.0\" id=\"rateCTfixed\"/>\n")
+else:
+    print "Error, mutation model "+args.mutationModel+" not recognized."
+    exit()
+xml.write("       <frequencies estimate=\"false\" spec=\'Frequencies\'>\n	 <frequencies spec=\'RealParameter\' id=\""+args.mutationModel+".freq\" value=\"0.25 0.25 0.25 0.25\"/>\n       </frequencies>\n     </substModel>\n   </siteModel>\n\n")
+              
+#migration model
+nHos=len(hostT.keys())
+meanL=0.0
+for h in hostT.keys():
+    meanL+=hostT[h][1]-hostT[h][0]
+meanL=meanL/len(hostT.keys())
+ma=args.maxHosts
+if ma<nHos:
+    ma=nHos+5
+    print "Maximum number of hosts specified is less than the minimum number of hosts. Setting it to "+str(ma)
+if ma<(nHos+2):
+    print "Warning: the maximum number of hosts is less than "+str(nHos+2)+". If there are problems in the execution of the BEAST2 analysis, try a higher values of maxHosts."
+start=(nHos+ma)/2
+if start<(nHos+2) and ma>=(nHos+2):
+    start=nHos+2
+xml.write("   <migrationModelUniform spec=\'MigrationModelUniform\' id=\'migModel\' minDemes=\'"+str(nHos)+"\'>\n     <rate spec=\'RealParameter\' value=\""+str(1.0/(meanL*nHos))+"\" dimension=\"1\" id=\"rate\"/>\n     <popSize spec=\'RealParameter\' value=\"1.0\" dimension=\"1\" id=\"popSize\"/>\n     <numDemes spec=\'IntegerParameter\' value=\""+str(start)+"\" dimension=\"1\" id=\"numDemes\" lower=\'"+str(nHos)+"\' upper=\'"+str(ma)+"\'/>\n     <trait idref=\'startInfectionsTraitSet\' type=\'start\'/>\n     <trait idref=\'endInfectionsTraitSet\' type=\'end\'/>\n   </migrationModelUniform>\n\n")            
+
+#priors
+xml.write("   <input spec=\'CompoundDistribution\' id=\'parameterPriors\'>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@mutationRate\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"-3.0\" S=\"6.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@popSize\">\n       <distr spec=\"LogNormalDistributionModel\"  M=\"0.0\" S=\"6.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rate\">\n       <distr spec=\'LogNormalDistributionModel\' M=\""+str(math.log(1.0/(meanL*nHos)))+"\" S=\"0.2\"/>\n     </distribution>\n")
+if args.mutationModel=="HKY":
+    xml.write("     <distribution spec=\'beast.math.distributions.Prior\' x=\"@hky.kappa\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n   </input>\n\n")
+elif args.mutationModel=="JC":
+   xml.write("   </input>\n\n")
+elif args.mutationModel=="GTR":
+    xml.write("     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateAC\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateAG\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateAT\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateCG\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateGT\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n   </input>\n\n")      
+              
+#Likelihoods
+xml.write("<input spec=\'TreeLikelihood\' id=\"treeLikelihood\">\n     <data idref=\"alignment\"/>\n     <tree idref=\"tree\"/>\n     <siteModel idref=\'siteModel\'/>\n   </input>\n\n   <input spec=\'StructuredCoalescentTreeDensityNew\' id=\'treePrior\' limitedLifespan=\'"+str(args.unlimLife)+"\' penalizeMigration=\'"+str(args.penalizeMigration)+"\'>\n     <multiTypeTreeConcise idref=\"tree\"/>\n     <migrationModelUniform idref=\"migModel\"/>\n   </input>\n\n   <run spec=\"MCMC\" id=\"mcmc\" chainLength=\""+str(args.numIter)+"\" storeEvery=\"10000\">\n\n")
+
+#migration model
+xml.write("     <init spec=\'StructuredCoalescentMultiTypeTreeConcise\' id=\'tree\' nTypes=\""+str(start)+"\">\n         <migrationModelUniform spec=\'MigrationModelUniform\' minDemes=\'"+str(nHos)+"\'>\n             <rate spec=\'RealParameter\' value=\""+str(1.0/(meanL*nHos))+"\" dimension=\"1\"/>\n             <popSize spec=\'RealParameter\' value=\"1.0\" dimension=\"1\"/>\n             <numDemes spec=\'IntegerParameter\' value=\""+str(start)+"\" dimension=\"1\"/>\n         </migrationModelUniform>\n         <trait idref=\'typeTraitSet\'/>\n         <trait idref=\'timeTraitSet\'/>\n     </init>\n\n")              
+              
+#state nodes              
+xml.write("     <state>\n       <stateNode idref=\"tree\"/>\n       <stateNode idref=\"rate\"/>\n       <stateNode idref=\"popSize\"/>\n       <stateNode idref=\"numDemes\"/>\n       <stateNode idref=\"mutationRate\"/>\n")
+if args.mutationModel=="HKY":
+    xml.write("       <stateNode idref=\"hky.kappa\"/>\n")
+elif args.mutationModel=="GTR":              
+    xml.write("       <stateNode idref=\"rateAC\"/>\n       <stateNode idref=\"rateAG\"/>\n       <stateNode idref=\"rateAT\"/>\n       <stateNode idref=\"rateCG\"/>\n       <stateNode idref=\"rateGT\"/>\n")
+xml.write("       <stateNode idref=\""+args.mutationModel+".freq\"/>\n     </state>\n\n")     
+              
+#compound distribution
+xml.write("     <distribution spec=\'CompoundDistribution\' id=\'posterior\'>\n       <distribution idref=\"treeLikelihood\"/>\n       <distribution idref=\'treePrior\'/>\n       <distribution idref=\"parameterPriors\"/>\n     </distribution>\n\n")
+              
+#operators
+xml.write("     <operator spec=\'ScaleOperator\' id=\'RateScaler\' parameter=\"@rate\" scaleFactor=\"0.8\" weight=\"1\"/>\n     <operator spec=\"ScaleOperator\" id=\"PopSizeScaler\" parameter=\"@popSize\" scaleFactor=\"0.8\" weight=\"1\"/>\n     <operator spec=\"ShortRangeUniformOperator\" id=\"NumDemesScaler\" parameter=\"@numDemes\" weight=\"1\"/>\n     <operator spec=\"ScaleOperator\" id=\"muRateScaler\" parameter=\"@mutationRate\" scaleFactor=\"0.8\" weight=\"1\"/>\n     <operator spec=\"DeltaExchangeOperator\" id=\"freqExchanger\" parameter=\"@"+args.mutationModel+".freq\" delta=\"0.01\" weight=\"0.1\"/>\n     <operator id=\'treeScaler.t\' spec=\'ScaleOperator\' scaleFactor=\"0.5\" weight=\"3\" tree=\"@tree\"/>\n     <operator id=\'treeRootScaler.t\' spec=\'ScaleOperator\' scaleFactor=\"0.5\" weight=\"3\" tree=\"@tree\" rootOnly=\'true\'/>\n     <operator id=\'UniformOperator.t\' spec=\'Uniform\' weight=\"30\" tree=\"@tree\"/>\n     <operator id=\'SubtreeSlide.t\' spec=\'SubtreeSlide\' weight=\"15\" gaussian=\"true\" size=\"1.0\" tree=\"@tree\"/>\n     <operator id=\'narrow.t\' spec=\'Exchange\' isNarrow=\'true\' weight=\"15\" tree=\"@tree\"/>\n     <operator id=\'wide.t\' spec=\'Exchange\' isNarrow=\'false\' weight=\"3\" tree=\"@tree\"/>\n     <operator id=\'WilsonBalding.t\' spec=\'WilsonBalding\' weight=\"3\" tree=\"@tree\"/>\n")
+if args.mutationModel=="HKY":
+    xml.write("     <operator spec=\'ScaleOperator\' id=\'kappaScaler\' parameter=\"@hky.kappa\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n")
+elif args.mutationModel=="GTR": 
+    xml.write("     <operator spec=\'ScaleOperator\' id=\'ACScaler\' parameter=\"@rateAC\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'AGScaler\' parameter=\"@rateAG\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'ATScaler\' parameter=\"@rateAT\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'CGScaler\' parameter=\"@rateCG\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'GTScaler\' parameter=\"@rateGT\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n")
+xml.write("\n")
+
+#loggers
+xml.write("     <logger logEvery=\""+str(args.numIter/10000)+"\" fileName=\""+args.output+".log\">\n       <model idref=\'posterior\'/>\n       <log idref=\"posterior\"/>       <log idref=\"treeLikelihood\"/>\n       <log idref=\"migModel\"/>\n       <log idref=\"mutationRate\"/>\n       <log spec=\'TreeHeightLogger\' tree=\'@tree\'/>\n       <log idref=\""+args.mutationModel+".freq\"/>\n")
+if args.mutationModel=="HKY":
+    xml.write("       <log idref=\"hky.kappa\"/>\n")
+elif args.mutationModel=="GTR":               
+    xml.write("       <log idref=\"rateAC\"/>\n       <log idref=\"rateAG\"/>\n       <log idref=\"rateAT\"/>\n       <log idref=\"rateCG\"/>\n       <log idref=\"rateGT\"/>\n")
+xml.write("     </logger>\n")
+     
+xml.write("     <logger logEvery=\""+str(args.numIter/5000)+"\" fileName=\""+args.output+".trees\" mode=\"tree\">\n       <log idref=\'treePrior\'/>\n     </logger>\n")
+
+xml.write("     <logger logEvery=\""+str(args.numIter/1000)+"\">\n       <model idref=\'posterior\'/>\n       <log idref=\"posterior\"/>\n       <log idref=\"treeLikelihood\"/>\n       <log spec=\'TreeHeightLogger\' tree=\'@tree\'/>\n       <log idref=\"migModel\"/>\n       <log idref=\"mutationRate\"/>\n       <ESS spec=\'ESS\' name=\'log\' arg=\"@treePrior\"/>\n       <ESS spec=\'ESS\' name=\'log\' arg=\"@posterior\"/>\n     </logger>\n\n   </run>\n </beast>\n\n")
+
+
+
+
+            
+      
+              
+              
+              
+              
+              
+              
+                  
+      
+			
+exit()
+
+
+
+
+
+		
\ No newline at end of file
diff --git a/modules/beast/__pycache__/fasta_functions.cpython-35.pyc b/modules/beast/__pycache__/fasta_functions.cpython-35.pyc
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diff --git a/modules/beast/__pycache__/scotti_functions.cpython-35.pyc b/modules/beast/__pycache__/scotti_functions.cpython-35.pyc
new file mode 100644
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GIT binary patch
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diff --git a/modules/beast/add_invariant_sites_beast.sh b/modules/beast/add_invariant_sites_beast.sh
new file mode 100755
index 0000000..b65bff4
--- /dev/null
+++ b/modules/beast/add_invariant_sites_beast.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+# input: 
+# (1) beast xml file (ex. beauti output) 
+# (2) number of invariant A's, C's, G's, and T's (in that order)
+# ex. bash add_invariant_sites_beast.sh beast.xml 100 120 130 140
+
+fout=$(echo $(echo $1 | cut -d. -f1)_invSites.xml)
+dataf=$(awk 'NR==1,/data id=\"/' $1 | tail -n1 | cut -d'"' -f2)
+awk 'NR==1,/data id=\"/{sub(/data id=\"/, "data id=\"orig_")} 1' $1 > $fout
+
+sed -ibak "1,/data>/s/data>/&\n\n<data id='${dataf}' \n spec='FilteredAlignment' \n filter='-' \n data='@orig_${dataf}' \n constantSiteWeights='${2} \n ${3} ${4} ${5}'\/> <!-- A C G T -->/" $fout
+
+rm ${fout}bak
diff --git a/modules/beast/beast.py b/modules/beast/beast.py
new file mode 100644
index 0000000..818a739
--- /dev/null
+++ b/modules/beast/beast.py
@@ -0,0 +1,102 @@
+# PIPELINE TO RUN BEAST ON FLUX
+
+# Import modules
+import sys
+import os
+import argparse
+from subprocess import call
+import re
+from fasta_functions import *
+
+# Get command line arguments
+parser = argparse.ArgumentParser(description='Run BEAST pipeline.')
+parser.add_argument('xmls', nargs='*', help='input BEAST xml file(s)')
+parser.add_argument('--n_reps', '-n', metavar='N', type=int, help='number of beast runs for each xml', default=3)
+parser.add_argument('--invar_sites', '-i', metavar='FILE', help='''
+                    option 1: fasta file to  get number of invariant As, Cs, Gs, and Ts not in alignment
+                    option 2: text file with number of invariant As, Cs, Gs, and Ts in alignment''')
+parser.add_argument('--gubbins_gff', '-g', metavar='GFF', help='''gubbins output GFF file to mask recombinant sites in alignment before counting invariant sites''')
+parser.add_argument('--tree', '-t', help='starting tree file in newick format')
+parser.add_argument('-scripts_dir', '-d', metavar='DIR', help='directory where all of the scripts are housed', default='/nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/beast')
+args = parser.parse_args()
+# Rename variables
+xmls = args.xmls
+n_reps = args.n_reps
+invar_sites = args.invar_sites
+gubbins_gff = args.gubbins_gff
+tree = args.tree
+scripts_dir = args.scripts_dir
+
+# Get invariant site counts
+if invar_sites is not None:
+    if not invar_sites.endswith(tuple(['fa','fasta'])):
+        # Path to file with invariant site counts
+        invar_counts_file = invar_sites
+    else:
+        # Count invariant sites in whole genome alignment
+        invar_counts_file = count_invar_sites(invar_sites, gubbins_gff)
+    with open(invar_counts_file) as f:
+        invar_counts = f.readline().strip()
+
+# Final xmls for beast
+xmls_for_beast = []
+
+# For each xml input file, modify the xml as desired and start beast
+for xml in xmls:
+
+    print('Processing ' + xml + '.')
+
+    # Change prefixes in BEAST xml (more informative when looking at trace files in Tracer)
+    print('Adding xml file name as prefix to sequence data in BEAST xml.')
+    call([scripts_dir + '/change_prefixes_beast_xml.sh', xml])
+
+    # Get newest xml name
+    xml2 = re.split('/|\.', xml)[-2] + '_renamed.xml'
+
+    # Add starting tree, if desired
+    if tree is not None:
+        print('Adding starting tree to BEAST xml.')
+        call([scripts_dir + '/insert_starting_tree_beast.sh', xml2, tree])
+        # Get newest xml name
+        xml3 = re.split('/|\.', xml2)[-2] + '_st.xml'
+    else:
+        xml3 = xml2
+
+    # Get invariant site counts
+    if invar_sites is not None:
+        # Add invariant site counts to xml
+        call([scripts_dir + '/add_invariant_sites_beast.sh', xml3, invar_counts]) #[int(x) for x in invar_counts.split(' ')])
+        # Get newest xml name
+        xml4 = re.split('/|\.', xml3)[-2] + '_invSites.xml'
+    else:
+        xml4 = xml3
+
+    # Final xml file
+    xml_final = xml4
+    print('Final xml for input into beast: ' + xml_final + '.')
+
+    # Delete intermediate xmls
+    to_remove = []
+    for fname in os.listdir('.'):
+        b = re.search('.*_renamed_.*xml', fname)
+        s = fname == xml_final
+        if b and not s:
+            print('Removing intermediate file ' + fname + '.')
+            os.remove(fname)   
+
+    # Add xml file name to list for beast
+    xmls_for_beast.append(xml_final) 
+
+# Generate input file for beast script
+with open('input_beast.txt', 'w') as f:
+    for x in xmls_for_beast:
+        for i in range(n_reps):
+            f.write(x + '\n')
+
+#call([scripts_dir + '/generate_input_file_beast_pbs.sh', ' '.join(xmls_for_beast), n_reps])
+print('input_beast.txt generated.')
+
+# Generate PBS scripts and submit jobs to flux
+print('Generating PBS script(s) and submitting jobs to flux.')
+call(['perl', scripts_dir + '/beast_pbs_flux.pl', 'input_beast.txt'])
+
diff --git a/modules/beast/beast_pbs_flux.pl b/modules/beast/beast_pbs_flux.pl
new file mode 100755
index 0000000..5062933
--- /dev/null
+++ b/modules/beast/beast_pbs_flux.pl
@@ -0,0 +1,100 @@
+#Creates and submits PBS scripts for performing BEAST analysis with desired xmls
+#
+#Takes as input:
+#		1) config_file	- A 1-column file where each line is the file name (minus the .xml extension) for a beast run
+# UPDATED 2017-09-21 from beast2/2.4.5 to beast2/2.4.7
+# UPDATED 2018-11-19 from beast2/2.4.7 to /nfs/esnitkin/bin_group/beast_v2.5.1/beast/bin/beast
+
+#PRINT USAGE
+if(@ARGV == 0)
+{
+
+	print "\n\nperl beast_PBS.pl config_file\n\n";
+
+	exit;
+
+}#end if
+
+
+#READ IN COMMAND LINE ARGUMENTS
+my $config_file = shift @ARGV;
+
+
+#GO THROUGH EACH LINE OF THE CONFIG FILE, CREATING AND SUBMITTING PBS FILES
+open CONFIG, $config_file;
+my $count = 1;
+
+foreach my $job (<CONFIG>)
+{
+
+	#GET CURRENT ARGUMENTS
+	chomp $job;
+	my ($fnameorig) = split /\t/, $job;
+        my ($fname) = split /\./, $fnameorig;
+
+	#CREATE OUTPUT DIRECTORY
+	my $date = `date +%Y-%m-%d-%H-%M-%S`;
+	chomp $date;
+
+	my $wd = `pwd`;
+	chomp $wd;
+	$out_dir = "$wd/$date\_beast$count\_$fname";
+	mkdir $out_dir;
+
+
+	#CREATE AND WRITE PBS FILE
+	my $rand = int(rand(10000000));
+	my $pbs_file = "beast$count\_$fname\_$rand.pbs";
+
+	open PBS, ">$pbs_file";
+
+	print PBS "#!/bin/sh\n";
+	print PBS "####  PBS preamble\n";
+	print PBS "\n";
+	print PBS "#PBS -N beast$count\_$fname\n";
+	print PBS "\n";
+	print PBS "# User info\n";
+	print PBS "#PBS -M zenalapp\@umich.edu\n";
+	print PBS "#PBS -m abe\n";
+	print PBS "\n";
+	print PBS "# Change the number of cores (ppn=1), amount of memory, and walltime:\n";
+	print PBS "#PBS -l nodes=1:ppn=12,pmem=4gb,walltime=10:00:00:00\n";
+	print PBS "#PBS -j oe\n";
+	print PBS "#PBS -V\n";
+	print PBS "\n";
+	print PBS "#PBS -A esnitkin_flux\n";
+	print PBS "#PBS -q flux\n";
+	print PBS "#PBS -l qos=flux\n";
+	print PBS "\n";
+	print PBS "####  End PBS preamble\n";
+	print PBS "\n";
+	print PBS "#  Show list of CPUs you ran on, if you're running under PBS\n";
+	print PBS "if [ -n \"\$PBS_NODEFILE\" ]; then cat \$PBS_NODEFILE; fi\n";
+	print PBS "\n";
+	print PBS "#  Change to the directory you submitted from\n";
+	print PBS "cd $out_dir";
+	print PBS "\n";
+	print PBS "#Load modules\n";
+	#print PBS "module load beast2/2.4.7\n";
+    print PBS "module load beagle\n";
+    #print PBS "addonmanager -add BEAST_CLASSIC\n";
+    #print PBS "addonmanager -add SCOTTI\n";
+	print PBS "\n";
+	print PBS "#  Put your job commands here:\n";
+	print PBS "/nfs/esnitkin/bin_group/beast_v2.5.1/beast/bin/beast -beagle_SSE -instances 12 -threads 12 beast$count\_$fnameorig\n";
+	print PBS "\n";
+
+	close PBS;
+
+
+	#SUBMIT JOB
+	`mv $pbs_file $out_dir`;
+        `cp $fnameorig $out_dir/beast$count\_$fnameorig`;
+        chdir($out_dir);
+        #`/nfs/esnitkin/Zena/lib/change_prefixes_beast_xml.sh beast$count\_$fnameorig`;
+        #`mv beast$count\_$fname\_renamed.xml beast$count\_$fnameorig`;
+	`qsub $pbs_file`;
+        chdir($wd);
+	$count ++;
+
+}#end foreach
diff --git a/modules/beast/change_prefixes_beast_xml.sh b/modules/beast/change_prefixes_beast_xml.sh
new file mode 100755
index 0000000..837301d
--- /dev/null
+++ b/modules/beast/change_prefixes_beast_xml.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+
+## Changes prefixes in beast xml file
+## Specifically, adds prefixes to the sequence id name and discrete trait name based on xml file name (the prefix is the xml file name)
+## Takes as input: beast xml file
+## Usage: change_prefixes_beast.sh beast.xml 
+## Note: in example above, the prefix would be 'beast'
+
+ids=$(grep -A1 '<data' $1 | grep 'id=' | grep -o -P '(?<=").*(?=")')
+len_ids=$(echo $ids | wc -w)
+seqs=$(echo $ids | cut -d' ' -f1)
+if [ $len_ids == 2 ]; then 
+  discrete=$(echo $ids | cut -d' ' -f2)
+fi
+
+prefix=$(echo $1 | grep -o -P '.*(?=\.xml)')
+
+sed "s/${seqs}/${prefix}_${seqs}/g" $1 > ${prefix}_renamed.xml
+if [ $len_ids == 2 ]; then
+  sed -ibak "s/${discrete}/${prefix}_${discrete}/g" ${prefix}_renamed.xml
+  rm ${prefix}_renamed.xmlbak
+fi
+sed -ibak "s/EBSP/${prefix}_EBSP/g" ${prefix}_renamed.xml
+rm ${prefix}_renamed.xmlbak
diff --git a/modules/beast/countInvarSites.py b/modules/beast/countInvarSites.py
new file mode 100644
index 0000000..73a5930
--- /dev/null
+++ b/modules/beast/countInvarSites.py
@@ -0,0 +1,56 @@
+# Count invariant sites in alignment file
+
+# Import modules
+from subprocess import call
+from Bio import AlignIO
+from Bio import SeqIO
+from Bio.Seq import Seq
+from Bio.SeqRecord import SeqRecord
+import pandas as pd
+from collections import defaultdict
+import re
+import os
+
+# Set paths
+python = '/nfs/esnitkin/bin_group/anaconda3/bin/python'
+maskVarsPath = '/nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/mask_gubbins_variants.py'
+
+
+# Add gubbins filtered masks to full alignment.
+# Note: the full alignment should be the same one used as input to gubbins.
+# Input:
+#    (1) Whole genome alignment file in FASTA format
+#    (2) Gubbins GFF output file
+#    (3) 
+# Output:
+#    (1) Masked whole genome alignment file in FASTA format
+#    (2) Text file with list of masked positions in each genome
+#    (3) Masked variant-only alignment file in FASTA format
+def mask_positions(fasta, gff):
+
+
+
+# Count invariant sites in an alignment file (fasta format)
+# Input: 
+#    (1) Path to alignment file in fasta format
+#    (2) (optional) Path to [gubbins] GFF file to mask recombinant sites first
+#    (3)
+#    (4) (optional) Path to mask_gubbins_variants.py (default: '/nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/mask_gubbins_variants.py')    
+# Output: 
+#    (1) Text file (*_invar_site_counts.txt) with invariant site counts in the following order: A,C,G,T
+#    (2) If GFF path given, masked fasta file (*_gubbins_masked.fa)
+#    (3) Returns name of text file with invariant site counts (*_invar_site_counts.txt)
+def count_invar_sites(fasta,gff=None,mask_vars_path=mask_vars_path)
+    # Mask recombinant regions before counting invariant sites
+    if gff is not None:
+        call([python, mask_vars_path, fasta, gff])
+        aln_file = re.split('/|\.', fasta)[-2] + \
+                  '_gubbins_masked.fa'
+    else:
+        aln_file = fasta
+    # Count invariant sites in whole genome alignment
+    call([python, scripts_dir + '/get_num_invariant_sites_v2.py', aln_file])
+    invar_counts_file = re.split('/|\.', alnFile)[-2] + \
+                      '_invar_site_counts.txt'
+    return(invar_counts_file)
+   
diff --git a/modules/beast/fasta_functions.py b/modules/beast/fasta_functions.py
new file mode 100755
index 0000000..70c2085
--- /dev/null
+++ b/modules/beast/fasta_functions.py
@@ -0,0 +1,275 @@
+# Functions to manipulate fasta files
+
+# Functions:
+#    subset_fasta
+#    mask_positions
+#    count_invar_sites
+
+# Import modules
+from subprocess import call
+from Bio import AlignIO
+from Bio import SeqIO
+from Bio.Seq import Seq
+from Bio.SeqRecord import SeqRecord
+import pandas as pd
+from collections import defaultdict
+from collections import Counter
+import re
+import os
+
+def subset_fasta(ids, fastain, fastaout):
+    """Creates a new fasta file with a subset of original sequences.
+
+    Args:
+        idnames: List of sequence names to be extracted.
+        fastain: Path to fasta file containing all sequences of interest.
+        fastaout: Name of output fasta file containing only the sequences of interest.
+
+    Output:
+        Fasta file containing only the sequences of interest.
+    """    
+
+    with open(fastaout, 'w') as f:
+        for rec in SeqIO.parse(fastain, 'fasta'):
+            if str(rec.id) in ids:
+                f.write('>' + ids[ids.index(rec.id)] + '\n')
+                f.write(str(rec.seq) + '\n')
+
+
+def get_fasta_subsets(csv, fasta):
+    """Creates new fasta files with subsets of the original sequences.
+
+    Args:
+        csv: csv file where each line contains a different subset of sequence names to be extracted.
+        fasta: Path to fasta file containing all sequences of interest.
+
+    Output:
+        Fasta files containing only the sequences of interest.
+
+    Returns:
+        List of paths to subsetted fasta files.
+    """
+
+    fas = []
+
+    with open(csv) as f:
+        csv_reader = csv.reader(f, delimiter=',')
+        count = 0
+        for subset in csv_reader:
+            count += 1
+            fastaout = fasta.split('.')[0] + '_subset' + str(count) + '.fasta'
+            fa = subset_fasta(subset, fasta, fastaout)
+            fas.append(fa)
+
+    return(fas)
+
+
+def rm_invar_sites(fasta, outfile=None, outfmt=['fasta','vcf'], outdir='.', 
+                   path={'snpsites':'/nfs/esnitkin/bin_group/anaconda3/bin/snp-sites'}):
+    """Removes invariant sites from a multifasta file using snp-sites.
+
+    Args:
+        fasta: Alignment fasta file from which to get variant sites.
+        outfile: (Optional) Output file name.
+        outfmt: Format of output file (fasta or vcf).
+        outdir: (Optional) Output directory (default: current working directory).
+        path: Dictionary of paths including the path to snp-sites (key: snpsites, value: path)
+
+    Output:
+        Fasta or VCF file of variant sites.
+
+    Returns:
+        Path to output file.
+    """
+
+    if len(outfmt) > 1:
+       outfmt = outfmt[0]
+
+    # Get output fasta file name
+    if outfile == None:
+        outfile = outdir + '/' + re.split('/|\.', fasta)[-2] + 'var_sites.' + outfmt
+
+    # Get output format
+    if outfmt == 'fasta':
+        flag = ' -m '
+    elif outfmt == 'vcf':
+        flag = ' -v '
+
+    # Get variant sites and write to fasta file using snp-sites
+    print('Getting variant sites using snp-sites.')
+    cmd = path['snpsites'] + ' ' + fasta + \
+          flag + ' -o ' + outfile
+
+    os.system(cmd)
+    print(outfile, 'written.')
+
+    return(outfile)
+
+
+def mask_positions(fasta, gff, outdir='.', masked_sites_file=False):
+    """Masks positions in GFF file in alignment file in fasta format.
+
+    If fasta is the whole genome alignment fasta used in gubbins 
+    and gff is the gubbins output GFF file, then recombinant 
+    regions identified by gubbins are masked (on a per-genome basis).
+    Optionally returns a text file with list of masked positions in each genome.
+
+    Args:
+        fasta: Alignment fasta file in which sites will be masked.
+        gff: GFF file containing sites that will be masked in certain genomes
+            (ex. gubbins output GFF).
+        outdir: Output file directory (default: current working directory).
+        masked_sites_file: If true, generates a text file with a list of masked positions in each genome.
+
+    Output:
+        Masked whole genome alignment file in FASTA format.
+        (Optional) Text file with list of masked positions in each genome.
+
+    Returns:
+        Path to masked fasta file.
+    """
+
+    # Read in alignment and gff file
+    print('Reading ', gff, '.', sep='')
+    gff = pd.read_csv(gff, sep='\t', skiprows=2, header=None)
+    print('Reading ', fasta, '.', sep='')
+    aln = AlignIO.read(fasta, 'fasta')
+
+    # Get indices/positions of recombinant regions identified by gubbins
+    print('Getting recombinant positions.')
+    recomb_regions = defaultdict(list)
+
+    for row in gff.iterrows():
+        start = row[1][3]
+        end = row[1][4]
+        region = list(range(start, end))
+        taxa = row[1][8].split(';')[2]
+        taxa = taxa.replace('taxa=\"', '')
+        taxa = taxa.replace('\"', '')
+        taxa = list(taxa.split())
+        for isolate in taxa:
+            for position in region:
+                recomb_regions[isolate].append(position)
+
+    # Mask indices/positions of recombinant regions identified by gubbins
+    print('Masking recombinant positions in whole genome alignment.')
+    sample_masked_indices = defaultdict(list)
+    new_aln = list()
+
+    for record in aln:
+        seq_str = list(str(record.seq))
+        masked_indices = recomb_regions.get(record.id, [])
+        for index in masked_indices:
+            seq_str[index] = 'N'
+        seq_str = ''.join(seq_str)
+        new_record = SeqRecord(Seq(seq_str), id=record.id, description='')
+        sample_masked_indices[record.id] = masked_indices
+        new_aln.append(new_record)
+
+    # Write new FASTA file with recombinant regions masked
+    fasta_outfile = outdir + '/' + re.split('/|\.', fasta)[-2] + \
+                    '_gubbins_masked.fa'
+
+    print('Writing', fasta_outfile)
+    with open(fasta_outfile, 'w') as handle:
+        SeqIO.write(new_aln, handle, 'fasta')
+
+    if masked_sites_file:
+        # Write text file with list of recombinant sites for each genome
+        text_outfile = outdir + '/' + re.split('/|\.', fasta)[-2] + \
+                       '_masked_recomb_positions.txt'
+        print('Writing', text_outfile)
+        with open(text_outfile, 'w') as handle:
+            for sample, positions in sample_masked_indices.items():
+                line = str(sample) + '\t' + ','.join(map(str, positions)) + '\n'
+                handle.write(line)
+
+    return(fasta_outfile)
+
+def count_invar_sites(fasta,gff=None,outdir='.',path={'snpsites':'/nfs/esnitkin/bin_group/anaconda3/bin/snp-sites'}):
+    """Counts invariant sites in an alignment file (fasta format).
+    
+    Gets invariant site count for As, Cs, Gs, and Ts from an alignment file. 
+    If gff is not None, positions in the GFF file will be masked before 
+    invariant sites are counted.
+
+    Args:
+        fasta: Path to alignment file in fasta format.
+        gff: [Optional] GFF file of sections of genomes to mask (ex. gubbins output GFF). 
+        outdir: Output file directory (default: current working directory).
+        path: Dictionary of paths including the path to snp-sites (key: snpsites, value: path)
+
+    Output:
+        Text file (*_invar_site_counts.txt) with invariant site counts in the following order: A,C,G,T.
+        VCF file of variants (created by snp-sites)
+        If GFF path given, masked fasta file (*_gubbins_masked.fa).
+    
+    Returns:
+        Name of text file with invariant site counts (*_invar_site_counts.txt).
+
+    """
+
+    # Mask recombinant regions before counting invariant sites
+    if gff is not None:
+        aln_file = mask_positions(fasta, gff)
+    else:
+        aln_file = fasta
+
+    # Count invariant sites in whole genome alignment
+
+    # Read in alignment
+    print('Reading ', aln_file, '.', sep='')
+    aln = AlignIO.read(aln_file, 'fasta')
+
+    # Get variant positions
+    var_site_outfile = outdir + '/' + re.split('/|\.', aln_file)[-2] + \
+                       '_snp-sites.vcf'
+    print('Getting variant positions using snp-sites.')
+    cmd = path['snpsites'] + ' ' + aln_file + \
+      ' -v ' + ' -o ' + var_site_outfile
+
+    os.system(cmd)
+
+    positions = []
+    with open(var_site_outfile) as f:
+        for line in f:
+            li=line.strip()
+            if not li.startswith("#"):
+                positions.append(line.split('\t')[1])
+
+    # Get allele for invariant sites
+    invar_sites = []
+    for record in aln:
+        seq_str = list(str(record.seq))
+        for index in positions:
+            index = int(index)
+            seq_str[index] = ''
+        #tmp = ''.join(seq_str)
+        if len(invar_sites) == 0:
+            invar_sites = seq_str
+        else:
+            for i,b in enumerate(invar_sites):
+                if b is 'N' or b is 'n':
+                    invar_sites[i] = seq_str[i]
+        invar_counts = Counter(invar_sites)
+        if invar_counts['N'] == 0 and invar_counts['n'] == 0:
+            break
+
+    del invar_counts['']
+
+    # Get invariant site count for each base
+    #print('Counting bases.')
+    #invar_counts = Counter(invar_sites)
+
+    # Write base counts to files
+    invar_counts_file = outdir + '/' + re.split('/|\.', aln_file)[-2] + \
+                          '_invar_site_counts.txt'
+    print('Writing ', invar_counts_file, ' (order: A C G T).', sep='')
+    with open(invar_counts_file, 'w') as f:
+        for base, count in sorted(invar_counts.items()):
+            #if base in ['A','a','C','c','G','g','T','t']:
+            print(base + ' ' + str(count))
+            f.write('%s ' % (count))
+            #else:
+            #    print(base + ' ' + str(count))
+    return(invar_counts_file)
diff --git a/modules/beast/fasta_functions.pyc b/modules/beast/fasta_functions.pyc
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diff --git a/modules/beast/generate_input_file_beast_pbs.sh b/modules/beast/generate_input_file_beast_pbs.sh
new file mode 100755
index 0000000..f39e241
--- /dev/null
+++ b/modules/beast/generate_input_file_beast_pbs.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+# creates file with names of certain files in directory based on what would be listed with ls
+# ex: generate_input_file_beast_pbs.sh '*.xml' 3 
+# In the example above, an input_beast.txt file would be generated with all of the xml files in the directory listed 3 times. This will cause BEAST to run in triplicate for each xml file.
+# if using wildcard, put it in quotes
+
+num=$2
+
+export num;
+
+ls $1 | perl -ne 'print $_ x $ENV{num}' > input_beast.txt
diff --git a/modules/beast/get_num_invariant_sites_v2.py b/modules/beast/get_num_invariant_sites_v2.py
new file mode 100755
index 0000000..6628f78
--- /dev/null
+++ b/modules/beast/get_num_invariant_sites_v2.py
@@ -0,0 +1,89 @@
+# Get number of invariant A's, C's, G's, and T's from a whole-genome alignment.
+# These numbers can be used in BEAST analyses.
+# V2: uses snp-sites to get VCF to find variant positions instead of SNP matrix.
+# Input:
+#   (1) Whole genome alignment file in FASTA format
+#       (Note: This should be the one used to create the variant position
+#       FASTA file used in BEAST.)
+# Output:
+#   (1) VCF file of variants (created by snp-sites)
+#   (2) Text file with number of invariant A's, C's, G's, and T's in alignment (order: A C G T)
+
+# import libraries
+import argparse
+import os
+import re
+from Bio import AlignIO
+# from Bio.SeqRecord import SeqRecord
+# from Bio.Seq import Seq
+# import os
+# from Bio import SeqIO
+from collections import Counter
+import pandas as pd
+
+# parse command line arguments (input files)
+parser = argparse.ArgumentParser(
+                description='''Get number of invariant A's, C's, G's, and T's
+                    from a whole-genome alignment.
+                    Input:
+                      (1) Whole genome alignment file in FASTA format
+                          (The one used to create the variant position
+                          FASTA file used in BEAST, or the recombination-masked version..)
+                    Output:
+		      (1) VCF file of variants (created by snp-sites)
+                      (2) Text file with number of invariant
+                          A's, C's, G's, and T's in alignment (order: A C G T)
+                    ''')
+parser.add_argument('fasta', metavar='FASTA', type=str,
+                    help='whole genome alignment file in FASTA format')
+parser.add_argument('-o', '--outdir', metavar='OUTDIR', type=str,
+                    default='.',
+                    help='''output directory
+                    (default: .)''')
+args = parser.parse_args()
+
+aln_path = args.fasta
+outdir = args.outdir
+
+# read in alignment to be able to get invariant base counts
+print('Reading ', aln_path, '.', sep='')
+aln = AlignIO.read(aln_path, 'fasta')
+
+# get variant positions
+var_site_outfile = outdir + '/' + re.split('/|\.', aln_path)[-2] + \
+                '_snp-sites.vcf'
+print('Getting variant positions using snp-sites.')
+cmd = '/nfs/esnitkin/bin_group/anaconda3/bin/snp-sites ' + aln_path + \
+      ' -v ' + ' -o ' + var_site_outfile
+
+os.system(cmd)
+
+positions = []
+with open(var_site_outfile) as f:
+    for line in f:
+        li=line.strip()
+        if not li.startswith("#"):
+            positions.append(line.split('\t')[1])
+
+# get ref allele for invariant sites
+for record in aln:
+    seq_str = list(str(record.seq))
+    for index in positions:
+        index = int(index)
+        seq_str[index] = ''
+    invar_sites = ''.join(seq_str)
+    break
+
+# get invariant site count for each base
+print('Counting bases.')
+invar_counts = Counter(invar_sites)
+
+# write base counts to files
+invar_base_counts_file = outdir + '/' + re.split('/|\.', aln_path)[-2] + \
+                '_invar_site_counts.txt'
+print('Writing ', invar_base_counts_file, ' (order: A C G T).', sep='')
+with open(invar_base_counts_file, 'w') as f:
+    for base, count in sorted(invar_counts.items()):
+        f.write('%s ' % (count))
+
+print('Done.')
diff --git a/modules/beast/insert_starting_tree_beast.sh b/modules/beast/insert_starting_tree_beast.sh
new file mode 100755
index 0000000..38b6211
--- /dev/null
+++ b/modules/beast/insert_starting_tree_beast.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+# Add starting tree to BEAST xml file (instead of starting with a random tree)
+# Arguments: 1) xml file
+#	     2) tree file
+
+tree=$(less $2)
+
+for i in $(ls $1); do
+
+outfile=$(echo $i | cut -d. -f1)
+outfile=${outfile}_st.xml
+echo $outfile
+
+sed -e 's/id="RandomTree/id="NewickTree/g' -e "s/evolution.tree.RandomTree\" estimate=\"false\"/util.TreeParser\" IsLabelledNewick=\"true\" offset=\"0\" newick=\""$tree"\"/g" -e '/<populationModel id="ConstantPopulation/,/populationModel>/d' $i > $outfile
+
+done
diff --git a/modules/beast/scotti.py b/modules/beast/scotti.py
new file mode 100644
index 0000000..4900931
--- /dev/null
+++ b/modules/beast/scotti.py
@@ -0,0 +1,108 @@
+# PIPELINE TO RUN BEAST ON FLUX
+
+# Import modules
+import sys
+import os
+import argparse
+from subprocess import call
+import re
+from fasta_functions import *
+from scotti_functions import *
+
+# Get command line arguments
+parser = argparse.ArgumentParser(description='Generate SCOTTI xmls and run in BEAST2.')
+parser.add_argument('fasta', nargs='*', help='fasta file(s) of sequences to run SCOTTI on')
+parser.add_argument('--subsets','-s', help='csv file where each row is the names of the subset of  sequences in the fasta file to run SCOTTI on')
+parser.add_argument('--overwrite',"-ov", action='store_true', help="Overwrite output files.")
+parser.set_defaults(overwrite=False)
+parser.add_argument('--dates',"-d", help='Input csv file with sampling dates associated to each sample name (same name as fasta file). If not specified, looks for SCOTTI_dates.csv',default=os.getcwd()+"SCOTTI_dates.csv")
+parser.add_argument('--hosts',"-ho", help='Input csv file with sampling hosts associated to each sample name (same name as fasta file). If not specified, looks for SCOTTI_hosts.csv',default=os.getcwd()+"SCOTTI_hosts.csv")
+parser.add_argument('--hostTimes',"-ht", help='Input csv file with the earliest times in which hosts are infectable, and latest time when hosts are infective. Use same host names as those used for the sampling hosts file. If not specified, looks for SCOTTI_hostTimes.csv',default=os.getcwd()+"SCOTTI_hostTimes.csv")
+parser.add_argument('--maxHosts',"-m", help='Maximum number of hosts (between sampled, non-sampled, and non-observed) allowed.', type=int, default=-1)
+parser.add_argument('--unlimLife',"-u", action='store_true', help="non-sampled, non-observed hosts have unlimited life span (unlimited contribution in time to the outbreak). By default non-sampled non-observed hosts have limited duration. Usually asymptomaic patients have limited contributions, while to model environmental contamination it's better to use unlimited hosts. Unlimited life span can also decrease computational demand.")#, dest='limL', action='store_true'
+parser.set_defaults(unlimLife=False)
+parser.add_argument('--penalizeMigration',"-p", action='store_true', help="penalize lineages that are still in a deme after deme closure. By default, don't (original version).")#, dest='limL', action='store_true'
+parser.set_defaults(penalizeMigration=False)
+parser.add_argument('--numIter',"-N", help='Number of iterations of the MCMC. Default=100,000,000.', type=int, default=100000000)
+parser.add_argument('--mutationModel',"-mu", help='String represnting the mutation model to be used. Possibilities are \"HKY\" or \"JC\". Further alternaties can be specified by modifying the output xml manually.', default="HKY")
+parser.add_argument('--n_reps', '-n', metavar='N', type=int, help='number of beast runs for each xml', default=3)
+parser.add_argument('--invar_sites', '-i', metavar='FILE', help='''
+                    option 1: fasta file to  get number of invariant As, Cs, Gs, and Ts not in alignment
+                    option 2: text file with number of invariant As, Cs, Gs, and Ts in alignment''')
+parser.add_argument('--gubbins_gff', '-g', metavar='GFF', help='''gubbins output GFF file to mask recombinant sites in alignment before counting invariant sites''')
+parser.add_argument('--scripts_dir', '-sd', metavar='DIR', help='directory where all of the scripts are housed', default='/nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/beast')
+args = parser.parse_args()
+# Rename variables
+fasta = args.fasta
+subsets = args.subsets
+n_reps = args.n_reps
+invar_sites = args.invar_sites
+gubbins_gff = args.gubbins_gff
+scripts_dir = args.scripts_dir
+overwrite = args.overwrite
+dates = args.dates
+hosts = args.hosts
+hostTimes = args.hostTimes
+maxHosts = args.maxHosts
+unlimLife = args.unlimLife
+penalizeMigration = args.penalizeMigration
+numIter = args.numIter
+mutationModel = args.mutationModel
+
+# Change names of sample ids and host names if needed
+new_input_files = add_prefixes(hosts, dates, hostTimes, fasta)
+
+if new_input_files is not None:
+    hosts = new_input_files[0]
+    dates = new_input_files[1]
+    hostTimes = new_input_files[2]
+    fasta = new_input_files[3]
+
+# Make subsets of fasta file
+if subsets is not None:
+    print('Getting subsets of sequences from ' + fasta)
+    fas = get_fasta_subsets(subsets,fasta)
+else:
+    fas = fasta
+
+# Get invariant site counts
+if invar_sites is not None:
+    if not invar_sites.endswith(tuple(['fa','fasta'])):
+        # Path to file with invariant site counts
+        invar_counts_file = invar_sites
+    else:
+        # Count invariant sites in whole genome alignment
+        invar_counts_file = count_invar_sites(invar_sites, gubbins_gff)
+    with open(invar_counts_file) as f:
+        invar_counts = f.readline().strip().split(' ')
+    fixedAs = int(invar_counts[0])
+    fixedCs = int(invar_counts[1])
+    fixedGs = int(invar_counts[2])
+    fixedTs = int(invar_counts[3])
+else:
+    fixedAs = 0
+    fixedCs = 0
+    fixedGs = 0
+    fixedTs = 0
+
+# For each subset, generate the xml
+xmls = []
+for fa in fas:
+    print('Processing ' + fa + '.')
+    output = fa.split('.')[0]
+    xml = generate_scotti_xml(fa, dates, hosts, hostTimes, output, overwrite, maxHosts, unlimLife, penalizeMigration, numIter, mutationModel, fixedAs, fixedCs, fixedGs, fixedTs)
+    print(xml)
+    xmls.append(xml)
+
+# Generate input file for beast script
+with open('input_beast.txt', 'w') as f:
+    for x in xmls:
+        for i in range(n_reps):
+            f.write(x + '\n')
+
+print('input_beast.txt generated.')
+
+# Generate PBS scripts and submit jobs to flux
+print('Generating PBS script(s) and submitting jobs to flux.')
+call(['perl', scripts_dir + '/beast_pbs_flux.pl', 'input_beast.txt'])
+
diff --git a/modules/beast/scotti_functions.py b/modules/beast/scotti_functions.py
new file mode 100644
index 0000000..363108d
--- /dev/null
+++ b/modules/beast/scotti_functions.py
@@ -0,0 +1,759 @@
+'''Functions to generate a SCOTTI xml file. Modified from:
+https://github.com/Taming-the-BEAST/SCOTTI-Tutorial/blob/master/scripts/SCOTTI_generate_xml.py
+'''
+
+# Import modules
+import sys
+import os
+import math
+import csv
+from collections import defaultdict
+from datetime import datetime
+import time
+import re
+
+#does it represent an integer?
+def IsInt(s):
+    try: 
+        int(s)
+        return True
+    except ValueError:
+        return False
+
+#does it represent a float?
+def IsFloat(s):
+    try: 
+        float(s)
+        return True
+    except ValueError:
+        return False
+
+#make tree string into format for mugration
+def removeStates(treeString):
+        l=len(treeString)
+        newString=""
+        i=0
+        while i < l :
+                if treeString[i]=="[":
+                        while treeString[i]!="]":
+                                i+=1
+                        i+=1
+                newString+=treeString[i]
+                i+=1
+        return newString                
+
+#scale branch lengths 
+def scaleTreeString(treeString,scale):
+        l=len(treeString)
+        newString=""
+        i=0
+        while i < l :
+                if treeString[i]==":":
+                        newString+=":"
+                        numb=""
+                        i+=1
+                        while treeString[i]!="," and treeString[i]!=")" and treeString[i]!=";":
+                                numb+=treeString[i]
+                                i+=1
+                        newString+=str(float(numb)*scale)
+                newString+=treeString[i]
+                i+=1
+        return newString
+
+def generate_scotti_xml(fasta, dates, hosts, hostTimes, output=None, overwrite=False, maxHosts=None,  unlimLife=False, penalizeMigration=False, numIter=100000000, mutationModel='HKY', fixedAs=0, fixedCs=0, fixedGs=0, fixedTs=0):
+    """Generates an xml file that can be run in BEAST2.
+
+    Args:
+        fasta:          Input fasta alignment. If not specified, looks in the
+                        working directory for SCOTTI_aligment.fas
+        output:		    output file that will contain the newly created SCOTTI
+                        xml to be run with BEAST2. (default: fasta file base name)
+        overwrite:      Overwrite output files.
+        dates:          Input csv file with sampling dates associated to each
+                        sample name (same name as fasta file). If not
+                        specified, looks for SCOTTI_dates.csv
+        hosts:		Input csv file with sampling hosts associated to each
+                        sample name (same name as fasta file). If not
+                        specified, looks for SCOTTI_hosts.csv
+        hostTime:       Input csv file with the earliest times in which hosts
+                        are infectable, and latest time when hosts are
+                        infective. Use same host names as those used for the
+                        sampling hosts file. If not specified, looks for
+                        SCOTTI_hostTimes.csv
+        maxHosts:       Maximum number of hosts (between sampled, non-sampled,
+                        and non-observed) allowed.
+        unlimLife:      span (unlimited contribution in time to the outbreak).
+                        By default non-sampled non-observed hosts have limited
+                        duration. Usually asymptomaic patients have limited
+                        contributions, while to model environmental
+                        contamination it's better to use unlimited hosts.
+                        Unlimited life span can also decrease computational
+                        demand.
+        penalizeMigration: penalize lineages that are still in a deme after deme
+                        closure. By default, don't (original version).
+        numIter:        Number of iterations of the MCMC. Default=100,000,000.
+        mutationModel:  String represnting the mutation model to be used.
+                        Possibilities are "HKY" or "JC". Further alternaties
+                        can be specified by modifying the output xml manually.
+        fixedAs:        Number of sites fixed for A in the genome and not
+                        included in the alignment.
+        fixedCs:        Number of sites fixed for C in the genome and not
+                        included in the alignment.
+        fixedGs:        Number of sites fixed for G in the genome and not
+                        included in the alignment.
+        fixedTs:        Number of sites fixed for T in the genome and not
+                        included in the alignment.
+
+    Output: 
+        XML file that can be used to run SCOTTI in BEAST2.
+    """
+    # Set output file name if none specified
+    if output is None:
+        output = fasta.split('.')[0]
+    
+    # Check if input arguments make sense
+    if (not (os.path.isfile(fasta))):
+        print("Error: input fasta file not found, please specify it correctly in input using the option -f .\n")
+        return()
+    if (os.path.isfile(output + ".xml")) and (not overwrite):
+        print("Output xml file already exists, to allow overwriting of output file use option -ov, otherwise specify a different output file.\n")
+        return()
+    if not (os.path.isfile(dates)):
+        print("Error: input dates csv file not found, please specify it correctly in input using the option -d .\n")
+        return()
+    if not (os.path.isfile(hosts)):
+        print("Error: input hosts csv file not found, please specify it correctly in input using the option -s .\n")
+        return()
+    if not (os.path.isfile(hostTimes)):
+        print("Error: input hosts csv file not found, please specify it correctly in input using the option -i .\n")
+        return()
+    if (numIter<=0):
+        print("Error: the number of MCMC iterations specified is negative or null.\n")
+        return()
+    elif (numIter<=1000):
+        print("Very small number of MCMC iterations, might want to specifiy something bigger.\n")
+    if (mutationModel!="HKY" and mutationModel!="JC"):
+        print("Mutation model not supported. Please specify something among \"HKY\" or \"JC\".\n")
+        return()
+    if (fixedAs<0 or fixedCs<0 or fixedGs<0 or fixedTs<0):
+        print("Error: the number of fixed sites for each base type must be positive.\n")
+        return()
+
+    #read fasta file
+    sqFile=open(fasta)
+    seqs={}
+    line=sqFile.readline()
+    linesplit=line.split()
+    while line!="" and (len(linesplit)<1 or linesplit[0][0]!=">"):
+        line=sqFile.readline()
+        linesplit=line.split()
+    if line=="":
+        print("Wrong format in fasta file\n")
+        return()
+    while line!="":
+        if len(linesplit[0])>1:
+            name=linesplit[0].replace(">","")
+        else:
+            name=linesplit[1]
+        seq=""
+        line=sqFile.readline()
+        linesplit=line.split()
+        while line!="" and (len(linesplit)>0  and linesplit[0][0]!=">") and line!="\n":
+            seq+=linesplit[0]
+            line=sqFile.readline()
+            linesplit=line.split()
+        if name in list(seqs.keys()):
+            print("Error, multiple entries with same name "+name+" in fasta file. Only first part of the name line is used for the name.")
+            return()
+        seqs[name]=seq
+        while line!="" and  (len(linesplit)<1 or linesplit[0][0]!=">"):
+            line=sqFile.readline()
+            linesplit=line.split()
+    sqFile.close()
+
+    #read in file with host-sample information
+    hFile=open(hosts)
+    hosts={}
+    line=hFile.readline()
+    linesplit=("".join(line.split())).split(",")  
+    while line!="" and len(linesplit)>1: 
+        sam=linesplit[0]
+        hos=linesplit[1]
+        if not (sam in list(seqs.keys())):
+            print("Error: sample "+sam+" found in sample-host file but not found in the fasta file.")
+            return()
+        if sam in list(hosts.keys()):
+            print("Error, multiple entries with same name "+sam+" in hosts file. ")
+            return()
+        hosts[sam]=hos
+        line=hFile.readline()
+        linesplit=("".join(line.split())).split(",")
+        while line=="\n":
+            line=hFile.readline()
+            linesplit=("".join(line.split())).split(",")
+    hFile.close()
+
+    #read in sampling dates
+    dFile=open(dates)
+    dates={}
+    line=dFile.readline()
+    linesplit=("".join(line.split())).split(",")  
+    while line!="" and len(linesplit)>1:  
+        sam=linesplit[0]
+        dat=linesplit[1]
+        if not (IsFloat(dat)):
+            print("Error: date "+str(dat)+" for sample "+sam+" cannot be converted to a number")
+            return()
+        if not (sam in list(seqs.keys())):
+            print("Error: sample "+sam+" found in sampling dates file but not found in the fasta file.")
+            return()
+        if sam in list(dates.keys()):
+            print("Error, multiple entries with same name "+sam+" in dates file. ")
+            return()
+        dates[sam]=float(dat)
+        line=dFile.readline()
+        linesplit=("".join(line.split())).split(",")
+        while line=="\n":
+            line=dFile.readline()
+            linesplit=("".join(line.split())).split(",")
+    dFile.close()
+        
+    #read in file with host times information
+    hFile=open(hostTimes)
+    hostT={}
+    line=hFile.readline()
+    linesplit=("".join(line.split())).split(",")  
+    while line!="" and len(linesplit)>2: 
+        hos=linesplit[0]
+        dat=float(linesplit[1])
+        dat2=float(linesplit[2])
+        if (not (IsFloat(dat))) or (not (IsFloat(dat2))):
+            print("Error: date "+str(dat)+" or "+str(dat2)+" for sample "+sam+" cannot be converted to a number")
+            return()
+        #if not (hos in seqs.keys()):
+        #    print "Error: sample "+hos+" found in host times file but not found in the fasta file."
+        #    return()
+        if hos in list(hostT.keys()):
+            print("Error, multiple entries with same name "+hos+" in hosts-times file. ")
+            return()
+        hostT[hos]=[dat, dat2]
+        line=hFile.readline()
+        linesplit=("".join(line.split())).split(",")
+        while line=="\n":
+            line=hFile.readline()
+            linesplit=("".join(line.split())).split(",")
+    hFile.close()
+
+    #check that the details are correct
+    for s in list(seqs.keys()):
+        if not (s in list(dates.keys())):
+            print("Error, no date specified for sample "+s)
+            return()
+        if not (s in list(hosts.keys())):
+            print("Error, no host specified for sample "+s)
+            return()
+    for s in list(hosts.keys()):
+        if not (s in list(seqs.keys())):
+            print("Error, no sequence specified for sample "+s)
+            return()
+        if not (s in list(dates.keys())):
+            print("Error, no date specified for sample "+s)
+            return()
+    for s in list(dates.keys()):
+        if not (s in list(seqs.keys())):
+            print("Error, no sequence specified for sample "+s)
+            return()
+        if not (s in list(hosts.keys())):
+            print("Error, no host specified for sample "+s)
+            return()
+    for s in list(hosts.keys()):
+        if not (hosts[s] in list(hostT.keys())):
+            print("Warning: no dates specified for host "+hosts[s]+". I will assume this host is available throghtout")
+            hostT[hosts[s]]=[-1000000000000,1000000000000]
+
+
+    for h in list(hostT.keys()):
+        found=0
+        for s in list(hosts.keys()):
+            if hosts[s]==h:
+                found=1
+        if found==0:
+            print("Warning: no sample specified for host "+h+". I will assume this host could have been infected or not. If you are sure this host was infected, then please include a non-informative sample (all \"N\"s) from it.")
+
+    xml=open(output+'.xml',"w")
+    xml.write("<beast version=\'2.0\' namespace=\'beast.evolution.alignment:beast.core:beast.core.parameter:beast.evolution.tree:beast.evolution.tree.coalescent:beast.core.util:beast.evolution.operators:beast.evolution.sitemodel:beast.evolution.substitutionmodel:beast.evolution.likelihood:beast.evolution.tree:beast.math.distributions:multitypetreeVolz.distributions:multitypetreeVolz.operators:multitypetreeVolz.util\'>\n\n"+"  <data id=\"alignmentVar\" dataType=\"nucleotide\">\n")
+    for s in list(seqs.keys()):
+        xml.write("    <sequence taxon=\'"+s+"\' value=\'"+seqs[s]+"\'/>\n")
+    xml.write("  </data>\n")
+    st=""
+    if fixedAs>0 and fixedCs>0 and fixedGs>0 and fixedTs>0:
+        st="constantSiteWeights=\'"+str(fixedAs)+" "+str(fixedCs)+" "+str(fixedGs)+" "+str(fixedTs)+"\'"
+    xml.write("  <data id=\'alignment\' spec=\'FilteredAlignment\' filter=\'-\' data=\'@alignmentVar\' "+st+"/>\n\n")
+
+    #hosts
+    xml.write("   <typeTraitSet spec=\'TraitSetWithLimits\' id=\'typeTraitSet\' traitname=\"type\" value=\"")
+    hL=list(hosts.keys())
+    for s in hL:
+        xml.write(s+"="+hosts[s])
+        if s!=hL[len(hL)-1]:
+            xml.write(",")
+    xml.write("\">\n     <taxa spec=\'TaxonSet\' alignment=\'@alignment\'/>\n   </typeTraitSet>\n\n")
+
+    #dates
+    xml.write("   <timeTraitSet spec=\'TraitSetWithLimits\' id=\'timeTraitSet\' traitname=\"date-forward\" value=\"")
+    dL=list(dates.keys())
+    for s in range(len(dL)):
+        xml.write(dL[s]+"="+str(dates[dL[s]]))
+        if (s!=(len(dL)-1)):
+            xml.write(",")
+    xml.write("\">\n     <taxa spec=\'TaxonSet\' alignment=\'@alignment\'/>\n   </timeTraitSet>\n\n")
+
+    #end infectability
+    xml.write("   <endInfectionsTraitSet spec=\'InfectionTraitSet\' id=\'endInfectionsTraitSet\' type=\'end\' traitname=\"date-forward\" value=\"")
+    dL=list(hostT.keys())
+    for s in dL:
+        xml.write(s+"="+str(hostT[s][1]))
+        if s!=dL[len(dL)-1]:
+            xml.write(",")
+    xml.write("\">\n          <taxaDates idref=\'timeTraitSet\'/>\n           <taxaTypes idref=\'typeTraitSet\'/>\n   </endInfectionsTraitSet>\n\n")
+
+    #start infectability
+    xml.write("   <startInfectionsTraitSet spec=\'InfectionTraitSet\' id=\'startInfectionsTraitSet\' type=\'start\' traitname=\"date-forward\" value=\"")
+    dL=list(hostT.keys())
+    for s in dL:
+        xml.write(s+"="+str(hostT[s][0]))
+        if s!=dL[len(dL)-1]:
+            xml.write(",")
+    xml.write("\">\n          <taxaDates idref=\'timeTraitSet\'/>\n           <taxaTypes idref=\'typeTraitSet\'/>\n   </startInfectionsTraitSet>\n\n")
+
+    #check that times fit
+    for s in list(seqs.keys()):
+        h=hosts[s]
+        if dates[s]<=hostT[hosts[s]][0]:
+            print("Error, sampling time "+str(dates[s])+" of sample "+s+" must be after start of infectability time "+str(hostT[hosts[s]][0])+" of host "+h)
+            return()
+        if dates[s]>=hostT[hosts[s]][1]:
+            print("Error, sampling time "+str(dates[s])+" of sample "+s+" must be before end of infectiousness time "+str(hostT[hosts[s]][0])+" of host "+h)
+            return()
+
+    #include mutation model
+    xml.write("   <siteModel spec=\"SiteModel\" id=\"siteModel\">\n     <mutationRate spec=\'RealParameter\' id=\"mutationRate\" value=\"1.0\"/>\n     <substModel spec=\"")
+    if mutationModel=="HKY":
+       xml.write("HKY\">\n       <kappa spec=\'RealParameter\' id=\"hky.kappa\" value=\"1.0\"/>\n")
+    elif mutationModel=="JC":
+       xml.write("jc69\">\n")
+    elif mutationModel=="GTR":
+       xml.write("GTR\" rateAC=\"@rateAC\" rateAG=\"@rateAG\" rateAT=\"@rateAT\" rateCG=\"@rateCG\" rateGT=\"@rateGT\" >\n       <parameter estimate=\'false\' name=\"rateCT\" lower=\"0.0\" value=\"1.0\" id=\"rateCTfixed\"/>\n")
+    else:
+        print("Error, mutation model "+mutationModel+" not recognized.")
+        return()
+    xml.write("       <frequencies estimate=\"false\" spec=\'Frequencies\'>\n        <frequencies spec=\'RealParameter\' id=\""+mutationModel+".freq\" value=\"0.25 0.25 0.25 0.25\"/>\n       </frequencies>\n     </substModel>\n   </siteModel>\n\n")
+                  
+    #migration model
+    nHos=len(list(hostT.keys()))
+    meanL=0.0
+    for h in list(hostT.keys()):
+        meanL+=hostT[h][1]-hostT[h][0]
+    meanL=meanL/len(list(hostT.keys()))
+    ma=maxHosts
+    if ma<nHos:
+        ma=nHos+5
+        print("Maximum number of hosts specified is less than the minimum number of hosts. Setting it to "+str(int(ma)))
+    if ma<(nHos+2):
+        print("Warning: the maximum number of hosts is less than "+str(int(nHos+2))+". If there are problems in the execution of the BEAST2 analysis, try a higher values of maxHosts.")
+    start=(nHos+ma)/2
+    if start<(nHos+2) and ma>=(nHos+2):
+        start=nHos+2
+    xml.write("   <migrationModelUniform spec=\'MigrationModelUniform\' id=\'migModel\' minDemes=\'"+str(int(nHos))+"\'>\n     <rate spec=\'RealParameter\' value=\""+str(1.0/(meanL*nHos))+"\" dimension=\"1\" id=\"rate\"/>\n     <popSize spec=\'RealParameter\' value=\"1.0\" dimension=\"1\" id=\"popSize\"/>\n     <numDemes spec=\'IntegerParameter\' value=\""+str(int(start))+"\" dimension=\"1\" id=\"numDemes\" lower=\'"+str(int(nHos))+"\' upper=\'"+str(int(ma))+"\'/>\n     <trait idref=\'startInfectionsTraitSet\' type=\'start\'/>\n     <trait idref=\'endInfectionsTraitSet\' type=\'end\'/>\n   </migrationModelUniform>\n\n")            
+
+    #priors
+    xml.write("   <input spec=\'CompoundDistribution\' id=\'parameterPriors\'>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@mutationRate\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"-3.0\" S=\"6.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@popSize\">\n       <distr spec=\"LogNormalDistributionModel\"  M=\"0.0\" S=\"6.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rate\">\n       <distr spec=\'LogNormalDistributionModel\' M=\""+str(math.log(1.0/(meanL*nHos)))+"\" S=\"0.2\"/>\n     </distribution>\n")
+    if mutationModel=="HKY":
+        xml.write("     <distribution spec=\'beast.math.distributions.Prior\' x=\"@hky.kappa\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n   </input>\n\n")
+    elif mutationModel=="JC":
+       xml.write("   </input>\n\n")
+    elif mutationModel=="GTR":
+        xml.write("     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateAC\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateAG\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateAT\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateCG\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateGT\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n   </input>\n\n")      
+                  
+    #Likelihoods
+    xml.write("<input spec=\'TreeLikelihood\' id=\"treeLikelihood\">\n     <data idref=\"alignment\"/>\n     <tree idref=\"tree\"/>\n     <siteModel idref=\'siteModel\'/>\n   </input>\n\n   <input spec=\'StructuredCoalescentTreeDensityNew\' id=\'treePrior\' limitedLifespan=\'"+str(unlimLife)+"\' penalizeMigration=\'"+str(penalizeMigration)+"\'>\n     <multiTypeTreeConcise idref=\"tree\"/>\n     <migrationModelUniform idref=\"migModel\"/>\n   </input>\n\n   <run spec=\"MCMC\" id=\"mcmc\" chainLength=\""+str(numIter)+"\" storeEvery=\"10000\">\n\n")
+
+    #migration model
+    xml.write("     <init spec=\'StructuredCoalescentMultiTypeTreeConcise\' id=\'tree\' nTypes=\""+str(int(start))+"\">\n         <migrationModelUniform spec=\'MigrationModelUniform\' minDemes=\'"+str(int(nHos))+"\'>\n             <rate spec=\'RealParameter\' value=\""+str(1.0/(meanL*nHos))+"\" dimension=\"1\"/>\n             <popSize spec=\'RealParameter\' value=\"1.0\" dimension=\"1\"/>\n             <numDemes spec=\'IntegerParameter\' value=\""+str(int(start))+"\" dimension=\"1\"/>\n         </migrationModelUniform>\n         <trait idref=\'typeTraitSet\'/>\n         <trait idref=\'timeTraitSet\'/>\n     </init>\n\n")    
+
+    #state nodes              
+    xml.write("     <state>\n       <stateNode idref=\"tree\"/>\n       <stateNode idref=\"rate\"/>\n       <stateNode idref=\"popSize\"/>\n       <stateNode idref=\"numDemes\"/>\n       <stateNode idref=\"mutationRate\"/>\n")
+    if mutationModel=="HKY":
+        xml.write("       <stateNode idref=\"hky.kappa\"/>\n")
+    elif mutationModel=="GTR":              
+        xml.write("       <stateNode idref=\"rateAC\"/>\n       <stateNode idref=\"rateAG\"/>\n       <stateNode idref=\"rateAT\"/>\n       <stateNode idref=\"rateCG\"/>\n       <stateNode idref=\"rateGT\"/>\n")
+    xml.write("       <stateNode idref=\""+mutationModel+".freq\"/>\n     </state>\n\n")     
+                  
+    #compound distribution
+    xml.write("     <distribution spec=\'CompoundDistribution\' id=\'posterior\'>\n       <distribution idref=\"treeLikelihood\"/>\n       <distribution idref=\'treePrior\'/>\n       <distribution idref=\"parameterPriors\"/>\n     </distribution>\n\n")
+                  
+    #operators
+    xml.write("     <operator spec=\'ScaleOperator\' id=\'RateScaler\' parameter=\"@rate\" scaleFactor=\"0.8\" weight=\"1\"/>\n     <operator spec=\"ScaleOperator\" id=\"PopSizeScaler\" parameter=\"@popSize\" scaleFactor=\"0.8\" weight=\"1\"/>\n     <operator spec=\"ShortRangeUniformOperator\" id=\"NumDemesScaler\" parameter=\"@numDemes\" weight=\"1\"/>\n     <operator spec=\"ScaleOperator\" id=\"muRateScaler\" parameter=\"@mutationRate\" scaleFactor=\"0.8\" weight=\"1\"/>\n     <operator spec=\"DeltaExchangeOperator\" id=\"freqExchanger\" parameter=\"@"+mutationModel+".freq\" delta=\"0.01\" weight=\"0.1\"/>\n     <operator id=\'treeScaler.t\' spec=\'ScaleOperator\' scaleFactor=\"0.5\" weight=\"3\" tree=\"@tree\"/>\n     <operator id=\'treeRootScaler.t\' spec=\'ScaleOperator\' scaleFactor=\"0.5\" weight=\"3\" tree=\"@tree\" rootOnly=\'true\'/>\n     <operator id=\'UniformOperator.t\' spec=\'Uniform\' weight=\"30\" tree=\"@tree\"/>\n     <operator id=\'SubtreeSlide.t\' spec=\'SubtreeSlide\' weight=\"15\" gaussian=\"true\" size=\"1.0\" tree=\"@tree\"/>\n     <operator id=\'narrow.t\' spec=\'Exchange\' isNarrow=\'true\' weight=\"15\" tree=\"@tree\"/>\n     <operator id=\'wide.t\' spec=\'Exchange\' isNarrow=\'false\' weight=\"3\" tree=\"@tree\"/>\n     <operator id=\'WilsonBalding.t\' spec=\'WilsonBalding\' weight=\"3\" tree=\"@tree\"/>\n")
+    if mutationModel=="HKY":
+        xml.write("     <operator spec=\'ScaleOperator\' id=\'kappaScaler\' parameter=\"@hky.kappa\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n")
+    elif mutationModel=="GTR": 
+        xml.write("     <operator spec=\'ScaleOperator\' id=\'ACScaler\' parameter=\"@rateAC\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'AGScaler\' parameter=\"@rateAG\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'ATScaler\' parameter=\"@rateAT\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'CGScaler\' parameter=\"@rateCG\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'GTScaler\' parameter=\"@rateGT\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n")
+    xml.write("\n")
+
+    #loggers
+    xml.write("     <logger logEvery=\""+str(int(numIter/10000))+"\" fileName=\""+output+".log\">\n       <model idref=\'posterior\'/>\n       <log idref=\"posterior\"/>       <log idref=\"treeLikelihood\"/>\n       <log idref=\"migModel\"/>\n       <log idref=\"mutationRate\"/>\n       <log spec=\'TreeHeightLogger\' tree=\'@tree\'/>\n       <log idref=\""+mutationModel+".freq\"/>\n")
+    if mutationModel=="HKY":
+        xml.write("       <log idref=\"hky.kappa\"/>\n")
+    elif mutationModel=="GTR":               
+        xml.write("       <log idref=\"rateAC\"/>\n       <log idref=\"rateAG\"/>\n       <log idref=\"rateAT\"/>\n       <log idref=\"rateCG\"/>\n       <log idref=\"rateGT\"/>\n")
+    xml.write("     </logger>\n")
+         
+    xml.write("     <logger logEvery=\""+str(int(numIter/5000))+"\" fileName=\""+output+".trees\" mode=\"tree\">\n       <log idref=\'treePrior\'/>\n     </logger>\n")
+
+    xml.write("     <logger logEvery=\""+str(int(numIter/1000))+"\">\n       <model idref=\'posterior\'/>\n       <log idref=\"posterior\"/>\n       <log idref=\"treeLikelihood\"/>\n       <log spec=\'TreeHeightLogger\' tree=\'@tree\'/>\n       <log idref=\"migModel\"/>\n       <log idref=\"mutationRate\"/>\n       <ESS spec=\'ESS\' name=\'log\' arg=\"@treePrior\"/>\n       <ESS spec=\'ESS\' name=\'log\' arg=\"@posterior\"/>\n     </logger>\n\n   </run>\n </beast>\n\n")
+
+    return(output+'.xml')
+
+
+def check_list_int(li):
+    """Checks a list to see if any of the values can be converted to int.
+
+    If any value in the input list can be converted to an int, True is 
+    returned. Otherwise, False  is returned.
+
+    Args:
+        li: list to check
+
+    Returns:
+        Boolean value. True if at least one element in the list can be 
+        converted to int, false otherwise.    
+    """
+    no_numbers = True
+    for i in li:
+        try:
+            int(i)
+            no_numbers = False
+            break
+        except:
+            continue
+    return not no_numbers
+
+def add_prefixes(hosts, dates, hostTimes, fasta, id_pref='S', host_pref='H'):
+    """Changes sequence id and host name prefixes in scotti input.
+
+    Checks to see if the sequence ids and host names in the scotti input
+    files are numeric. If they are, adds a character to the beginning of each
+    sample id ('S') and host ('H') and writes output files with renamed.
+    This fixes an error that occurs when numeric sequences are used to 
+    generate the MCC tree.
+
+    Args:
+        hosts: csv where the first column is the sample id and the 
+            second column is the host
+        dates: csv where the first column is the sample id and the
+            second column is the sampling date 
+        hostTimes: csv where the first column is the host, the second
+            column is the earliest time the host is infectable, and
+            the third column is the latest time the host is infective
+        fasta: list of fasta file(s) to make scotti xml(s)
+        id_pref: the prefix to prepend to the sample names, if needed 
+            (default: 'S')
+        host_pref: the prefix to prepend to the host names, if needed
+            (default: 'H') 
+
+    Output:
+        Nothing if all sample ids and host names already have a letter in them.
+        Up to 4 renamed files if some sample ids and/or host names do not have 
+        a letter in them. These are the original files renamed as .renamed.csv
+        and .renamed.fa (for the fasta file)
+
+    Returns:
+        Nothing if all sample ids and host names already have a letter in them.
+        A list with the names of the output files if they are generated.
+        The order of the files is: hosts, dates, hostTimes, fasta
+    """
+
+    hosts_dict = defaultdict(list)
+
+    with open(hosts) as f:
+        reader = csv.reader(f)
+        for row in reader:
+            for (i,v) in enumerate(row):
+                hosts_dict[i].append(v)    
+
+    i_hosts = hosts_dict[0] 
+    h_hosts = hosts_dict[1]
+
+    num_in_i = check_list_int(i_hosts)
+    num_in_h = check_list_int(h_hosts)
+
+    if all([not num_in_i, not num_in_h]):
+        return
+
+    dates_dict = defaultdict(list)
+
+    with open(dates) as f:
+        reader = csv.reader(f)
+        for row in reader:
+            for (i,v) in enumerate(row):
+                dates_dict[i].append(v)
+
+    i_dates = dates_dict[0] 
+    d_dates = dates_dict[1]
+
+    times_dict = defaultdict(list)
+
+    with open(hostTimes) as f:
+        reader = csv.reader(f)
+        for row in reader:
+            for (i,v) in enumerate(row):
+                times_dict[i].append(v)        
+
+    h_times = times_dict[0] 
+    t1_times = times_dict[1]
+    t2_times = times_dict[2]
+
+    hosts_renamed = hosts.split('.')[0] + '.renamed.csv'
+    dates_renamed = dates.split('.')[0] + '.renamed.csv'
+    times_renamed = hostTimes.split('.')[0] + '.renamed.csv'
+    fasta_renamed = [fa.split('.')[0] + '.renamed.fa' for fa in fasta]
+  
+    if num_in_i:
+        i_hosts_renamed = [id_pref + x for x in i_hosts]
+        i_dates_renamed = [id_pref + x for x in i_dates]
+        for i,fa in enumerate(fasta):
+            os.system('sed \'s/>/>S/g\' ' + fa + ' > ' + fasta_renamed[i])
+    else:
+        i_hosts_renamed = i_hosts
+        i_dates_renamed = i_dates
+    
+    if num_in_h:
+        h_hosts_renamed = [host_pref + x for x in h_hosts]
+        h_times_renamed = [host_pref + x for x in h_times]
+    else:
+        h_hosts_renamed = h_hosts
+        h_times_renamed = h_times
+
+    with open(hosts_renamed, 'w') as f:
+        writer = csv.writer(f)
+        writer.writerows(zip(i_hosts_renamed, h_hosts_renamed))
+
+    with open(dates_renamed, 'w') as f:
+        writer = csv.writer(f)
+        writer.writerows(zip(i_dates_renamed, d_dates))
+
+    with open(times_renamed, 'w') as f:
+        writer = csv.writer(f)
+        writer.writerows(zip(h_times_renamed, t1_times, t2_times))
+    
+    print('Changed prefixes for ids and/or hosts. New files:\n' + 
+        hosts_renamed + '\n' + dates_renamed + '\n' + times_renamed + '\n' + ' '.join(fasta_renamed))
+
+    return [hosts_renamed, dates_renamed, times_renamed, fasta_renamed]
+
+def get_seconds_since_epoch(date):
+    """Gets econds since epoch of a datetime date.
+
+    Args: 
+        date: datetime date
+
+    Returns:
+        seconds since eopch
+
+    """
+    return time.mktime(date.timetuple())
+
+
+def convert_ymd_to_decimal(dates):
+    """Converts a list of dates in yyyy-mm-dd or yyyy/mm/dd format to decimal format.
+
+    Modified from: https://stackoverflow.com/questions/6451655/python-how-to-convert-datetime-dates-to-decimal-years
+
+    Args:
+        dates: list of dates to convert
+    
+    Returns:
+        list of dates in decimal format
+
+    """
+
+    decimal_dates = []
+
+    for d in dates:
+        if re.search('-',d) is not None:
+            date = datetime.strptime(d, '%Y-%m-%d')
+        elif re.search('/',d) is not None:
+            date = datetime.strptime(d, '%Y/%m/%d')
+        else:
+            print('Dates not in correct format. Format should be yyyy-mm-dd or yyyy/mm/dd')
+            return
+
+        start_this_year = datetime(year=date.year, month=1, day=1)
+        start_next_year = datetime(year=date.year+1, month=1, day=1)
+
+        year_elapsed = get_seconds_since_epoch(date) - get_seconds_since_epoch(start_this_year)
+        year_duration = get_seconds_since_epoch(start_next_year) - get_seconds_since_epoch(start_this_year)
+        fraction = year_elapsed/year_duration
+
+        decimal_dates.append(date.year + fraction)
+
+    return decimal_dates
+        
+
+def generate_csvs_for_scotti(csv_file, id_col, host_col, culture_date_col,  
+                             first_date_col, last_date_col, 
+                             alt_first_date_col=None, outdir='.',outpref=None):
+    """Takes as input a csv file with sample ids, host ids, and dates, and 
+    makes csvs to be used in scotti.
+
+    The input csv should containt a column for each of the following: sample 
+    id, host id, first date, last date. More columns are okay. The output csvs
+    will be (id,host), (id,culture_date), (host,first_date,last_date)
+
+    Args:
+        csv_file: csv file with sample id, host id, first date, last date
+        id_col: column name for sample ids
+        host_col: column name for host ids
+        culture_date_col: column name for culture dates
+        first_date_col: column name for first date
+        last_date_col: column name for last date
+        alt_first_date_col: alternative first date (if first_date_col value is NA)
+        outdir: output directory (default: current working directory)
+        outpref: output file prefix (defaut: input file base name)
+
+    Output:
+        host csv: (id,host)
+        culture date csv: (id,culture_date)
+        first last date csv: (host,first_date,last_date)
+    """ 
+
+    # Read in data
+    columns = defaultdict(list)
+
+    with open(csv_file) as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            for (k,v) in row.items():
+                columns[k].append(v)
+    
+    # Get columns of interest
+    ids = columns[id_col]
+    hosts = columns[host_col]
+    culture_dates = columns[culture_date_col]
+    first_date = columns[first_date_col]
+    last_date = columns[last_date_col]
+    # If some first dates are NA (ex. no first negative culture), 
+    # use alternative first date (ex. first positive culture)
+    if alt_first_date_col is not None:
+        alt_first_date = columns[alt_first_date_col]
+        for i,d in enumerate(first_date):
+            if d == 'NA':
+                first_date[i] = alt_first_date[i]
+
+    # Convert dates to decimal format
+    culture_dates_dec = convert_ymd_to_decimal(culture_dates)
+    first_date_dec = convert_ymd_to_decimal(first_date)
+    last_date_dec = convert_ymd_to_decimal(last_date)
+
+    # Set output file names
+    if outpref is None:
+        outpref = csv.split('.')[0]
+    hosts_csv = outdir + '/' + outpref + '_hosts.csv'
+    dates_csv = outdir + '/' + outpref + '_dates.csv'
+    hostTimes_csv = outdir + '/' + outpref + '_hostTimes.csv'
+
+    # Write hosts output file
+    with open(hosts_csv, 'w') as f:
+        writer = csv.writer(f)
+        writer.writerows(zip(ids, hosts))
+
+    # Write dates output file
+    with open(dates_csv, 'w') as f:
+        writer = csv.writer(f)
+        writer.writerows(zip(ids, culture_dates_dec))
+
+    # Get unique hosts and first/last date
+    idxs = [idx for idx, item in enumerate(hosts) if item not in hosts[:idx]]
+    uniq_hosts = [hosts[i] for i in idxs]
+    uniq_first_date  = [first_date_dec[i] for i in idxs]
+    uniq_last_date  = [last_date_dec[i] for i in idxs]
+
+    # Write hostTimes output file
+    with open(hostTimes_csv, 'w') as f:
+        writer = csv.writer(f)
+        writer.writerows(zip(uniq_hosts, uniq_first_date, uniq_last_date))
+
+    return([hosts_csv, dates_csv, hostTimes_csv])
+
+
+def subset_scotti_csvs(hosts, dates, hostTimes, fasta):
+    """Subsets scotti csvs to only include samples and hosts in the fasta file.
+
+    csv files for each input fasta will be generated.
+
+    Args:
+        hosts: csv where the first column is the sample id and the 
+            second column is the host
+        dates: csv where the first column is the sample id and the
+            second column is the sampling date 
+        hostTimes: csv where the first column is the host, the second
+            column is the earliest time the host is infectable, and
+            the third column is the latest time the host is infective
+        fasta: list of fasta file(s) to make scotti xml(s)
+
+    Output:
+
+    Returns:
+
+    """
+
+    hosts_dict = defaultdict(list)
+
+    with open(hosts) as f:
+        reader = csv.reader(f)
+        for row in reader:
+            for (i,v) in enumerate(row):
+                hosts_dict[i].append(v)
+
+    i_hosts = hosts_dict[0]
+    h_hosts = hosts_dict[1]
+
+    dates_dict = defaultdict(list)
+
+    with open(dates) as f:
+        reader = csv.reader(f)
+        for row in reader:
+            for (i,v) in enumerate(row):
+                dates_dict[i].append(v)
+
+    i_dates = dates_dict[0]
+    d_dates = dates_dict[1]
+
+    times_dict = defaultdict(list)
+
+    with open(hostTimes) as f:
+        reader = csv.reader(f)
+        for row in reader:
+            for (i,v) in enumerate(row):
+                times_dict[i].append(v)
+
+    h_times = times_dict[0]
+    t1_times = times_dict[1]
+    t2_times = times_dict[2]
+
+    hosts_renamed = hosts.split('.')[0] + '.renamed.csv'
+    dates_renamed = dates.split('.')[0] + '.renamed.csv'
+    times_renamed = hostTimes.split('.')[0] + '.renamed.csv'
+    fasta_renamed = [fa.split('.')[0] + '.renamed.fa' for fa in fasta]
+
+    # For each fasta file
+    for fa in fasta:
+        # Get ids from fasta file
+        fa_ids = []
+        with open(fa) as f:
+            for line in f:
+                if re.search('^>', line) is not None:
+                    fa_ids.append(re.sub('^>', '', line))
+        
+         
+     
diff --git a/modules/beast/scotti_functions.py.bak b/modules/beast/scotti_functions.py.bak
new file mode 100644
index 0000000..1754c29
--- /dev/null
+++ b/modules/beast/scotti_functions.py.bak
@@ -0,0 +1,408 @@
+'''Functions to generate a SCOTTI xml file. Modified from:
+https://github.com/Taming-the-BEAST/SCOTTI-Tutorial/blob/master/scripts/SCOTTI_generate_xml.py
+'''
+
+# Import modules
+import sys
+import os
+import math
+
+#does it represent an integer?
+def IsInt(s):
+    try: 
+        int(s)
+        return True
+    except ValueError:
+        return False
+
+#does it represent a float?
+def IsFloat(s):
+    try: 
+        float(s)
+        return True
+    except ValueError:
+        return False
+
+#make tree string into format for mugration
+def removeStates(treeString):
+        l=len(treeString)
+        newString=""
+        i=0
+        while i < l :
+                if treeString[i]=="[":
+                        while treeString[i]!="]":
+                                i+=1
+                        i+=1
+                newString+=treeString[i]
+                i+=1
+        return newString                
+
+#scale branch lengths 
+def scaleTreeString(treeString,scale):
+        l=len(treeString)
+        newString=""
+        i=0
+        while i < l :
+                if treeString[i]==":":
+                        newString+=":"
+                        numb=""
+                        i+=1
+                        while treeString[i]!="," and treeString[i]!=")" and treeString[i]!=";":
+                                numb+=treeString[i]
+                                i+=1
+                        newString+=str(float(numb)*scale)
+                newString+=treeString[i]
+                i+=1
+        return newString
+
+def generate_scotti_xml(fasta, output, dates, hosts, hostTimes, overwrite=False, maxHosts=None,  unlimLife=False, penalizeMigration=False, numIter=100000000, mutationModel='HKY', fixedAs=0, fixedCs=0, fixedGs=0, fixedTs=0):
+    """Generates an xml file that can be run in BEAST2.
+
+    Args:
+        fasta:          Input fasta alignment. If not specified, looks in the
+                        working directory for SCOTTI_aligment.fas
+        output:		output file that will contain the newly created SCOTTI
+                        xml to be run with BEAST2.
+        overwrite:      Overwrite output files.
+        dates:          Input csv file with sampling dates associated to each
+                        sample name (same name as fasta file). If not
+                        specified, looks for SCOTTI_dates.csv
+        hosts:		Input csv file with sampling hosts associated to each
+                        sample name (same name as fasta file). If not
+                        specified, looks for SCOTTI_hosts.csv
+        hostTime:       Input csv file with the earliest times in which hosts
+                        are infectable, and latest time when hosts are
+                        infective. Use same host names as those used for the
+                        sampling hosts file. If not specified, looks for
+                        SCOTTI_hostTimes.csv
+        maxHosts:       Maximum number of hosts (between sampled, non-sampled,
+                        and non-observed) allowed.
+        unlimLife:      span (unlimited contribution in time to the outbreak).
+                        By default non-sampled non-observed hosts have limited
+                        duration. Usually asymptomaic patients have limited
+                        contributions, while to model environmental
+                        contamination it's better to use unlimited hosts.
+                        Unlimited life span can also decrease computational
+                        demand.
+        penalizeMigration: penalize lineages that are still in a deme after deme
+                        closure. By default, don't (original version).
+        numIter:        Number of iterations of the MCMC. Default=100,000,000.
+        mutationModel:  String represnting the mutation model to be used.
+                        Possibilities are "HKY" or "JC". Further alternaties
+                        can be specified by modifying the output xml manually.
+        fixedAs:        Number of sites fixed for A in the genome and not
+                        included in the alignment.
+        fixedCs:        Number of sites fixed for C in the genome and not
+                        included in the alignment.
+        fixedGs:        Number of sites fixed for G in the genome and not
+                        included in the alignment.
+        fixedTs:        Number of sites fixed for T in the genome and not
+                        included in the alignment.
+
+    Output: 
+        XML file that can be used to run SCOTTI in BEAST2.
+    """
+    
+    # Check if input arguments make sense
+    if (not (os.path.isfile(fasta))):
+        print "Error: input fasta file not found, please specify it correctly in input using the option -f .\n"
+        return()
+    if (os.path.isfile(output + ".xml")) and (not overwrite):
+        print "Output xml file already exists, to allow overwriting of output file use option -ov, otherwise specify a different output file.\n"
+        return()
+    if not (os.path.isfile(dates)):
+        print "Error: input dates csv file not found, please specify it correctly in input using the option -d .\n"
+        return()
+    if not (os.path.isfile(hosts)):
+        print "Error: input hosts csv file not found, please specify it correctly in input using the option -s .\n"
+        return()
+    if not (os.path.isfile(hostTimes)):
+        print "Error: input hosts csv file not found, please specify it correctly in input using the option -i .\n"
+        return()
+    if (numIter<=0):
+        print "Error: the number of MCMC iterations specified is negative or null.\n"
+        return()
+    elif (numIter<=1000):
+        print "Very small number of MCMC iterations, might want to specifiy something bigger.\n"
+    if (mutationModel!="HKY" and mutationModel!="JC"):
+        print "Mutation model not supported. Please specify something among \"HKY\" or \"JC\".\n"
+        return()
+    if (fixedAs<0 or fixedCs<0 or fixedGs<0 or fixedTs<0):
+        print "Error: the number of fixed sites for each base type must be positive.\n"
+        return()
+
+    #read fasta file
+    sqFile=open(fasta)
+    seqs={}
+    line=sqFile.readline()
+    linesplit=line.split()
+    while line!="" and (len(linesplit)<1 or linesplit[0][0]!=">"):
+        line=sqFile.readline()
+        linesplit=line.split()
+    if line=="":
+        print "Wrong format in fasta file\n"
+        return()
+    while line!="":
+        if len(linesplit[0])>1:
+            name=linesplit[0].replace(">","")
+        else:
+            name=linesplit[1]
+        seq=""
+        line=sqFile.readline()
+        linesplit=line.split()
+        while line!="" and (len(linesplit)>0  and linesplit[0][0]!=">") and line!="\n":
+            seq+=linesplit[0]
+            line=sqFile.readline()
+            linesplit=line.split()
+        if name in seqs.keys():
+            print "Error, multiple entries with same name "+name+" in fasta file. Only first part of the name line is used for the name."
+            return()
+        seqs[name]=seq
+        while line!="" and  (len(linesplit)<1 or linesplit[0][0]!=">"):
+            line=sqFile.readline()
+            linesplit=line.split()
+    sqFile.close()
+
+    #read in file with host-sample information
+    hFile=open(hosts)
+    hosts={}
+    line=hFile.readline()
+    linesplit=("".join(line.split())).split(",")  
+    while line!="" and len(linesplit)>1: 
+        sam=linesplit[0]
+        hos=linesplit[1]
+        if not (sam in seqs.keys()):
+            print "Error: sample "+sam+" found in sample-host file but not found in the fasta file."
+            return()
+        if sam in hosts.keys():
+            print "Error, multiple entries with same name "+sam+" in hosts file. "
+            return()
+        hosts[sam]=hos
+        line=hFile.readline()
+        linesplit=("".join(line.split())).split(",")
+        while line=="\n":
+            line=hFile.readline()
+            linesplit=("".join(line.split())).split(",")
+    hFile.close()
+
+    #read in sampling dates
+    dFile=open(dates)
+    dates={}
+    line=dFile.readline()
+    linesplit=("".join(line.split())).split(",")  
+    while line!="" and len(linesplit)>1:  
+        sam=linesplit[0]
+        dat=linesplit[1]
+        if not (IsFloat(dat)):
+            print "Error: date "+str(dat)+" for sample "+sam+" cannot be converted to a number"
+            return()
+        if not (sam in seqs.keys()):
+            print "Error: sample "+sam+" found in sampling dates file but not found in the fasta file."
+            return()
+        if sam in dates.keys():
+            print "Error, multiple entries with same name "+sam+" in dates file. "
+            return()
+        dates[sam]=float(dat)
+        line=dFile.readline()
+        linesplit=("".join(line.split())).split(",")
+        while line=="\n":
+            line=dFile.readline()
+            linesplit=("".join(line.split())).split(",")
+    dFile.close()
+        
+    #read in file with host times information
+    hFile=open(hostTimes)
+    hostT={}
+    line=hFile.readline()
+    linesplit=("".join(line.split())).split(",")  
+    while line!="" and len(linesplit)>2: 
+        hos=linesplit[0]
+        dat=float(linesplit[1])
+        dat2=float(linesplit[2])
+        if (not (IsFloat(dat))) or (not (IsFloat(dat2))):
+            print "Error: date "+str(dat)+" or "+str(dat2)+" for sample "+sam+" cannot be converted to a number"
+            return()
+        #if not (hos in seqs.keys()):
+        #    print "Error: sample "+hos+" found in host times file but not found in the fasta file."
+        #    return()
+        if hos in hostT.keys():
+            print "Error, multiple entries with same name "+hos+" in hosts-times file. "
+            return()
+        hostT[hos]=[dat, dat2]
+        line=hFile.readline()
+        linesplit=("".join(line.split())).split(",")
+        while line=="\n":
+            line=hFile.readline()
+            linesplit=("".join(line.split())).split(",")
+    hFile.close()
+
+    #check that the details are correct
+    for s in seqs.keys():
+        if not (s in dates.keys()):
+            print "Error, no date specified for sample "+s
+            return()
+        if not (s in hosts.keys()):
+            print "Error, no host specified for sample "+s
+            return()
+    for s in hosts.keys():
+        if not (s in seqs.keys()):
+            print "Error, no sequence specified for sample "+s
+            return()
+        if not (s in dates.keys()):
+            print "Error, no date specified for sample "+s
+            return()
+    for s in dates.keys():
+        if not (s in seqs.keys()):
+            print "Error, no sequence specified for sample "+s
+            return()
+        if not (s in hosts.keys()):
+            print "Error, no host specified for sample "+s
+            return()
+    for s in hosts.keys():
+        if not (hosts[s] in hostT.keys()):
+            print "Warning: no dates specified for host "+hosts[s]+". I will assume this host is available throghtout"
+            hostT[hosts[s]]=[-1000000000000,1000000000000]
+
+
+    for h in hostT.keys():
+        found=0
+        for s in hosts.keys():
+            if hosts[s]==h:
+                found=1
+        if found==0:
+            print "Warning: no sample specified for host "+h+". I will assume this host could have been infected or not. If you are sure this host was infected, then please include a non-informative sample (all \"N\"s) from it."
+
+    xml=open(output+".xml","w")
+    xml.write("<beast version=\'2.0\' namespace=\'beast.evolution.alignment:beast.core:beast.core.parameter:beast.evolution.tree:beast.evolution.tree.coalescent:beast.core.util:beast.evolution.operators:beast.evolution.sitemodel:beast.evolution.substitutionmodel:beast.evolution.likelihood:beast.evolution.tree:beast.math.distributions:multitypetreeVolz.distributions:multitypetreeVolz.operators:multitypetreeVolz.util\'>\n\n"+"  <data id=\"alignmentVar\" dataType=\"nucleotide\">\n")
+    for s in seqs.keys():
+        xml.write("    <sequence taxon=\'"+s+"\' value=\'"+seqs[s]+"\'/>\n")
+    xml.write("  </data>\n")
+    st=""
+    if fixedAs>0 and fixedCs>0 and fixedGs>0 and fixedTs>0:
+        st="constantSiteWeights=\'"+str(fixedAs)+" "+str(fixedCs)+" "+str(fixedGs)+" "+str(fixedTs)+"\'"
+    xml.write("  <data id=\'alignment\' spec=\'FilteredAlignment\' filter=\'-\' data=\'@alignmentVar\' "+st+"/>\n\n")
+
+    #hosts
+    xml.write("   <typeTraitSet spec=\'TraitSetWithLimits\' id=\'typeTraitSet\' traitname=\"type\" value=\"")
+    hL=hosts.keys()
+    for s in hL:
+        xml.write(s+"="+hosts[s])
+        if s!=hL[len(hL)-1]:
+            xml.write(",")
+    xml.write("\">\n     <taxa spec=\'TaxonSet\' alignment=\'@alignment\'/>\n   </typeTraitSet>\n\n")
+
+    #dates
+    xml.write("   <timeTraitSet spec=\'TraitSetWithLimits\' id=\'timeTraitSet\' traitname=\"date-forward\" value=\"")
+    dL=dates.keys()
+    for s in range(len(dL)):
+        xml.write(dL[s]+"="+str(dates[dL[s]]))
+        if (s!=(len(dL)-1)):
+            xml.write(",")
+    xml.write("\">\n     <taxa spec=\'TaxonSet\' alignment=\'@alignment\'/>\n   </timeTraitSet>\n\n")
+
+    #end infectability
+    xml.write("   <endInfectionsTraitSet spec=\'InfectionTraitSet\' id=\'endInfectionsTraitSet\' type=\'end\' traitname=\"date-forward\" value=\"")
+    dL=hostT.keys()
+    for s in dL:
+        xml.write(s+"="+str(hostT[s][1]))
+        if s!=dL[len(dL)-1]:
+            xml.write(",")
+    xml.write("\">\n          <taxaDates idref=\'timeTraitSet\'/>\n           <taxaTypes idref=\'typeTraitSet\'/>\n   </endInfectionsTraitSet>\n\n")
+
+    #start infectability
+    xml.write("   <startInfectionsTraitSet spec=\'InfectionTraitSet\' id=\'startInfectionsTraitSet\' type=\'start\' traitname=\"date-forward\" value=\"")
+    dL=hostT.keys()
+    for s in dL:
+        xml.write(s+"="+str(hostT[s][0]))
+        if s!=dL[len(dL)-1]:
+            xml.write(",")
+    xml.write("\">\n          <taxaDates idref=\'timeTraitSet\'/>\n           <taxaTypes idref=\'typeTraitSet\'/>\n   </startInfectionsTraitSet>\n\n")
+
+    #check that times fit
+    for s in seqs.keys():
+        h=hosts[s]
+        if dates[s]<=hostT[hosts[s]][0]:
+            print "Error, sampling time "+str(dates[s])+" of sample "+s+" must be after start of infectability time "+str(hostT[hosts[s]][0])+" of host "+h
+            return()
+        if dates[s]>=hostT[hosts[s]][1]:
+            print "Error, sampling time "+str(dates[s])+" of sample "+s+" must be before end of infectiousness time "+str(hostT[hosts[s]][0])+" of host "+h
+            return()
+
+    #include mutation model
+    xml.write("   <siteModel spec=\"SiteModel\" id=\"siteModel\">\n     <mutationRate spec=\'RealParameter\' id=\"mutationRate\" value=\"1.0\"/>\n     <substModel spec=\"")
+    if mutationModel=="HKY":
+       xml.write("HKY\">\n       <kappa spec=\'RealParameter\' id=\"hky.kappa\" value=\"1.0\"/>\n")
+    elif mutationModel=="JC":
+       xml.write("jc69\">\n")
+    elif mutationModel=="GTR":
+       xml.write("GTR\" rateAC=\"@rateAC\" rateAG=\"@rateAG\" rateAT=\"@rateAT\" rateCG=\"@rateCG\" rateGT=\"@rateGT\" >\n       <parameter estimate=\'false\' name=\"rateCT\" lower=\"0.0\" value=\"1.0\" id=\"rateCTfixed\"/>\n")
+    else:
+        print "Error, mutation model "+mutationModel+" not recognized."
+        return()
+    xml.write("       <frequencies estimate=\"false\" spec=\'Frequencies\'>\n        <frequencies spec=\'RealParameter\' id=\""+mutationModel+".freq\" value=\"0.25 0.25 0.25 0.25\"/>\n       </frequencies>\n     </substModel>\n   </siteModel>\n\n")
+                  
+    #migration model
+    nHos=len(hostT.keys())
+    meanL=0.0
+    for h in hostT.keys():
+        meanL+=hostT[h][1]-hostT[h][0]
+    meanL=meanL/len(hostT.keys())
+    ma=maxHosts
+    if ma<nHos:
+        ma=nHos+5
+        print "Maximum number of hosts specified is less than the minimum number of hosts. Setting it to "+str(ma)
+    if ma<(nHos+2):
+        print "Warning: the maximum number of hosts is less than "+str(nHos+2)+". If there are problems in the execution of the BEAST2 analysis, try a higher values of maxHosts."
+    start=(nHos+ma)/2
+    if start<(nHos+2) and ma>=(nHos+2):
+        start=nHos+2
+    xml.write("   <migrationModelUniform spec=\'MigrationModelUniform\' id=\'migModel\' minDemes=\'"+str(nHos)+"\'>\n     <rate spec=\'RealParameter\' value=\""+str(1.0/(meanL*nHos))+"\" dimension=\"1\" id=\"rate\"/>\n     <popSize spec=\'RealParameter\' value=\"1.0\" dimension=\"1\" id=\"popSize\"/>\n     <numDemes spec=\'IntegerParameter\' value=\""+str(start)+"\" dimension=\"1\" id=\"numDemes\" lower=\'"+str(nHos)+"\' upper=\'"+str(ma)+"\'/>\n     <trait idref=\'startInfectionsTraitSet\' type=\'start\'/>\n     <trait idref=\'endInfectionsTraitSet\' type=\'end\'/>\n   </migrationModelUniform>\n\n")            
+
+    #priors
+    xml.write("   <input spec=\'CompoundDistribution\' id=\'parameterPriors\'>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@mutationRate\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"-3.0\" S=\"6.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@popSize\">\n       <distr spec=\"LogNormalDistributionModel\"  M=\"0.0\" S=\"6.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rate\">\n       <distr spec=\'LogNormalDistributionModel\' M=\""+str(math.log(1.0/(meanL*nHos)))+"\" S=\"0.2\"/>\n     </distribution>\n")
+    if mutationModel=="HKY":
+        xml.write("     <distribution spec=\'beast.math.distributions.Prior\' x=\"@hky.kappa\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n   </input>\n\n")
+    elif mutationModel=="JC":
+       xml.write("   </input>\n\n")
+    elif mutationModel=="GTR":
+        xml.write("     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateAC\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateAG\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateAT\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateCG\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n     <distribution spec=\'beast.math.distributions.Prior\' x=\"@rateGT\">\n       <distr spec=\'LogNormalDistributionModel\' M=\"0.0\" S=\"4.0\"/>\n     </distribution>\n   </input>\n\n")      
+                  
+    #Likelihoods
+    xml.write("<input spec=\'TreeLikelihood\' id=\"treeLikelihood\">\n     <data idref=\"alignment\"/>\n     <tree idref=\"tree\"/>\n     <siteModel idref=\'siteModel\'/>\n   </input>\n\n   <input spec=\'StructuredCoalescentTreeDensityNew\' id=\'treePrior\' limitedLifespan=\'"+str(unlimLife)+"\' penalizeMigration=\'"+str(penalizeMigration)+"\'>\n     <multiTypeTreeConcise idref=\"tree\"/>\n     <migrationModelUniform idref=\"migModel\"/>\n   </input>\n\n   <run spec=\"MCMC\" id=\"mcmc\" chainLength=\""+str(numIter)+"\" storeEvery=\"10000\">\n\n")
+
+    #migration model
+    xml.write("     <init spec=\'StructuredCoalescentMultiTypeTreeConcise\' id=\'tree\' nTypes=\""+str(start)+"\">\n         <migrationModelUniform spec=\'MigrationModelUniform\' minDemes=\'"+str(nHos)+"\'>\n             <rate spec=\'RealParameter\' value=\""+str(1.0/(meanL*nHos))+"\" dimension=\"1\"/>\n             <popSize spec=\'RealParameter\' value=\"1.0\" dimension=\"1\"/>\n             <numDemes spec=\'IntegerParameter\' value=\""+str(start)+"\" dimension=\"1\"/>\n         </migrationModelUniform>\n         <trait idref=\'typeTraitSet\'/>\n         <trait idref=\'timeTraitSet\'/>\n     </init>\n\n")    
+
+    #state nodes              
+    xml.write("     <state>\n       <stateNode idref=\"tree\"/>\n       <stateNode idref=\"rate\"/>\n       <stateNode idref=\"popSize\"/>\n       <stateNode idref=\"numDemes\"/>\n       <stateNode idref=\"mutationRate\"/>\n")
+    if mutationModel=="HKY":
+        xml.write("       <stateNode idref=\"hky.kappa\"/>\n")
+    elif mutationModel=="GTR":              
+        xml.write("       <stateNode idref=\"rateAC\"/>\n       <stateNode idref=\"rateAG\"/>\n       <stateNode idref=\"rateAT\"/>\n       <stateNode idref=\"rateCG\"/>\n       <stateNode idref=\"rateGT\"/>\n")
+    xml.write("       <stateNode idref=\""+mutationModel+".freq\"/>\n     </state>\n\n")     
+                  
+    #compound distribution
+    xml.write("     <distribution spec=\'CompoundDistribution\' id=\'posterior\'>\n       <distribution idref=\"treeLikelihood\"/>\n       <distribution idref=\'treePrior\'/>\n       <distribution idref=\"parameterPriors\"/>\n     </distribution>\n\n")
+                  
+    #operators
+    xml.write("     <operator spec=\'ScaleOperator\' id=\'RateScaler\' parameter=\"@rate\" scaleFactor=\"0.8\" weight=\"1\"/>\n     <operator spec=\"ScaleOperator\" id=\"PopSizeScaler\" parameter=\"@popSize\" scaleFactor=\"0.8\" weight=\"1\"/>\n     <operator spec=\"ShortRangeUniformOperator\" id=\"NumDemesScaler\" parameter=\"@numDemes\" weight=\"1\"/>\n     <operator spec=\"ScaleOperator\" id=\"muRateScaler\" parameter=\"@mutationRate\" scaleFactor=\"0.8\" weight=\"1\"/>\n     <operator spec=\"DeltaExchangeOperator\" id=\"freqExchanger\" parameter=\"@"+mutationModel+".freq\" delta=\"0.01\" weight=\"0.1\"/>\n     <operator id=\'treeScaler.t\' spec=\'ScaleOperator\' scaleFactor=\"0.5\" weight=\"3\" tree=\"@tree\"/>\n     <operator id=\'treeRootScaler.t\' spec=\'ScaleOperator\' scaleFactor=\"0.5\" weight=\"3\" tree=\"@tree\" rootOnly=\'true\'/>\n     <operator id=\'UniformOperator.t\' spec=\'Uniform\' weight=\"30\" tree=\"@tree\"/>\n     <operator id=\'SubtreeSlide.t\' spec=\'SubtreeSlide\' weight=\"15\" gaussian=\"true\" size=\"1.0\" tree=\"@tree\"/>\n     <operator id=\'narrow.t\' spec=\'Exchange\' isNarrow=\'true\' weight=\"15\" tree=\"@tree\"/>\n     <operator id=\'wide.t\' spec=\'Exchange\' isNarrow=\'false\' weight=\"3\" tree=\"@tree\"/>\n     <operator id=\'WilsonBalding.t\' spec=\'WilsonBalding\' weight=\"3\" tree=\"@tree\"/>\n")
+    if mutationModel=="HKY":
+        xml.write("     <operator spec=\'ScaleOperator\' id=\'kappaScaler\' parameter=\"@hky.kappa\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n")
+    elif mutationModel=="GTR": 
+        xml.write("     <operator spec=\'ScaleOperator\' id=\'ACScaler\' parameter=\"@rateAC\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'AGScaler\' parameter=\"@rateAG\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'ATScaler\' parameter=\"@rateAT\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'CGScaler\' parameter=\"@rateCG\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n     <operator spec=\'ScaleOperator\' id=\'GTScaler\' parameter=\"@rateGT\" scaleFactor=\"0.8\" weight=\"0.1\"/>\n")
+    xml.write("\n")
+
+    #loggers
+    xml.write("     <logger logEvery=\""+str(numIter/10000)+"\" fileName=\""+output+".log\">\n       <model idref=\'posterior\'/>\n       <log idref=\"posterior\"/>       <log idref=\"treeLikelihood\"/>\n       <log idref=\"migModel\"/>\n       <log idref=\"mutationRate\"/>\n       <log spec=\'TreeHeightLogger\' tree=\'@tree\'/>\n       <log idref=\""+mutationModel+".freq\"/>\n")
+    if mutationModel=="HKY":
+        xml.write("       <log idref=\"hky.kappa\"/>\n")
+    elif mutationModel=="GTR":               
+        xml.write("       <log idref=\"rateAC\"/>\n       <log idref=\"rateAG\"/>\n       <log idref=\"rateAT\"/>\n       <log idref=\"rateCG\"/>\n       <log idref=\"rateGT\"/>\n")
+    xml.write("     </logger>\n")
+         
+    xml.write("     <logger logEvery=\""+str(numIter/5000)+"\" fileName=\""+output+".trees\" mode=\"tree\">\n       <log idref=\'treePrior\'/>\n     </logger>\n")
+
+    xml.write("     <logger logEvery=\""+str(numIter/1000)+"\">\n       <model idref=\'posterior\'/>\n       <log idref=\"posterior\"/>\n       <log idref=\"treeLikelihood\"/>\n       <log spec=\'TreeHeightLogger\' tree=\'@tree\'/>\n       <log idref=\"migModel\"/>\n       <log idref=\"mutationRate\"/>\n       <ESS spec=\'ESS\' name=\'log\' arg=\"@treePrior\"/>\n       <ESS spec=\'ESS\' name=\'log\' arg=\"@posterior\"/>\n     </logger>\n\n   </run>\n </beast>\n\n")
+
+    return()
+
diff --git a/modules/beast/scotti_functions.pyc b/modules/beast/scotti_functions.pyc
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HcmV?d00001

diff --git a/modules/beast/test.csv b/modules/beast/test.csv
new file mode 100644
index 0000000..9e65848
--- /dev/null
+++ b/modules/beast/test.csv
@@ -0,0 +1,2 @@
+a,b,c
+d,e,f,g
diff --git a/modules/beast/testfn.py b/modules/beast/testfn.py
new file mode 100644
index 0000000..4933ee2
--- /dev/null
+++ b/modules/beast/testfn.py
@@ -0,0 +1,17 @@
+a=['b','1','a','2']
+no_numbers = True
+for i in a:
+    try:
+        int(i)
+        no_numbers = False
+        break
+    except:
+        continue
+
+from scotti_functions import *
+
+test = add_prefixes('one_facil_subtree_hosts.csv', 'one_facil_subtree_dates.csv', 'one_facil_subtree_hostTimes.csv')
+
+#test = add_prefixes('one_facil_subtree_hosts.renamed.csv', 'one_facil_subtree_dates.renamed.csv', 'one_facil_subtree_hostTimes.renamed.csv')
+
+print(test[0])
diff --git a/modules/cluster_based_prep/log_modules.py b/modules/cluster_based_prep/log_modules.py
deleted file mode 100644
index 9289934..0000000
--- a/modules/cluster_based_prep/log_modules.py
+++ /dev/null
@@ -1,30 +0,0 @@
-__author__ = 'alipirani'
-
-import logging
-from datetime import datetime
-
-def keep_logging(pmessage, lmessage, logger, mode):
-    print(pmessage)
-    if mode == 'warning':
-        logger.warning(lmessage)
-    elif mode == 'info':
-        logger.info(lmessage)
-    elif mode == 'exception':
-        logger.exception(lmessage)
-    elif mode == 'debug':
-        logger.debug(lmessage)
-    else:
-        logger.error(lmessage)
-
-
-def generate_logger(output_folder, analysis_name, log_unique_time):
-    # Create a new logger; create a file handler; create a logging format; add the handlers to the logger
-    logger = logging.getLogger(__name__)
-    logger.setLevel(logging.DEBUG)
-    #handler = logging.FileHandler('{}/{}_{}_.log.txt'.format(args.output_folder, args.analysis_name, datetime.now().strftime('%Y_%m_%d_%H_%M_%S')))
-    handler = logging.FileHandler('{}/{}_{}.log.txt'.format(output_folder, log_unique_time, analysis_name))
-    handler.setLevel(logging.DEBUG)
-    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-    handler.setFormatter(formatter)
-    logger.addHandler(handler)
-    return logger
\ No newline at end of file
diff --git a/modules/cluster_based_prep/logging_subprocess.py b/modules/cluster_based_prep/logging_subprocess.py
deleted file mode 100644
index 4aee619..0000000
--- a/modules/cluster_based_prep/logging_subprocess.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# https://gist.github.com/hangtwenty/6390750
-import sys
-if sys.version_info < (3, 2):
-    import subprocess32 as sp
-else:
-    import subprocess as sp
-import select
-import logging
-from logging import DEBUG, ERROR
-
-def call(popenargs, logger, stderr_log_level=logging.ERROR, stdout_log_level=logging.DEBUG, **kwargs):
-    """
-    Variant of subprocess.call that accepts a logger instead of stdout/stderr,
-    and logs stdout messages via logger.debug and stderr messages via
-    logger.error.
-    """
-
-    try:
-
-        child = sp.Popen(popenargs, stdout=sp.PIPE, stderr=sp.PIPE, shell=True, **kwargs)
-
-        log_level = {child.stdout: stdout_log_level, child.stderr: stderr_log_level}
-        #print log_level
-        def check_io():
-            ready_to_read = select.select([child.stdout, child.stderr], [], [], 1000)[0]
-            #print ready_to_read
-            for io in ready_to_read:
-                line = io.readline()
-                #print line
-                if line:
-                    #print log_level[io]
-                    logger.log(log_level[io], line[:-1])
-
-        # keep checking stdout/stderr until the child exits
-        while child.poll() is None:
-            check_io()
-        check_io()  # check again to catch anything after the process exits
-    except Exception as e:
-        raise(e)
-
-    return child.wait()
\ No newline at end of file
diff --git a/modules/cluster_based_prep/prep_cluster_calling.py b/modules/cluster_based_prep/prep_cluster_calling.py
deleted file mode 100644
index 8fc34b0..0000000
--- a/modules/cluster_based_prep/prep_cluster_calling.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import sys
-import os
-from os import path
-sys.path.append(path.dirname(path.dirname(path.dirname(path.abspath(__file__)))))
-import argparse
-import errno
-import glob
-import re
-import multiprocessing
-import subprocess as subprocess
-from datetime import datetime
-from joblib import Parallel, delayed
-from modules.logging_subprocess import *
-from modules.log_modules import *
-from argparse import RawTextHelpFormatter
-
-# Command Line Argument Parsing
-def parser():
-    parser = argparse.ArgumentParser(description='\nPrepare Cluster based Reference genome and config files\n', formatter_class=RawTextHelpFormatter)
-    required = parser.add_argument_group('Required arguments')
-    optional = parser.add_argument_group('Optional arguments')
-    required.add_argument('-cluster_names', action='store', dest="cluster_names", help='Provide a file describing cluster group. Each line represents a cluster and each sample name in a cluster is seperated by a tab.', required=True)
-    required.add_argument('-report', action='store', dest="report", help='comma-seperated Assembly report', required=True)
-    required.add_argument('-assemblies', action='store', dest="assemblies", help='Path to final assembly fasta files', required=True)
-    required.add_argument('-config', action='store', dest="index", help='path to config file', required=True)
-    required.add_argument('-reads_dir', action='store', dest="reads_dir", help='path to reads directory', required=True)
-    return parser
-
-
-
-
-def prep_cluster_names():
-    report_dict_contigs = {}
-    report_dict_length = {}
-    os.system("mkdir filename_meta")
-    os.system("mkdir filename_ls_commands")
-    with open(args.report) as fp:
-        for line in fp:
-            line = line.strip()
-            line_split = line.split(',')
-            first_part = re.sub("_S.*", "", line_split[0])
-            report_dict_contigs[first_part] = line_split[1]
-            report_dict_length[first_part] = line_split[2]
-    print "preparing cluster group reference genomes"
-    cluster_groups = {}
-    count = 0
-    with open(args.cluster_names) as fp:
-        for line in fp:
-            count = count + 1
-            line = line.strip()
-            line_split = line.split('\t')
-            cluster_groups[count] = line_split
-        fp.close()
-    f = open("cluster_reference_genome.sh", 'w+')
-    f_temp = open("cluster_temp.txt", 'w+')
-    for key in cluster_groups.keys():
-        cluster_no = key
-        cluster_dir = "cluster_" + str(key) + "_size_" + str(len(cluster_groups[key]))
-        os.system("mkdir %s" % cluster_dir)
-        cluster_file = "cluster_" + str(key) + "_size_" + str(len(cluster_groups[key])) + "_meta.txt"
-        cluster_file_names = "cluster_" + str(key) + "_size_" + str(len(cluster_groups[key])) + "_filenames.txt"
-        f1 = open("%s/%s" % (cluster_dir, cluster_file), 'w+')
-        f2 = open("%s/%s" % (cluster_dir, cluster_file_names), 'w+')
-        for i in cluster_groups[key]:
-            if i in report_dict_length.keys():
-                print_string = "cluster_" + str(key) + "_size_" + str(len(cluster_groups[key])) + ",%s,%s,%s" % (i, report_dict_contigs[i], report_dict_length[i])
-                f1.write(print_string + '\n')
-                cmd = "basename %s/%s_*R1.fastq.gz\n" % (args.reads_dir, i)
-                proc = subprocess.Popen([cmd], stdout=subprocess.PIPE, shell=True)
-                (out, err) = proc.communicate()
-                f2.write("%s" % (out))
-            else:
-                print "%s: Not found" % i
-        get_best_ref_cmd = "sort -t',' -k3n %s/%s | head -n1 | cut -d',' -f2\n" % (cluster_dir, cluster_file)
-        f.write(get_best_ref_cmd)
-        f_temp.write("cluster_" + str(key) + '\n')
-
-
-
-
-
-
-
-
-
-
-# Start of Main Method/Pipeline
-if __name__ == '__main__':
-    # Set up logging modules and config file
-    start_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-    start_time_2 = datetime.now()
-    args = parser().parse_args()
-    global log_unique_time
-    log_unique_time = datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
-    global logger
-    if args.cluster_names:
-        prep_cluster_names()
\ No newline at end of file
diff --git a/modules/core_prep_sanity_checks.py b/modules/core_prep_sanity_checks.py
index 8bc2231..d8685b0 100644
--- a/modules/core_prep_sanity_checks.py
+++ b/modules/core_prep_sanity_checks.py
@@ -45,12 +45,36 @@ def make_sure_files_exists(vcf_file_array, Config, logger):
         ori_indel_file = files.replace("filter2_final.vcf_no_proximate_snp.vcf",
                                        "filter2_indel_final.vcf")
 
-        if os.path.isfile(ori_unmapped_file) and os.path.isfile(ori_proximate_file) and os.path.isfile(
-                ori_variant_position_file) and os.path.isfile(ori_5bp_mpileup_file) and os.path.isfile(
-                ori_mpileup_file) and os.path.isfile(ori_filter_file) and os.path.isfile(ori_indel_file):
-            continue
+        # if os.path.isfile(ori_unmapped_file) and os.path.isfile(ori_proximate_file) and os.path.isfile(
+        #         ori_variant_position_file) and os.path.isfile(ori_5bp_mpileup_file) and os.path.isfile(
+        #         ori_mpileup_file) and os.path.isfile(ori_filter_file) and os.path.isfile(ori_indel_file):
+        #     continue
+        # else:
+        #     not_found_files.append(files)
+
+        if not os.path.isfile(ori_unmapped_file):
+            not_found_files.append(ori_unmapped_file)
+
+        elif not os.path.isfile(ori_proximate_file):
+            not_found_files.append(ori_proximate_file)
+
+        elif not os.path.isfile(ori_variant_position_file):
+            not_found_files.append(ori_variant_position_file)
+
+        elif not os.path.isfile(ori_5bp_mpileup_file):
+            not_found_files.append(ori_5bp_mpileup_file)
+
+        elif not os.path.isfile(ori_mpileup_file):
+            not_found_files.append(ori_mpileup_file)
+
+        elif not os.path.isfile(ori_filter_file):
+            not_found_files.append(ori_filter_file)
+
+        elif not os.path.isfile(ori_indel_file):
+            not_found_files.append(ori_indel_file)
         else:
-            not_found_files.append(files)
+            continue
+
     if len(not_found_files) > 0:
         for i in not_found_files:
             keep_logging('Error finding variant calling output files for: %s' % os.path.basename(i.replace('_filter2_final.vcf_no_proximate_snp.vcf', '')),
diff --git a/modules/core_prep_sanity_checks.pyc b/modules/core_prep_sanity_checks.pyc
index 706b34cfec31038e23f4caf3e323ae5bb0cfb6e9..f3887976bda0c6035c8ebac0126f7ebb50751414 100644
GIT binary patch
delta 382
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zhLxe2k%6(0m!X7_A&ZG2i<zN@4J1^26{Z5rMODELR`DIC0?b8K!2wnw$q2Ot%tffE
z2U)`jR$>iP0_Gx=fK+gSRm8$nfVrqDxWOuTVGaRvK`J09yo41b1N2P_2SYXsL(xCR
zib#;iWF@9x#`&AenD|&YJs5z1jg66a@<cWXWkya$PEH|40VZ6SooVwHw$IFrDU(-l
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delta 259
zcmca4e@~8``7<w9W8waZ>^FrMF)}cuGceRLGgQbj)-W)*#EMS*Jh8q=l(B@7A&ZG2
zi<zN@jiH&50Y!uzLxclEgcC!A3qyn(DpJVH07Ny+3|TA;H7pFx5{wLtK!NZQR*)dj
z_7o0=Y!-&1d6TP|q8RNrUt{89;WS|Y0yZ{A-pL=?B={LQ89AAB8C4i{8QGWw88>UN
de`aR%nJmM7pV4>oAMOSwMw`vGd?AdC$^a(GGg<%u

diff --git a/modules/gatk.py b/modules/gatk.py
index f42b47a..6b41685 100644
--- a/modules/gatk.py
+++ b/modules/gatk.py
@@ -7,127 +7,211 @@
 from modules.logging_subprocess import *
 from sys import platform as _platform
 
-def gatk_filter2(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp):
-    base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
-    filter_criteria = ConfigSectionMap("SNP_filters", Config)['filter_criteria']
-    if ConfigSectionMap(filter_criteria, Config)['avg_depth'] == "yes":
-        keep_logging('The average depth filter is turned on.', 'The average depth filter is turned on.', logger, 'info')
-        low_Dp = float(Avg_dp) / 2
-        high_Dp = float(Avg_dp) * 5
-        DP_filter = "DP > %s && DP < %s" % (int(low_Dp), int(high_Dp))
-    else:
-        DP_filter = "DP > %s" % ConfigSectionMap(filter_criteria, Config)['dp']
-    MQ_filter = "MQ > %s" % ConfigSectionMap(filter_criteria, Config)['mq']
-    FQ_filter = "FQ < %s" % ConfigSectionMap(filter_criteria, Config)['fq']
-    FQ_filter2 = "FQ < %s" % ConfigSectionMap(filter_criteria, Config)['fq2']
-    QUAL_filter = "QUAL > %s" % ConfigSectionMap(filter_criteria, Config)['qual']
-    AF_filter = "AF1 > %s" % float(ConfigSectionMap(filter_criteria, Config)['af'])
-    gatk_filter2_parameter_expression = "%s && %s && %s && %s && %s && %s" % (FQ_filter, MQ_filter, QUAL_filter, DP_filter, FQ_filter2, AF_filter)
-    if os.path.exists(final_raw_vcf):
-        gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_gatk.vcf --variant %s --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
-    else:
-        gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_gatk.vcf --variant %s.gz --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
-    filter_flag_command = "grep '#\|PASS_filter2' %s/%s_filter2_gatk.vcf > %s/%s_filter2_final.vcf" % (out_path, analysis, out_path, analysis)
-    keep_logging(gatk_filter2_command, gatk_filter2_command, logger, 'debug')
-    keep_logging(filter_flag_command, filter_flag_command, logger, 'debug')
-    try:
-        call(gatk_filter2_command, logger)
-        call(filter_flag_command, logger)
-    except sp.CalledProcessError:
-        keep_logging('Error in GATK filter step. Exiting.', 'Error in GATK filter step. Exiting.', logger, 'exception')
-        sys.exit(1)
-    gatk_filter2_final_vcf = "%s/%s_filter2_final.vcf" % (out_path, analysis)
-    return gatk_filter2_final_vcf
+def gatk_filter(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp):
+    if ConfigSectionMap("pipeline", Config)['variant_caller'] == "samtools":
+        base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+        filter_criteria = ConfigSectionMap("SNP_filters", Config)['filter_criteria']
+        print "Using variant filter parameters from: %s" % filter_criteria
+        if ConfigSectionMap(filter_criteria, Config)['avg_depth'] == "yes":
+            keep_logging('The average depth filter is turned on.', 'The average depth filter is turned on.', logger, 'info')
+            low_Dp = float(Avg_dp) / 2
+            high_Dp = float(Avg_dp) * 5
+            DP_filter = "DP > %s && DP < %s" % (int(low_Dp), int(high_Dp))
+        else:
+            DP_filter = "DP > %s" % ConfigSectionMap(filter_criteria, Config)['dp']
+        MQ_filter = "MQ > %s" % ConfigSectionMap(filter_criteria, Config)['mq']
+        FQ_filter = "FQ < %s" % ConfigSectionMap(filter_criteria, Config)['fq']
+        FQ_filter2 = "FQ < %s" % ConfigSectionMap(filter_criteria, Config)['fq2']
+        QUAL_filter = "QUAL > %s" % ConfigSectionMap(filter_criteria, Config)['qual']
+        AF_filter = "AF1 > %s" % float(ConfigSectionMap(filter_criteria, Config)['af'])
+        gatk_filter2_parameter_expression = "%s && %s && %s && %s && %s && %s" % (FQ_filter, MQ_filter, QUAL_filter, DP_filter, FQ_filter2, AF_filter)
+        if os.path.exists(final_raw_vcf):
+            gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_gatk.vcf --variant %s --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+        else:
+            gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_gatk.vcf --variant %s.gz --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+        filter_flag_command = "grep '#\|PASS_filter2' %s/%s_filter2_gatk.vcf > %s/%s_filter2_final.vcf" % (out_path, analysis, out_path, analysis)
+        keep_logging(gatk_filter2_command, gatk_filter2_command, logger, 'debug')
+        keep_logging(filter_flag_command, filter_flag_command, logger, 'debug')
+        try:
+            call(gatk_filter2_command, logger)
+            call(filter_flag_command, logger)
+        except sp.CalledProcessError:
+            keep_logging('Error in GATK filter step. Exiting.', 'Error in GATK filter step. Exiting.', logger, 'exception')
+            sys.exit(1)
+        gatk_filter2_final_vcf = "%s/%s_filter2_final.vcf" % (out_path, analysis)
+        return gatk_filter2_final_vcf
+    elif ConfigSectionMap("pipeline", Config)['variant_caller'] == "gatkhaplotypecaller":
+        base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)[
+            'gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+        filter_criteria = ConfigSectionMap("SNP_filters", Config)['filter_criteria']
+        keep_logging("Using variant filter parameters from: %s" % filter_criteria, "Using variant filter parameters from: %s" % filter_criteria, logger, 'info')
+        if ConfigSectionMap(filter_criteria, Config)['avg_depth'] == "yes":
+            keep_logging('The average depth filter is turned on.', 'The average depth filter is turned on.', logger,
+                         'info')
+            low_Dp = float(Avg_dp) / 2
+            high_Dp = float(Avg_dp) * 5
+            DP_filter = "DP > %s && DP < %s" % (int(low_Dp), int(high_Dp))
+        else:
+            DP_filter = "DP > %s" % ConfigSectionMap(filter_criteria, Config)['dp']
+        MQ_filter = "MQ > %s" % ConfigSectionMap(filter_criteria, Config)['mq']
+        QUAL_filter = "QD > %s" % ConfigSectionMap(filter_criteria, Config)['qual']
+        AF_filter = "AF > %s" % float(ConfigSectionMap(filter_criteria, Config)['af'])
+        gatk_filter2_parameter_expression = "%s && %s && %s && %s" % (
+        MQ_filter, QUAL_filter, DP_filter, AF_filter)
+        if os.path.exists(final_raw_vcf):
+            gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_gatk.vcf --variant %s --filterExpression \"%s\" --filterName PASS_filter2" % (
+            base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+        else:
+            gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_gatk.vcf --variant %s.gz --filterExpression \"%s\" --filterName PASS_filter2" % (
+            base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+        filter_flag_command = "grep '#\|PASS_filter2' %s/%s_filter2_gatk.vcf > %s/%s_filter2_final.vcf" % (
+        out_path, analysis, out_path, analysis)
+        keep_logging(gatk_filter2_command, gatk_filter2_command, logger, 'debug')
+        keep_logging(filter_flag_command, filter_flag_command, logger, 'debug')
+        try:
+            call(gatk_filter2_command, logger)
+            call(filter_flag_command, logger)
+        except sp.CalledProcessError:
+            keep_logging('Error in GATK filter step. Exiting.', 'Error in GATK filter step. Exiting.', logger,
+                         'exception')
+            sys.exit(1)
+        gatk_filter2_final_vcf = "%s/%s_filter2_final.vcf" % (out_path, analysis)
+        return gatk_filter2_final_vcf
 
-def gatk_filter2_contamination(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp):
-    base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
-    filter_criteria = "contamination_filters"
+def gatk_filter_contamination(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp):
+    if ConfigSectionMap("pipeline", Config)['variant_caller'] == "samtools":
+        base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+        filter_criteria = "contamination_filters"
+        if ConfigSectionMap(filter_criteria, Config)['avg_depth'] == "yes":
+            keep_logging('The average depth filter is turned on.', 'The average depth filter is turned on.', logger, 'info')
+            low_Dp = float(Avg_dp) / 2
+            high_Dp = float(Avg_dp) * 5
+            DP_filter = "DP > %s && DP < %s" % (int(low_Dp), int(high_Dp))
+        else:
+            DP_filter = "DP > %s" % float(ConfigSectionMap(filter_criteria, Config)['dp'])
+        MQ_filter = "MQ > %s" % float(ConfigSectionMap(filter_criteria, Config)['mq'])
+        FQ_filter = "FQ > %s" % float(ConfigSectionMap(filter_criteria, Config)['fq'])
+        QUAL_filter = "QUAL > %s" % float(ConfigSectionMap(filter_criteria, Config)['qual'])
+        AF_filter = "AF1 < %s" % float(ConfigSectionMap(filter_criteria, Config)['af'])
+        gatk_filter2_parameter_expression = "%s && %s && %s && %s && %s" % (FQ_filter, MQ_filter, QUAL_filter, DP_filter, AF_filter)
+        if os.path.exists(final_raw_vcf):
+            gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_contamination.vcf --variant %s --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+        else:
+            gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_contamination.vcf --variant %s.gz --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+        filter_flag_command = "grep '#\|PASS_filter2' %s/%s_filter2_contamination.vcf > %s/%s_filter2_final_contamination.vcf" % (out_path, analysis, out_path, analysis)
+        keep_logging(gatk_filter2_command, gatk_filter2_command, logger, 'debug')
+        keep_logging(filter_flag_command, filter_flag_command, logger, 'debug')
+        try:
+            call(gatk_filter2_command, logger)
+            call(filter_flag_command, logger)
+        except sp.CalledProcessError:
+            keep_logging('Error in GATK filter step. Exiting.', 'Error in GATK filter step. Exiting.', logger, 'exception')
+            sys.exit(1)
+        gatk_filter2_final_contamination_vcf = "%s/%s_filter2_final_contamination.vcf" % (out_path, analysis)
+        #extract_dp = "egrep -v \"^#\" %s | cut -f 8 | sed 's/^.*DP=\([0-9]*\);.*$/\1/' > %s/%s_depth_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
+        extract_dp = "egrep -v \"^#\" %s | cut -f 8 | grep -Po 'DP=[0-9]*;?' | sed 's/DP=//g' | sed 's/;//g' > %s/%s_depth_values.txt" % (
+        gatk_filter2_final_contamination_vcf, out_path, analysis)
+        extract_pos = "grep -v '^#' %s | awk -F'\t' '{print $2}' > %s/%s_POS_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
+        extract_fq = "awk -F'\t' '{print $8}' %s  | grep -o 'FQ=.*' | sed 's/FQ=//g' | awk -F';' '{print $1}' > %s/%s_FQ_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
+        extract_mq = "egrep -v \"^#\" %s | cut -f 8 | sed 's/^.*MQ=\([0-9]*\);.*$/\1/' > %s/%s_MQ_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
+        extract_af = "awk -F'\t' '{print $8}' %s  | grep -o 'AF1=.*' | sed 's/AF1=//g' | awk -F';' '{print $1}' > %s/%s_AF1_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
+        try:
+            call(extract_dp, logger)
+            call(extract_pos, logger)
+            call(extract_fq, logger)
+            call(extract_mq, logger)
+            call(extract_af, logger)
+            keep_logging(extract_dp, filter_flag_command, logger, 'debug')
+            keep_logging(extract_pos, filter_flag_command, logger, 'debug')
+            keep_logging(extract_fq, filter_flag_command, logger, 'debug')
+            keep_logging(extract_mq, filter_flag_command, logger, 'debug')
+            keep_logging(extract_af, filter_flag_command, logger, 'debug')
+        except sp.CalledProcessError:
+            keep_logging('Error in GATK contamination filter step. Exiting.', 'Error in GATK contamination filter step. Exiting.', logger, 'exception')
+            sys.exit(1)
+        header = "pos,af"
+        header_cmd = "echo \"%s\" > %s/header.txt" % (header, out_path)
+        call(header_cmd, logger)
+        paste_command = "paste -d, %s/%s_POS_values.txt %s/%s_AF1_values.txt > %s/%s_temp_paste_file.txt" % (out_path, analysis, out_path, analysis, out_path, analysis)
+        call(paste_command, logger)
+        combine_file_cmd = "cat %s/header.txt %s/%s_temp_paste_file.txt > %s/%s_INFO.txt" % (out_path, out_path, analysis, out_path, analysis)
+        call(combine_file_cmd, logger)
+        return gatk_filter2_final_contamination_vcf
+    elif ConfigSectionMap("pipeline", Config)['variant_caller'] == "gatkhaplotypecaller":
+        print "filter"
+
+def gatk_filter_indel(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp):
+    # if ConfigSectionMap("pipeline", Config)['variant_caller'] == "samtools":
+    #     base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+    #     filter_criteria = ConfigSectionMap("SNP_filters", Config)['filter_criteria']
+    #     if ConfigSectionMap(filter_criteria, Config)['avg_depth'] == "yes":
+    #         keep_logging("Using variant filter parameters from: %s" % filter_criteria,
+    #                      "Using variant filter parameters from: %s" % filter_criteria, logger, 'info')
+    #         low_Dp = float(Avg_dp) / 2
+    #         high_Dp = float(Avg_dp) * 5
+    #         DP_filter = "DP > %s && DP < %s" % (int(low_Dp), int(high_Dp))
+    #     else:
+    #         DP_filter = "DP > %s" % ConfigSectionMap(filter_criteria, Config)['dp']
+    #     MQ_filter = "MQ > %s" % ConfigSectionMap(filter_criteria, Config)['mq']
+    #     FQ_filter = "FQ < %s" % ConfigSectionMap(filter_criteria, Config)['fq']
+    #     FQ_filter2 = "FQ < %s" % ConfigSectionMap(filter_criteria, Config)['fq2']
+    #     QUAL_filter = "QUAL > %s" % ConfigSectionMap(filter_criteria, Config)['qual']
+    #     AF_filter = "AF1 > %s" % float(ConfigSectionMap(filter_criteria, Config)['af'])
+    #
+    #     gatk_filter2_parameter_expression = "%s && %s && %s && %s && %s && %s" % (FQ_filter, MQ_filter, QUAL_filter, DP_filter, FQ_filter2, AF_filter)
+    #     if os.path.exists(final_raw_vcf):
+    #         gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_indel_gatk.vcf --variant %s --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+    #     else:
+    #         gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_indel_gatk.vcf --variant %s.gz --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+    #
+    #     filter_flag_command = "grep '#\|PASS_filter2' %s/%s_filter2_indel_gatk.vcf > %s/%s_filter2_indel_final.vcf" % (out_path, analysis, out_path, analysis)
+    #     keep_logging(gatk_filter2_command, gatk_filter2_command, logger, 'debug')
+    #     keep_logging(filter_flag_command, filter_flag_command, logger, 'debug')
+    #     try:
+    #         call(gatk_filter2_command, logger)
+    #         call(filter_flag_command, logger)
+    #     except sp.CalledProcessError:
+    #         keep_logging('Error in GATK filter step. Exiting.', 'Error in GATK filter step. Exiting.', logger, 'exception')
+    #         sys.exit(1)
+    #     gatk_filter2_final_vcf = "%s/%s_filter2_indel_final.vcf" % (out_path, analysis)
+    #     return gatk_filter2_final_vcf
+    # elif ConfigSectionMap("pipeline", Config)['variant_caller'] == "gatkhaplotypecaller":
+    base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)[
+        'gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+    filter_criteria = ConfigSectionMap("SNP_filters", Config)['filter_criteria']
+    keep_logging("Using variant filter parameters from: %s" % filter_criteria, "Using variant filter parameters from: %s" % filter_criteria, logger, 'info')
     if ConfigSectionMap(filter_criteria, Config)['avg_depth'] == "yes":
-        keep_logging('The average depth filter is turned on.', 'The average depth filter is turned on.', logger, 'info')
+        keep_logging('The average depth filter is turned on.', 'The average depth filter is turned on.', logger,
+                     'info')
         low_Dp = float(Avg_dp) / 2
         high_Dp = float(Avg_dp) * 5
         DP_filter = "DP > %s && DP < %s" % (int(low_Dp), int(high_Dp))
     else:
         DP_filter = "DP > %s" % float(ConfigSectionMap(filter_criteria, Config)['dp'])
     MQ_filter = "MQ > %s" % float(ConfigSectionMap(filter_criteria, Config)['mq'])
-    FQ_filter = "FQ > %s" % float(ConfigSectionMap(filter_criteria, Config)['fq'])
-    QUAL_filter = "QUAL > %s" % float(ConfigSectionMap(filter_criteria, Config)['qual'])
-    AF_filter = "AF1 < %s" % float(ConfigSectionMap(filter_criteria, Config)['af'])
-    gatk_filter2_parameter_expression = "%s && %s && %s && %s && %s" % (FQ_filter, MQ_filter, QUAL_filter, DP_filter, AF_filter)
-    if os.path.exists(final_raw_vcf):
-        gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_contamination.vcf --variant %s --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
-    else:
-        gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_contamination.vcf --variant %s.gz --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
-    filter_flag_command = "grep '#\|PASS_filter2' %s/%s_filter2_contamination.vcf > %s/%s_filter2_final_contamination.vcf" % (out_path, analysis, out_path, analysis)
-    keep_logging(gatk_filter2_command, gatk_filter2_command, logger, 'debug')
-    keep_logging(filter_flag_command, filter_flag_command, logger, 'debug')
-    try:
-        call(gatk_filter2_command, logger)
-        call(filter_flag_command, logger)
-    except sp.CalledProcessError:
-        keep_logging('Error in GATK filter step. Exiting.', 'Error in GATK filter step. Exiting.', logger, 'exception')
-        sys.exit(1)
-    gatk_filter2_final_contamination_vcf = "%s/%s_filter2_final_contamination.vcf" % (out_path, analysis)
-    #extract_dp = "egrep -v \"^#\" %s | cut -f 8 | sed 's/^.*DP=\([0-9]*\);.*$/\1/' > %s/%s_depth_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
-    extract_dp = "egrep -v \"^#\" %s | cut -f 8 | grep -Po 'DP=[0-9]*;?' | sed 's/DP=//g' | sed 's/;//g' > %s/%s_depth_values.txt" % (
-    gatk_filter2_final_contamination_vcf, out_path, analysis)
-    extract_pos = "grep -v '^#' %s | awk -F'\t' '{print $2}' > %s/%s_POS_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
-    extract_fq = "awk -F'\t' '{print $8}' %s  | grep -o 'FQ=.*' | sed 's/FQ=//g' | awk -F';' '{print $1}' > %s/%s_FQ_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
-    extract_mq = "egrep -v \"^#\" %s | cut -f 8 | sed 's/^.*MQ=\([0-9]*\);.*$/\1/' > %s/%s_MQ_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
-    extract_af = "awk -F'\t' '{print $8}' %s  | grep -o 'AF1=.*' | sed 's/AF1=//g' | awk -F';' '{print $1}' > %s/%s_AF1_values.txt" % (gatk_filter2_final_contamination_vcf, out_path, analysis)
-    try:
-        call(extract_dp, logger)
-        call(extract_pos, logger)
-        call(extract_fq, logger)
-        call(extract_mq, logger)
-        call(extract_af, logger)
-        keep_logging(extract_dp, filter_flag_command, logger, 'debug')
-        keep_logging(extract_pos, filter_flag_command, logger, 'debug')
-        keep_logging(extract_fq, filter_flag_command, logger, 'debug')
-        keep_logging(extract_mq, filter_flag_command, logger, 'debug')
-        keep_logging(extract_af, filter_flag_command, logger, 'debug')
-    except sp.CalledProcessError:
-        keep_logging('Error in GATK contamination filter step. Exiting.', 'Error in GATK contamination filter step. Exiting.', logger, 'exception')
-        sys.exit(1)
-    header = "pos,af"
-    header_cmd = "echo \"%s\" > %s/header.txt" % (header, out_path)
-    call(header_cmd, logger)
-    paste_command = "paste -d, %s/%s_POS_values.txt %s/%s_AF1_values.txt > %s/%s_temp_paste_file.txt" % (out_path, analysis, out_path, analysis, out_path, analysis)
-    call(paste_command, logger)
-    combine_file_cmd = "cat %s/header.txt %s/%s_temp_paste_file.txt > %s/%s_INFO.txt" % (out_path, out_path, analysis, out_path, analysis)
-    call(combine_file_cmd, logger)
-    return gatk_filter2_final_contamination_vcf
-
-def gatk_filter2_indel(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp):
-    base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
-    filter_criteria = ConfigSectionMap("SNP_filters", Config)['filter_criteria']
-    if ConfigSectionMap(filter_criteria, Config)['avg_depth'] == "yes":
-        keep_logging('The average depth filter is turned on.', 'The average depth filter is turned on.', logger, 'info')
-        low_Dp = float(Avg_dp) / 2
-        high_Dp = float(Avg_dp) * 5
-        DP_filter = "DP > %s && DP < %s" % (int(low_Dp), int(high_Dp))
-    else:
-        DP_filter = "DP > %s" % ConfigSectionMap(filter_criteria, Config)['dp']
-    MQ_filter = "MQ > %s" % ConfigSectionMap(filter_criteria, Config)['mq']
-    FQ_filter = "FQ < %s" % ConfigSectionMap(filter_criteria, Config)['fq']
-    FQ_filter2 = "FQ < %s" % ConfigSectionMap(filter_criteria, Config)['fq2']
-    QUAL_filter = "QUAL > %s" % ConfigSectionMap(filter_criteria, Config)['qual']
-    AF_filter = "AF1 > %s" % float(ConfigSectionMap(filter_criteria, Config)['af'])
-
-    gatk_filter2_parameter_expression = "%s && %s && %s && %s && %s && %s" % (FQ_filter, MQ_filter, QUAL_filter, DP_filter, FQ_filter2, AF_filter)
+    QUAL_filter = "QD > %s" % float(ConfigSectionMap(filter_criteria, Config)['qd'])
+    AF_filter = "AF > %s" % float(ConfigSectionMap(filter_criteria, Config)['af'])
+
+    #gatk_filter2_parameter_expression = "%s && %s && %s && %s" % (MQ_filter, QUAL_filter, DP_filter, AF_filter)
+    gatk_filter2_parameter_expression = "%s && %s && %s && %s" % (MQ_filter, QUAL_filter, DP_filter, AF_filter)
+
     if os.path.exists(final_raw_vcf):
-        gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_indel_gatk.vcf --variant %s --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+        gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_indel_gatk.vcf --variant %s --filterExpression \"%s\" --filterName PASS_filter2" % (
+        base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
     else:
-        gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_indel_gatk.vcf --variant %s.gz --filterExpression \"%s\" --filterName PASS_filter2" % (base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
+        gatk_filter2_command = "java -jar %s -T VariantFiltration -R %s -o %s/%s_filter2_indel_gatk.vcf --variant %s.gz --filterExpression \"%s\" --filterName PASS_filter2" % (
+        base_cmd, reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
 
-    filter_flag_command = "grep '#\|PASS_filter2' %s/%s_filter2_indel_gatk.vcf > %s/%s_filter2_indel_final.vcf" % (out_path, analysis, out_path, analysis)
+    filter_flag_command = "grep '#\|PASS_filter2' %s/%s_filter2_indel_gatk.vcf > %s/%s_filter2_indel_final.vcf" % (
+    out_path, analysis, out_path, analysis)
     keep_logging(gatk_filter2_command, gatk_filter2_command, logger, 'debug')
     keep_logging(filter_flag_command, filter_flag_command, logger, 'debug')
     try:
         call(gatk_filter2_command, logger)
         call(filter_flag_command, logger)
     except sp.CalledProcessError:
-        keep_logging('Error in GATK filter step. Exiting.', 'Error in GATK filter step. Exiting.', logger, 'exception')
+        keep_logging('Error in GATK filter step. Exiting.', 'Error in GATK filter step. Exiting.', logger,
+                     'exception')
         sys.exit(1)
     gatk_filter2_final_vcf = "%s/%s_filter2_indel_final.vcf" % (out_path, analysis)
     return gatk_filter2_final_vcf
@@ -171,6 +255,16 @@ def gatk_vcf2fasta_filter2(only_snp_filter2_vcf_file, out_path, analysis, refere
     gatk_vcf2fasta_filter2_file = "%s_filter2.fasta" % (only_snp_filter2_vcf_file)
     return gatk_vcf2fasta_filter2_file
 
+def gatkhaplotypecaller(out_finalbam, out_path, reference, analysis, logger, Config):
+    base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+    reference_filename = ConfigSectionMap(reference, Config)['ref_path'] + "/" + ConfigSectionMap(reference, Config)['ref_name']
+    cmd = "java -jar %s %s -R %s -I %s -o %s/%s_aln_mpileup_raw.vcf" % (base_cmd, ConfigSectionMap("gatk", Config)['haplotype_parameters'], reference_filename, out_finalbam, out_path, analysis)
+    keep_logging('Running Command: [%s]' % cmd, 'Running Command: [%s]' % cmd, logger, 'info')
+    #os.system(cmd)
+    call(cmd, logger)
+    final_raw_vcf =  "%s/%s_aln_mpileup_raw.vcf" % (out_path, analysis)
+    return final_raw_vcf
+
 """ Unused methods """
 def gatk_filter1(final_raw_vcf, out_path, analysis, reference):
     base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
@@ -197,14 +291,14 @@ def indel_realign(out_marked_sort_bam_rename, reference, out_path, analysis):
     out_indel_realigned = "%s/%s_aln_realigned.bam" % (out_path, analysis)
     return out_indel_realigned
 
-def gatkhaplotypecaller(out_finalbam, out_path, reference, analysis):
-    base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
-    reference_filename = ConfigSectionMap(reference)['ref_path'] + "/" + ConfigSectionMap(reference)['ref_name']
-    cmd = "java -jar %s %s -R %s -I %s -o %s/%s_aln_gatk_raw.vcf" % (base_cmd, ConfigSectionMap("gatk")['haplotype_parameters'], reference_filename, out_finalbam, out_path, analysis)
-    keep_logging('Running Command: [%s]' % cmd, 'Running Command: [%s]' % cmd, logger, 'info')
-    os.system(cmd)
-    final_raw_vcf =  "%s/%s_aln_gatk_raw.vcf" % (out_path, analysis)
-    return final_raw_vcf
+# def gatkhaplotypecaller(out_finalbam, out_path, reference, analysis):
+#     base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+#     reference_filename = ConfigSectionMap(reference)['ref_path'] + "/" + ConfigSectionMap(reference)['ref_name']
+#     cmd = "java -jar %s %s -R %s -I %s -o %s/%s_aln_gatk_raw.vcf" % (base_cmd, ConfigSectionMap("gatk")['haplotype_parameters'], reference_filename, out_finalbam, out_path, analysis)
+#     keep_logging('Running Command: [%s]' % cmd, 'Running Command: [%s]' % cmd, logger, 'info')
+#     os.system(cmd)
+#     final_raw_vcf =  "%s/%s_aln_gatk_raw.vcf" % (out_path, analysis)
+#     return final_raw_vcf
 
 def gatk_vcf2fasta_filter1(only_snp_filter1_vcf_file, out_path, analysis, reference):
     base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)['gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
diff --git a/modules/gatk.pyc b/modules/gatk.pyc
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diff --git a/modules/phage_detection.py b/modules/phage_detection.py
index 2a10997..7a3e7e9 100644
--- a/modules/phage_detection.py
+++ b/modules/phage_detection.py
@@ -19,7 +19,6 @@ def run_phaster(reference_genome, outdir, logger, Config):
         keep_logging("Phaster Post Json file %s/%s_phaster_post.json already exists. Skipping Phaster Job submission. To get latest Phaster results remove this file and run core_prep step again." % (outdir, str(out_name[0])), "Phaster Post Json: %s/%s_phaster_post.json exists. Skipping Phaster Job submission. To get latest Phaster results remove this file and run core_prep step again." % (outdir, str(out_name[0])), logger,
                      'info')
 
-
 def parse_phaster(reference_genome, outdir, logger, Config):
     out_name = (os.path.basename(reference_genome)).split('.')
     if os.path.isfile("%s/%s_phaster_post.json" % (outdir, str(out_name[0]))) and os.stat("%s/%s_phaster_post.json" % (outdir, str(out_name[0]))).st_size != 0:
@@ -83,6 +82,7 @@ def parse_phaster(reference_genome, outdir, logger, Config):
                 # keep_logging('Running: %s' % phaster_unzip_cmd, 'Running: %s' % phaster_get_cmd, logger, 'debug')
                 # call(phaster_get_zip_cmd, logger)
                 # call(phaster_unzip_cmd, logger)
+
     if os.path.isfile("%s/summary.txt" % outdir):
         keep_logging('Extracting Phage region information from %s/summary.txt' % outdir, 'Extracting Phage region information from %s/summary.txt' % outdir, logger, 'info')
         get_phage_regions = "sed -n -e '/REGION/,$p' %s/summary.txt | awk 'NR>2' | awk -F' ' '{print $5}'" % outdir
@@ -99,7 +99,25 @@ def parse_phaster(reference_genome, outdir, logger, Config):
         f_open = open("%s/phage_region_positions.txt" % outdir, 'w+')
         for pos in phage_positions:
             f_open.write(str(pos) + "\n")
-    else:
+
+    if os.path.isfile("%s/summary.txt" % os.path.dirname(reference_genome)):
+        keep_logging('Extracting Phage region information from %s/summary.txt' % os.path.dirname(reference_genome), 'Extracting Phage region information from %s/summary.txt' % os.path.dirname(reference_genome), logger, 'info')
+        get_phage_regions = "sed -n -e '/REGION/,$p' %s/summary.txt | awk 'NR>2' | awk -F' ' '{print $5}'" % os.path.dirname(reference_genome)
+        proc = subprocess.Popen([get_phage_regions], stdout=subprocess.PIPE, shell=True)
+        (out, err) = proc.communicate()
+        out = (out.strip()).split('\n')
+        #print out
+        phage_positions = []
+        for i in out:
+            i_range = i.split('-')
+            end_range = int(i_range[1]) + 1
+            phage_positions.extend(list(range(int(i_range[0]), end_range)))
+
+        f_open = open("%s/phage_region_positions.txt" % outdir, 'w+')
+        for pos in phage_positions:
+            f_open.write(str(pos) + "\n")
+
+    if not os.path.isfile("%s/phage_region_positions.txt" % outdir):
         keep_logging('Phaster output file %s/summary.txt not found. Phaster job is still running or Phaster get json results were empty.' % outdir,
                      'Phaster output file %s/summary.txt not found. Phaster job is still running or Phaster get json results were empty.' % outdir, logger, 'exception')
         exit()
diff --git a/modules/phage_detection.pyc b/modules/phage_detection.pyc
index 7c27eaaa7b159dbdba44494ba5ae9f1b0f916f7a..a7712be648aecc798e2fce533fd4928dac8c404e 100644
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diff --git a/modules/remove_5_bp_snp_indel.py b/modules/remove_5_bp_snp_indel.py
index 6e4170a..20640be 100644
--- a/modules/remove_5_bp_snp_indel.py
+++ b/modules/remove_5_bp_snp_indel.py
@@ -5,7 +5,8 @@
 from config_settings import ConfigSectionMap
 import csv
 from modules.logging_subprocess import *
-from modules.log_modules import *
+from modules.log_modules import keep_logging
+from modules.gatk import *
 
 """ Initialize the arrays """
 indel_positions = []
@@ -13,97 +14,208 @@
 
 """ Remove SNPs that are within 5 bp in proximity to an indel """
 def remove_5_bp_snp_indel(raw_vcf_file, out_path, analysis, reference, logger, Config):
-    remove_snps_5_bp_snp_indel_file_name = raw_vcf_file + "_5bp_indel_removed.vcf"
-    with open(raw_vcf_file, 'rU') as csv_file:
-        for line in csv_file:
-            if not line.startswith('#'):
-                line_array = line.split('\t')
-                if line_array[7].startswith('INDEL;'):
-                     indel_positions.append(line_array[1])
-        for i in indel_positions:
-            lower_range = int(i) - 5
-            upper_range = int(i) + 6
-            for positions in range(lower_range,upper_range):
-                indel_range_positions.append(positions)
-    f1=open(remove_snps_5_bp_snp_indel_file_name, 'w+')
-    with open(raw_vcf_file, 'rU') as csv_file2:
-        for line in csv_file2:
-            if not line.startswith('#'):
-               line_array = line.split('\t')
-               if int(line_array[1]) not in indel_range_positions:
-                   print_string = line
-                   f1.write(print_string)
-            else:
-                print_string = line
-                f1.write(print_string)
-    return remove_snps_5_bp_snp_indel_file_name
-
-""" Remove SNPs that are within 5 bp in proximity to an indel """
+    #variant_caller = eval(ConfigSectionMap("pipeline", Config)['variant_caller'])
+    if ConfigSectionMap("pipeline", Config)['variant_caller'] == "samtools":
+        print "Samtools: Removing SNPs proximate to Indel by 5bp"
+        remove_snps_5_bp_snp_indel_file_name = raw_vcf_file + "_5bp_indel_removed.vcf"
+        with open(raw_vcf_file, 'rU') as csv_file:
+            for line in csv_file:
+                if not line.startswith('#'):
+                    line_array = line.split('\t')
+                    if line_array[7].startswith('INDEL;'):
+                         indel_positions.append(line_array[1])
+            for i in indel_positions:
+                lower_range = int(i) - 5
+                upper_range = int(i) + 6
+                for positions in range(lower_range,upper_range):
+                    indel_range_positions.append(positions)
+        f1=open(remove_snps_5_bp_snp_indel_file_name, 'w+')
+        with open(raw_vcf_file, 'rU') as csv_file2:
+            for line in csv_file2:
+                if not line.startswith('#'):
+                   line_array = line.split('\t')
+                   if int(line_array[1]) not in indel_range_positions:
+                       print_string = line
+                       f1.write(print_string)
+                else:
+                    print_string = line
+                    f1.write(print_string)
+        return remove_snps_5_bp_snp_indel_file_name
+
+    elif ConfigSectionMap("pipeline", Config)['variant_caller'] == "gatkhaplotypecaller":
+        print "GATK Haplotype caller: Removing SNPs proximate to Indel by 5bp"
+        remove_snps_5_bp_snp_indel_file_name = raw_vcf_file + "_5bp_indel_removed.vcf"
+        indel_file_name = raw_vcf_file + "_indel.vcf"
+        base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)[
+            'gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+        cmd = "java -jar %s -T SelectVariants -R %s -V %s -selectType INDEL -o %s" % (
+        base_cmd, reference, raw_vcf_file, indel_file_name)
+        call(cmd, logger)
+        keep_logging('Running Command: [%s]' % cmd, 'Running Command: [%s]' % cmd, logger, 'info')
+        with open(indel_file_name, 'rU') as csv_file:
+            for line in csv_file:
+                if not line.startswith('#'):
+                    line_array = line.split('\t')
+                    indel_positions.append(line_array[1])
+            for i in indel_positions:
+                lower_range = int(i) - 5
+                upper_range = int(i) + 6
+                for positions in range(lower_range,upper_range):
+                    indel_range_positions.append(positions)
+        f1 = open(remove_snps_5_bp_snp_indel_file_name, 'w+')
+        with open(raw_vcf_file, 'rU') as csv_file2:
+            for line in csv_file2:
+                if not line.startswith('#'):
+                    line_array = line.split('\t')
+                    if int(line_array[1]) not in indel_range_positions:
+                        print_string = line
+                        f1.write(print_string)
+                else:
+                    print_string = line
+                    f1.write(print_string)
+        return remove_snps_5_bp_snp_indel_file_name
+
+""" Extract Indels from raw vcf files """
 def prepare_indel(raw_vcf_file, out_path, analysis, reference, logger, Config):
-    indel_file_name = raw_vcf_file + "_indel.vcf"
-    with open(raw_vcf_file, 'rU') as csv_file:
-        for line in csv_file:
-            if not line.startswith('#'):
-                line_array = line.split('\t')
-                if line_array[7].startswith('INDEL;'):
-                     indel_positions.append(int(line_array[1]))
-    #print indel_positions
-    f1=open(indel_file_name, 'w+')
-    with open(raw_vcf_file, 'rU') as csv_file2:
-        for line in csv_file2:
-            if not line.startswith('#'):
-               line_array = line.split('\t')
-               if int(line_array[1]) in indel_positions:
-                   print_string = line
-                   f1.write(print_string)
-            else:
-                print_string = line
-                f1.write(print_string)
-    #print indel_file_name
-    return indel_file_name
-
-# Remove SNPS that are within 10 bp in proximity to each other
-def remove_proximate_snps(gatk_filter2_final_vcf_file, out_path, analysis, reference, logger, Config):
-    filter_criteria = ConfigSectionMap("SNP_filters", Config)['filter_criteria']
-    print "The proximate filter criteria is: %s" % str(ConfigSectionMap(filter_criteria, Config)['prox'])
-    all_position = []
-    remove_proximate_position_array = []
-    gatk_filter2_final_vcf_file_no_proximate_snp = gatk_filter2_final_vcf_file + "_no_proximate_snp.vcf"
-    with open(gatk_filter2_final_vcf_file, 'rU') as csv_file:
-        for line in csv_file:
-            if not line.startswith('#'):
-                line_array = line.split('\t')
-                all_position.append(line_array[1])
-    for position in all_position:
-        position_index = all_position.index(position)
-        next_position_index = position_index + 1
-        if next_position_index < len(all_position):
-            diff = int(all_position[next_position_index]) - int(position)
-            if diff < int(ConfigSectionMap(filter_criteria, Config)['prox']):
-                if position not in remove_proximate_position_array and all_position[next_position_index] not in remove_proximate_position_array:
-                    remove_proximate_position_array.append(int(position))
-                    remove_proximate_position_array.append(int(all_position[next_position_index]))
-    #print remove_proximate_position_array
-    f1=open(gatk_filter2_final_vcf_file_no_proximate_snp, 'w+')
-    with open(gatk_filter2_final_vcf_file, 'rU') as csv_file2:
-        for line in csv_file2:
-            if not line.startswith('#'):
-               line_array = line.split('\t')
-               if int(line_array[1]) not in remove_proximate_position_array:
-                   print_string = line
-                   f1.write(print_string)
-            else:
-                print_string = line
-                f1.write(print_string)
-    gatk_filter2_final_vcf_file_no_proximate_snp_positions = gatk_filter2_final_vcf_file + "_no_proximate_snp.vcf_positions_array"
-    f2=open(gatk_filter2_final_vcf_file_no_proximate_snp_positions, 'w+')
-    for i in remove_proximate_position_array:
-        position_print_string = str(i) + "\n"
-        f2.write(position_print_string)
-    return gatk_filter2_final_vcf_file_no_proximate_snp
 
+    #variant_caller = eval(ConfigSectionMap("pipeline", Config)['variant_caller'])
+    if ConfigSectionMap("pipeline", Config)['variant_caller'] == "samtools":
+        print "Samtools: Extracting indels from raw vcf files"
+        indel_file_name = raw_vcf_file + "_indel.vcf"
+        with open(raw_vcf_file, 'rU') as csv_file:
+            for line in csv_file:
+                if not line.startswith('#'):
+                    line_array = line.split('\t')
+                    if line_array[7].startswith('INDEL;'):
+                         indel_positions.append(int(line_array[1]))
+        #print indel_positions
+        f1=open(indel_file_name, 'w+')
+        with open(raw_vcf_file, 'rU') as csv_file2:
+            for line in csv_file2:
+                if not line.startswith('#'):
+                   line_array = line.split('\t')
+                   if int(line_array[1]) in indel_positions:
+                       print_string = line
+                       f1.write(print_string)
+                else:
+                    print_string = line
+                    f1.write(print_string)
+        #print indel_file_name
+        return indel_file_name
+    elif ConfigSectionMap("pipeline", Config)['variant_caller'] == "gatkhaplotypecaller":
+        print "GATK Haplotype caller: Extracting indels from raw vcf files"
+        indel_file_name = raw_vcf_file + "_indel.vcf"
+        base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)[
+            'gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+        cmd = "java -jar %s -T SelectVariants -R %s -V %s -selectType INDEL -o %s" % (
+            base_cmd, reference, raw_vcf_file, indel_file_name)
+        call(cmd, logger)
+        keep_logging('Running Command: [%s]' % cmd, 'Running Command: [%s]' % cmd, logger, 'info')
+        return indel_file_name
+
+""" Extract Indels from raw vcf files """
+def prepare_indel_gatk(out_finalbam, out_path, analysis, reference, logger, Config):
+    reference_filename = ConfigSectionMap(reference, Config)['ref_path'] + "/" + ConfigSectionMap(reference, Config)['ref_name']
+    final_raw_vcf = gatkhaplotypecaller(out_finalbam, out_path, reference, analysis, logger, Config)
+    if not os.path.isfile(final_raw_vcf):
+        keep_logging('Error in GATK Haplotype Variant Calling step. Exiting.', 'Error in GATK Haplotype Variant Calling step. Exiting.', logger, 'exception')
+        exit()
+    else:
+        print "GATK Haplotype caller: Extracting indels from raw vcf files"
+        indel_file_name = final_raw_vcf + "_indel.vcf"
+        base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)[
+            'gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+        cmd = "java -jar %s -T SelectVariants -R %s -V %s -selectType INDEL -o %s" % (
+            base_cmd, reference_filename, final_raw_vcf, indel_file_name)
+        call(cmd, logger)
+        keep_logging('Running Command: [%s]' % cmd, 'Running Command: [%s]' % cmd, logger, 'info')
+        return indel_file_name
+
+
+
+    # #variant_caller = eval(ConfigSectionMap("pipeline", Config)['variant_caller'])
+    # if ConfigSectionMap("pipeline", Config)['variant_caller'] == "samtools":
+    #     print "Samtools: Extracting indels from raw vcf files"
+    #     indel_file_name = raw_vcf_file + "_indel.vcf"
+    #     with open(raw_vcf_file, 'rU') as csv_file:
+    #         for line in csv_file:
+    #             if not line.startswith('#'):
+    #                 line_array = line.split('\t')
+    #                 if line_array[7].startswith('INDEL;'):
+    #                      indel_positions.append(int(line_array[1]))
+    #     #print indel_positions
+    #     f1=open(indel_file_name, 'w+')
+    #     with open(raw_vcf_file, 'rU') as csv_file2:
+    #         for line in csv_file2:
+    #             if not line.startswith('#'):
+    #                line_array = line.split('\t')
+    #                if int(line_array[1]) in indel_positions:
+    #                    print_string = line
+    #                    f1.write(print_string)
+    #             else:
+    #                 print_string = line
+    #                 f1.write(print_string)
+    #     #print indel_file_name
+    #     return indel_file_name
+    # elif ConfigSectionMap("pipeline", Config)['variant_caller'] == "gatkhaplotypecaller":
+    #     print "GATK Haplotype caller: Extracting indels from raw vcf files"
+    #     indel_file_name = raw_vcf_file + "_indel.vcf"
+    #     base_cmd = ConfigSectionMap("bin_path", Config)['binbase'] + "/" + ConfigSectionMap("gatk", Config)[
+    #         'gatk_bin'] + "/" + ConfigSectionMap("gatk", Config)['base_cmd']
+    #     cmd = "java -jar %s -T SelectVariants -R %s -V %s -selectType INDEL -o %s" % (
+    #         base_cmd, reference, raw_vcf_file, indel_file_name)
+    #     call(cmd, logger)
+    #     keep_logging('Running Command: [%s]' % cmd, 'Running Command: [%s]' % cmd, logger, 'info')
+    #     return indel_file_name
 
 
+# Remove SNPS that are within 10 bp in proximity to each other
+def remove_proximate_snps(gatk_filter2_final_vcf_file, out_path, analysis, reference, logger, Config):
+    #variant_caller = eval(ConfigSectionMap("pipeline", Config)['variant_caller'])
+    if ConfigSectionMap("pipeline", Config)['variant_caller'] == "samtools":
+        print "Samtools: Removing proximate SNPs"
+        filter_criteria = ConfigSectionMap("SNP_filters", Config)['filter_criteria']
+        print "The proximate filter criteria is: %s" % str(ConfigSectionMap(filter_criteria, Config)['prox'])
+        all_position = []
+        remove_proximate_position_array = []
+        gatk_filter2_final_vcf_file_no_proximate_snp = gatk_filter2_final_vcf_file + "_no_proximate_snp.vcf"
+        with open(gatk_filter2_final_vcf_file, 'rU') as csv_file:
+            for line in csv_file:
+                if not line.startswith('#'):
+                    line_array = line.split('\t')
+                    all_position.append(line_array[1])
+        for position in all_position:
+            position_index = all_position.index(position)
+            next_position_index = position_index + 1
+            if next_position_index < len(all_position):
+                diff = int(all_position[next_position_index]) - int(position)
+                if diff < int(ConfigSectionMap(filter_criteria, Config)['prox']):
+                    if position not in remove_proximate_position_array and all_position[next_position_index] not in remove_proximate_position_array:
+                        remove_proximate_position_array.append(int(position))
+                        remove_proximate_position_array.append(int(all_position[next_position_index]))
+        #print remove_proximate_position_array
+        f1=open(gatk_filter2_final_vcf_file_no_proximate_snp, 'w+')
+        with open(gatk_filter2_final_vcf_file, 'rU') as csv_file2:
+            for line in csv_file2:
+                if not line.startswith('#'):
+                   line_array = line.split('\t')
+                   if int(line_array[1]) not in remove_proximate_position_array:
+                       print_string = line
+                       f1.write(print_string)
+                else:
+                    print_string = line
+                    f1.write(print_string)
+        gatk_filter2_final_vcf_file_no_proximate_snp_positions = gatk_filter2_final_vcf_file + "_no_proximate_snp.vcf_positions_array"
+        f2=open(gatk_filter2_final_vcf_file_no_proximate_snp_positions, 'w+')
+        for i in remove_proximate_position_array:
+            position_print_string = str(i) + "\n"
+            f2.write(position_print_string)
+        return gatk_filter2_final_vcf_file_no_proximate_snp
+
+
+    elif ConfigSectionMap("pipeline", Config)['variant_caller'] == "gatkhaplotypecaller":
+        print "GATK Haplotype caller: Removing proximate SNPs"
+        filter_criteria = ConfigSectionMap("SNP_filters", Config)['filter_criteria']
 
 
 
diff --git a/modules/remove_5_bp_snp_indel.pyc b/modules/remove_5_bp_snp_indel.pyc
index 70bb6991e944b858561d2ac9b858d3c1614c1fc1..0733f00e5d06f6c36a60ec4471619af6c9271ee8 100644
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diff --git a/modules/stages.py b/modules/stages.py
index f4a2b76..5bf4990 100644
--- a/modules/stages.py
+++ b/modules/stages.py
@@ -137,35 +137,17 @@ def qualimap(out_sorted_bam, out_path, analysis, logger, Config):
     return qualimap_report
 
 """ Variant Filteration """
-def filter2_variants(final_raw_vcf, out_path, analysis, ref_index, logger, Config, Avg_dp):
+def filter_variants(final_raw_vcf, out_path, analysis, ref_index, logger, Config, Avg_dp):
     reference = ConfigSectionMap(ref_index, Config)['ref_path'] + "/" + ConfigSectionMap(ref_index, Config)['ref_name']
-    gatk_filter2_final_vcf_file = gatk_filter2(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp)
-    gatk_filter2_final_vcf_contamination_file = gatk_filter2_contamination(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp)
-    gatk_filter2_final_vcf_file_no_proximate_snp = remove_proximate_snps(gatk_filter2_final_vcf_file, out_path, analysis, reference, logger, Config)
-    keep_logging('The vcf file with no proximate snp: {}'.format(gatk_filter2_final_vcf_file_no_proximate_snp), 'The vcf file with no proximate snp: {}'.format(gatk_filter2_final_vcf_file_no_proximate_snp), logger, 'debug')
-    gatk_vcf2fasta_filter2_file = gatk_vcf2fasta_filter2(gatk_filter2_final_vcf_file, out_path, analysis, reference, logger, Config)
-    gatk_vcf2fasta_filter2_file_no_proximate = gatk_vcf2fasta_filter2(gatk_filter2_final_vcf_file_no_proximate_snp, out_path, analysis, reference, logger, Config)
-    vcftools_vcf2fasta_filter2_file = vcftools_vcf2fasta_filter2(gatk_filter2_final_vcf_file, out_path, analysis, reference, logger, Config)
-    vcftools_vcf2fasta_filter2_file_no_proximate = vcftools_vcf2fasta_filter2(gatk_filter2_final_vcf_file_no_proximate_snp, out_path, analysis, reference, logger, Config)
-    keep_logging('The final Consensus Fasta file: {}'.format(gatk_vcf2fasta_filter2_file), 'The final Consensus Fasta file: {}'.format(gatk_vcf2fasta_filter2_file), logger, 'debug')
-    keep_logging('The final Consensus Fasta file with no proximate: {}'.format(gatk_vcf2fasta_filter2_file_no_proximate), 'The final Consensus Fasta file with no proximate: {}'.format(gatk_vcf2fasta_filter2_file_no_proximate), logger, 'debug')
-    keep_logging('The final Consensus Fasta file from VCF-consensus: {}'.format(vcftools_vcf2fasta_filter2_file), 'The final Consensus Fasta file from VCF-consensus: {}'.format(vcftools_vcf2fasta_filter2_file), logger, 'debug')
-    keep_logging('The final Consensus Fasta file from VCF-consensus with no proximate: {}'.format(vcftools_vcf2fasta_filter2_file_no_proximate), 'The final Consensus Fasta file from VCF-consensus with no proximate: {}'.format(vcftools_vcf2fasta_filter2_file_no_proximate), logger, 'debug')
-
-def filter2_indels(final_raw_indel_vcf, out_path, analysis, ref_index, logger, Config, Avg_dp):
-    reference = ConfigSectionMap(ref_index, Config)['ref_path'] + "/" + ConfigSectionMap(ref_index, Config)['ref_name']
-    gatk_filter2_final_vcf_file = gatk_filter2_indel(final_raw_indel_vcf, out_path, analysis, reference, logger, Config, Avg_dp)
-    # gatk_filter2_final_vcf_file_no_proximate_snp = remove_proximate_snps(gatk_filter2_final_vcf_file, out_path, analysis, reference, logger, Config)
-    # keep_logging('The vcf file with no proximate snp: {}'.format(gatk_filter2_final_vcf_file_no_proximate_snp), 'The vcf file with no proximate snp: {}'.format(gatk_filter2_final_vcf_file_no_proximate_snp), logger, 'debug')
-    # gatk_vcf2fasta_filter2_file = gatk_vcf2fasta_filter2(gatk_filter2_final_vcf_file, out_path, analysis, reference, logger, Config)
-    # gatk_vcf2fasta_filter2_file_no_proximate = gatk_vcf2fasta_filter2(gatk_filter2_final_vcf_file_no_proximate_snp, out_path, analysis, reference, logger, Config)
-    # vcftools_vcf2fasta_filter2_file = vcftools_vcf2fasta_filter2(gatk_filter2_final_vcf_file, out_path, analysis, reference, logger, Config)
-    # vcftools_vcf2fasta_filter2_file_no_proximate = vcftools_vcf2fasta_filter2(gatk_filter2_final_vcf_file_no_proximate_snp, out_path, analysis, reference, logger, Config)
-    # keep_logging('The final Consensus Fasta file: {}'.format(gatk_vcf2fasta_filter2_file), 'The final Consensus Fasta file: {}'.format(gatk_vcf2fasta_filter2_file), logger, 'debug')
-    # keep_logging('The final Consensus Fasta file with no proximate: {}'.format(gatk_vcf2fasta_filter2_file_no_proximate), 'The final Consensus Fasta file with no proximate: {}'.format(gatk_vcf2fasta_filter2_file_no_proximate), logger, 'debug')
-    # keep_logging('The final Consensus Fasta file from VCF-consensus: {}'.format(vcftools_vcf2fasta_filter2_file), 'The final Consensus Fasta file from VCF-consensus: {}'.format(vcftools_vcf2fasta_filter2_file), logger, 'debug')
-    # keep_logging('The final Consensus Fasta file from VCF-consensus with no proximate: {}'.format(vcftools_vcf2fasta_filter2_file_no_proximate), 'The final Consensus Fasta file from VCF-consensus with no proximate: {}'.format(vcftools_vcf2fasta_filter2_file_no_proximate), logger, 'debug')
+    gatk_filter_final_vcf_file = gatk_filter(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp)
+    #gatk_filter_final_vcf_contamination_file = gatk_filter_contamination(final_raw_vcf, out_path, analysis, reference, logger, Config, Avg_dp)
+    gatk_filter_final_vcf_file_no_proximate_snp = remove_proximate_snps(gatk_filter_final_vcf_file, out_path, analysis, reference, logger, Config)
+    keep_logging('The vcf file with no proximate snp: {}'.format(gatk_filter_final_vcf_file_no_proximate_snp), 'The vcf file with no proximate snp: {}'.format(gatk_filter_final_vcf_file_no_proximate_snp), logger, 'debug')
+
 
+def filter_indels(final_raw_indel_vcf, out_path, analysis, ref_index, logger, Config, Avg_dp):
+    reference = ConfigSectionMap(ref_index, Config)['ref_path'] + "/" + ConfigSectionMap(ref_index, Config)['ref_name']
+    gatk_filter_final_vcf_file = gatk_filter_indel(final_raw_indel_vcf, out_path, analysis, reference, logger, Config, Avg_dp)
 
 
 """ Unused Methods """
diff --git a/modules/stages.pyc b/modules/stages.pyc
index d560233588141c7ad0b64005240eafd4aa31dfd5..01bac29c46f04a072d2bacfb944aa16eb337be82 100644
GIT binary patch
delta 375
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l2FR3b5RnHWDnLXvh-d;58Vn%gCp)S;adH45JEs6AI{@(xUs3=7

delta 793
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jM|@&&gBTq#DHN?I+C$O$qCMtMPQKH0ZB|pXX-)YLX*Iwu

diff --git a/modules/variant_diagnostics/DP_analysis.py b/modules/variant_diagnostics/DP_analysis.py
index 4de84e6..8334012 100644
--- a/modules/variant_diagnostics/DP_analysis.py
+++ b/modules/variant_diagnostics/DP_analysis.py
@@ -52,7 +52,6 @@ def DP_analysis():
     DP_values_file = "%s/%s_DP_values" % (args.filter2_only_snp_vcf_dir, analysis)
     f2=open(DP_values_file, 'w+')
 
-
     with open("%s/temp_Only_filtered_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir, 'rU') as csv_filess:
         csv_readerr = csv.reader(csv_filess, delimiter='\t')
         next(csv_readerr, None)
@@ -95,5 +94,104 @@ def DP_analysis():
     #subprocess.call(["%s" % cmd], shell=True)
     #subprocess.check_call('%s' % cmd)
 
+def filtered_DP_analysis():
+    print "Analyzing depth of all the filtered positions..."
+    extract_DP_positions = "awk -F\'\\t\' \'{print $1}\' Only_filtered_positions_for_closely_matrix.txt | sed \'/^$/d\' > filtered_extract_DP_positions.txt"
+    os.system(extract_DP_positions)
+
+
+    filename_base = os.path.basename(args.filter2_only_snp_vcf_filename)
+    aln_mpileup_vcf_file = filename_base.replace('_filter2_final.vcf_no_proximate_snp.vcf', '_aln_mpileup_raw.vcf_5bp_indel_removed.vcf')
+    analysis = filename_base.replace('_filter2_final.vcf_no_proximate_snp.vcf', '')
+    grep_reference_file = "grep \'^##reference\' %s" % aln_mpileup_vcf_file
+    proc = subprocess.Popen([grep_reference_file], stdout=subprocess.PIPE, shell=True)
+    (out, err) = proc.communicate()
+    out = out.strip()
+    reference_file = out.split(':')
+    #gatk_filter2_final_vcf_file = gatk_filter2(aln_mpileup_vcf_file, temp_dir, analysis, reference_file[1])
+    #gatk_filter2_final_vcf_file_no_proximate_snp = remove_proximate_snps(gatk_filter2_final_vcf_file, temp_dir, analysis, reference_file[1])
+    # DP_values_file = "%s/%s_DP_values" % (args.filter2_only_snp_vcf_dir, analysis)
+    # f2=open(DP_values_file, 'w+')
+
+    filtered_DP_values_file = "%s/%s_filtered_DP_values" % (args.filter2_only_snp_vcf_dir, analysis)
+    filtered_f2 = open(filtered_DP_values_file, 'w+')
+
+    # with open("%s/temp_Only_filtered_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir, 'rU') as csv_filess:
+    #     csv_readerr = csv.reader(csv_filess, delimiter='\t')
+    #     next(csv_readerr, None)
+    #     for rows in csv_readerr:
+    #         #print rows
+    #         #grep_dp_field = "grep -wP \'^\S+\s+%s\s+\b\' %s | awk -F\'\\t\' \'{print $8}\' | grep -o \'DP=.*\' | sed \'s/DP=//g\' | awk -F\';\' \'{print $1}\'" % (rows[0], aln_mpileup_vcf_file)
+    #         grep_dp_field = "grep -w \'%s\' %s" % (rows[0], aln_mpileup_vcf_file)
+    #         awk_dp_field = "awk -F\'\t\' \'$2 == %s\' %s | awk -F\'\t\' \'{print $8}\' | awk -F\';\' \'{print $1}\' | sed \'s/DP=//g\'" % (rows[0], aln_mpileup_vcf_file)
+    #         #print awk_dp_field
+    #         #proc = subprocess.Popen([grep_dp_field], stdout=subprocess.PIPE, shell=True)
+    #         #(out2, err2) = proc.communicate()
+    #         #out_split = out.split('\n')
+    #         #out = out.strip()
+    #         proc = subprocess.Popen([awk_dp_field], shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    #         (out2, err2) = proc.communicate()
+    #         #print out2.strip()
+    #         if out2:
+    #             #print out2.strip()
+    #             if "INDEL" in out2:
+    #                 #print awk_dp_field
+    #                 out2 == "NA"
+    #             f2.write(out2.strip() + '\n')
+    #             # if len(out_split) > 1:
+    #             #     print out_split[0]
+    #             # # for i in out:
+    #             # #     print i
+    #             # line_split = out.split('\t')
+    #             # #print line_split
+    #             # if line_split[1] == rows[0]:
+    #             #     DP_field = line_split[7].split(';')
+    #             #     DP_value = DP_field[0].replace('DP=', '')
+    #             #print out
+    #         else:
+    #             f2.write("NA\n")
+    #             #print "NA"
+
+    with open("%s/Only_filtered_positions_for_closely_matrix.txt" % args.filter2_only_snp_vcf_dir, 'rU') as csv_filess:
+        csv_readerr = csv.reader(csv_filess, delimiter='\t')
+        next(csv_readerr, None)
+        for rows in csv_readerr:
+            #print rows
+            #grep_dp_field = "grep -wP \'^\S+\s+%s\s+\b\' %s | awk -F\'\\t\' \'{print $8}\' | grep -o \'DP=.*\' | sed \'s/DP=//g\' | awk -F\';\' \'{print $1}\'" % (rows[0], aln_mpileup_vcf_file)
+            grep_dp_field = "grep -w \'%s\' %s" % (rows[0], aln_mpileup_vcf_file)
+            awk_dp_field = "awk -F\'\t\' \'$2 == %s\' %s | awk -F\'\t\' \'{print $8}\' | awk -F\';\' \'{print $1}\' | sed \'s/DP=//g\'" % (rows[0], aln_mpileup_vcf_file)
+            #print awk_dp_field
+            #proc = subprocess.Popen([grep_dp_field], stdout=subprocess.PIPE, shell=True)
+            #(out2, err2) = proc.communicate()
+            #out_split = out.split('\n')
+            #out = out.strip()
+            proc = subprocess.Popen([awk_dp_field], shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            (out2, err2) = proc.communicate()
+            #print out2.strip()
+            if out2:
+                #print out2.strip()
+                if "INDEL" in out2:
+                    #print awk_dp_field
+                    out2 == "NA"
+                filtered_f2.write(out2.strip() + '\n')
+                # if len(out_split) > 1:
+                #     print out_split[0]
+                # # for i in out:
+                # #     print i
+                # line_split = out.split('\t')
+                # #print line_split
+                # if line_split[1] == rows[0]:
+                #     DP_field = line_split[7].split(';')
+                #     DP_value = DP_field[0].replace('DP=', '')
+                #print out
+            else:
+                filtered_f2.write("NA\n")
+                #print "NA"
+
+    #os.system(cmd) change
+    #subprocess.call(["%s" % cmd], shell=True)
+    #subprocess.check_call('%s' % cmd)
+
+DP_analysis()
 
-DP_analysis()
\ No newline at end of file
+filtered_DP_analysis()
\ No newline at end of file
diff --git a/modules/variant_diagnostics/R_scripts/non_core_variant_qc.R b/modules/variant_diagnostics/R_scripts/non_core_variant_qc.R
new file mode 100755
index 0000000..419cd0e
--- /dev/null
+++ b/modules/variant_diagnostics/R_scripts/non_core_variant_qc.R
@@ -0,0 +1,122 @@
+#' ---
+#' title: "Non-Core Variant Matrix QC"
+#' params:
+#'   mat: NULL
+#' ---
+
+#+ echo=F, warnings=T, message=T
+# Input to script: SNP_matrix_code.csv
+
+# Load libraries
+library(pheatmap)
+library(htmlTable)
+# Load functions
+source('/nfs/esnitkin/bin_group/pipeline/Github/scripts/variant_parser_functions.R')
+
+# Get command line arguments
+#args = commandArgs(trailingOnly = TRUE)
+
+#params = list()
+#params$mat = '/scratch/esnitkin_fluxod/apirani/Project_Penn_KPC/Analysis/Regional_KPC_transmission/2018_01_22_Penn_ST258_variant_calling/2018_12_06_14_33_46_core_results/data_matrix/matrices/SNP_matrix_code.csv'
+
+# Read in and parse variant matrix
+alt_mat = parse_snps(params$mat)
+
+#' Total number of variants:
+
+#+ echo=F, warnings=F, message=T
+nrow(alt_mat$mat)
+
+#' Total number of non-phage variants:
+#+ echo=F, warnings=F, message=T
+sum(rowSums(alt_mat$mat == -2) == 0)
+# htmlTable(matrix(c(-4:3,'filtered (low MQ)','filtered (low FQ)','filtered (phage/repeat region)',
+#                  'unmapped','reference allele','core variant','filtered (DP/QUAL)','non-core variant'),
+#                  ncol=2),
+#           align='c|l',header=c('Number','Description'))
+
+#' What numbers mean: 
+#+ echo=F, warnings=F, message=T
+
+#' | Number | Description   |          
+#' |:------:|:-------------:|
+#' | -4     | filtered (low MQ) | 
+#' | -3     | filtered (low FQ) | 
+#' | -2     | filtered (phage/repeat region) | 
+#' | -1     | unmapped | 
+#' | 0      | reference allele |
+#' | 1      | core variant  | 
+#' | 2      | filtered (DP/QUAL) | 
+#' | 3      | non-core variant |
+
+#+ echo=F, warnings=F, message=T
+var_type_counts = (sapply(as.data.frame(t(alt_mat$mat)), function(x) table(factor(x,levels=c(-1,0,1,2,3,-2,-3,-4)))))
+
+#' Masked variant positions:
+#+ echo=F, warnings=F, message=T
+paste('Phage or repeat region:', sum(var_type_counts[6,] != 0), (sum(var_type_counts[6,] != 0)/length(var_type_counts[1,])))
+paste('Low FQ:', sum(var_type_counts[7,] != 0),(sum(var_type_counts[7,] != 0)/length(var_type_counts[1,])))
+paste('Low MQ:', sum(var_type_counts[8,] != 0),(sum(var_type_counts[8,] != 0)/length(var_type_counts[1,])))
+#' Individual variant positions:
+#+ echo=F, warnings=F, message=T
+paste('Unmapped, filtered, and true:', sum(var_type_counts[1,] != 0 & var_type_counts[4,] != 0), (sum(var_type_counts[1,] != 0 & var_type_counts[4,] != 0)/length(var_type_counts[1,])))
+paste('Only unmapped or true:', sum(var_type_counts[1,] != 0 & var_type_counts[4,] == 0),(sum(var_type_counts[1,] != 0 & var_type_counts[4,] == 0)/length(var_type_counts[1,])))
+paste('Only filtered or true:', sum(var_type_counts[1,] == 0 & var_type_counts[4,] != 0),(sum(var_type_counts[1,] == 0 & var_type_counts[4,] != 0)/length(var_type_counts[1,])))
+paste('Only true:', sum(var_type_counts[3,] > 0),(sum(var_type_counts[3,] > 0)/length(var_type_counts[1,])))
+
+# Heatmap function
+heatmap_colors = c('#b35806','#e08214','#fdb863','#fee0b6','#d8daeb','#b2abd2','#8073ac','#542788')
+allele_heatmap = function(mat,col=heatmap_colors){
+  par(mfrow=c(1,1))
+  pos_not_filt_frac = colSums(mat == 3 | mat == 1)/(colSums(mat == 2) + colSums(mat == 3 | mat == 1))
+  names(pos_not_filt_frac) = colnames(mat)
+  genome_filt_count = rowSums(mat == 2)
+  names(genome_filt_count) = rownames(mat)
+  pheatmap(mat,color=col,
+           show_rownames = F, show_colnames = F,
+           annotation_row = as.data.frame(genome_filt_count), annotation_col = as.data.frame(pos_not_filt_frac),
+           cluster_rows = F, cluster_cols = F)
+}
+
+
+#' Heatmap before gubbins recombination filtering. Phage regions masked. x axis is variants (ordered by position in genome), y axis is genomes. Rows are genomes and columns are variants.
+#+ echo=F, warnings=F, message=T
+mat = t(alt_mat$mat)
+colnames(mat) = rownames(alt_mat$mat)
+allele_heatmap(mat)
+allele_heatmap(mat[,colSums(mat == -2) == 0],col=heatmap_colors[c(1,2,4:length(heatmap_colors))])
+
+#' Histogram of number genomes with each variant. Phage regions masked.
+#+ echo=F, warnings=F, message=T
+hist(colSums(mat == 3 | mat == 1),1000,main='',xlab='Number of genomes with variant')
+
+#' Heatmap before gubbins recombination filtering. Phage regions and variants positions filtered because of low FQ masked.
+#+ echo=F, warnings=T, message=T
+# Mask variants with low FQ (-3) 
+mat_maskFQ = mat
+mat_maskFQ[,colSums(mat == -3) != 0] = -3
+#mat_maskFQ[mat == -4] = -2.5
+allele_heatmap(mat_maskFQ)
+allele_heatmap(mat_maskFQ[,colSums(mat_maskFQ == -2) == 0 & colSums(mat_maskFQ == -3) == 0],col=heatmap_colors[c(1,4:length(heatmap_colors))])
+
+#' Histogram of number genomes with each variant. Phage regions and variants positions filtered because of low FQ masked.
+#+ echo=F, warnings=T, message=T
+hist(alt_mat$pos[colSums(mat == -3) != 0],1000,main='',xlab='Low FQ positions across genome')
+hist(colSums(mat_maskFQ == 3 | mat_maskFQ == 1),1000,main='',xlab='Number of genomes with variant')
+
+#' Heatmap before gubbins recombination filtering. Phage regions and variants positions filtered because of low FQ and low MQ masked.
+#+ echo=F, warnings=T, message=T
+# Mask variants with low MQ (-4) 
+mat_maskMQ = mat_maskFQ
+mat_maskMQ[,colSums(mat == -4) != 0] = -4
+allele_heatmap(mat_maskMQ)
+allele_heatmap(mat_maskMQ[,colSums(mat_maskMQ == -2) == 0 & colSums(mat_maskMQ == -3) == 0 & colSums(mat_maskMQ == -4) == 0],col=heatmap_colors[4:length(heatmap_colors)])
+
+#' Histogram of number genomes with each variant. Phage regions and variants positions filtered because of low FQ and low MQ masked.
+#+ echo=F, warnings=T, message=T
+hist(alt_mat$pos[colSums(mat == -4) != 0],1000,main='',xlab='Low MQ positions across genome')
+hist(colSums(mat_maskMQ == 3 | mat_maskMQ == 1),1000,main='',xlab='Number of genomes with variant')
+
+
+
+
diff --git a/modules/variant_diagnostics/R_scripts/render_non_core_variant_qc.R b/modules/variant_diagnostics/R_scripts/render_non_core_variant_qc.R
new file mode 100755
index 0000000..bd4e5fe
--- /dev/null
+++ b/modules/variant_diagnostics/R_scripts/render_non_core_variant_qc.R
@@ -0,0 +1,12 @@
+# Get command line arguments
+args = commandArgs(trailingOnly = TRUE)
+
+if(length(args) == 2){
+  pref=paste0(args[[2]],'_')
+}else{
+  pref=''
+}
+
+rmarkdown::render('non_core_variant_qc.R', 
+                  params=list(mat=args[[1]]),
+                  output_file=paste0(pref,"non_core_variant_qc.html"))
diff --git a/modules/variant_diagnostics/core_pipeline.py b/modules/variant_diagnostics/core_pipeline.py
index 2a44261..0ded491 100644
--- a/modules/variant_diagnostics/core_pipeline.py
+++ b/modules/variant_diagnostics/core_pipeline.py
@@ -6,7 +6,7 @@
 import csv
 import subprocess
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-""" Hacky way yo append. Instead Add this path to PYTHONPATH Variable """
+""" Hacky way to append. Instead Add this path to PYTHONPATH Variable """
 from collections import OrderedDict
 from collections import defaultdict
 from joblib import Parallel, delayed
@@ -36,7 +36,7 @@
 from pyfasta import Fasta
 from core_prep_sanity_checks import *
 from iqtree import iqtree
-#from core_prep import *
+
 
 
 # Parse Command line Arguments
@@ -75,12 +75,17 @@
                     help='yes/no for debug mode')
 args = parser.parse_args()
 
+""" Generic Methods """
 def make_sure_path_exists(out_path):
-    """
-    Fuction to make sure output folder exists. If not, create it.
-    :param: out_path
+    """This function checks if the args out_path exists and generates an empty directory if it doesn't.
+
+    :param:
+        out_path: Directory path to check or create a new directory.
+
     :return: null/exception
+
     """
+
     try:
         os.makedirs(out_path)
     except OSError as exception:
@@ -91,16 +96,30 @@ def make_sure_path_exists(out_path):
             exit()
 
 def run_command(i):
+    """Function to run each command and is run as a part of python Parallel mutiprocessing method.
+
+    :param:
+        i: command variable to run
+
+    :return:
+        done: string variable with completion status of command.
     """
-    Function to run each command and is run as a part of python Parallel mutiprocessing method.
-    :param: command
-    :return: Done status
-    """
+
     call("%s" % i, logger)
+    # A subprocess exception is raised if the command finish abnormally.
+    # An exception is raised in call method.
+    # If none of the exceptions are raised, return done status.
     done = "Completed: %s" % i
     return done
 
-""" core_prep methods """
+"""core_prep methods 
+    
+    This block contains methods that are respnsible for running the first part of core_All step of the pipeline.
+    This methods generates all the necessary intermediate files required for the second part of core_All step.
+    Example of intermediate files: various diagnostics files/matrices where it decides why a variant was filtered out.
+
+"""
+
 def create_positions_filestep(vcf_filenames):
 
     """
@@ -366,7 +385,7 @@ def create_indel_job(jobrun, vcf_filenames, unique_position_file, tmp_dir):
 
         for i in vcf_filenames:
             job_name = os.path.basename(i)
-            job_print_string = "#PBS -N %s\n#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n\n/nfs/esnitkin/bin_group/anaconda2/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/reason_job_indel_debug.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_file %s -unique_position_file %s -tmp_dir %s\n" % (job_name, ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'], args.filter2_only_snp_vcf_dir, i, unique_position_file, tmp_dir)
+            job_print_string = "#PBS -N %s\n#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n\n/nfs/esnitkin/bin_group/anaconda2/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/reason_job_indel_debug_gatk.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_file %s -unique_position_file %s -tmp_dir %s\n" % (job_name, ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'], args.filter2_only_snp_vcf_dir, i, unique_position_file, tmp_dir)
             job_file_name = "%s_indel.pbs" % (i)
             f1=open(job_file_name, 'w+')
             f1.write(job_print_string)
@@ -424,7 +443,15 @@ def create_indel_job(jobrun, vcf_filenames, unique_position_file, tmp_dir):
         call("bash %s" % command_file, logger)
 
 
-""" core methods """
+"""core methods 
+
+    This block contains methods that are respnsible for running the second part of core_All step of the pipeline.
+    It uses intermediate files generated during the first step, finds core SNPs and annotates variants using snpEff.
+    It will generate all types of SNP matrices that is required for downstream pathways / Association analysis.
+    Output:
+        - 
+
+"""
 
 def generate_paste_command():
     """
@@ -978,7 +1005,12 @@ def temp_generate_indel_position_label_data_matrix_All_label():
             print_string_header = print_string_header + os.path.basename(i) + "\t"
         f33.write('\t' + print_string_header.strip() + '\n')
         keep_logging('Reading temporary label positions file: %s/temp_label_final_raw.txt' % args.filter2_only_snp_vcf_dir, 'Reading temporary label positions file: %s/temp_label_final_raw.txt' % args.filter2_only_snp_vcf_dir, logger, 'info')
-        lll = ['reference_unmapped_position', 'LowFQ', 'LowFQ_DP', 'LowFQ_QUAL', 'LowFQ_DP_QUAL', 'LowFQ_QUAL_DP', 'HighFQ_DP', 'HighFQ_QUAL', 'HighFQ_DP_QUAL', 'HighFQ_QUAL_DP', 'HighFQ', 'LowFQ_proximate_SNP', 'LowFQ_DP_proximate_SNP', 'LowFQ_QUAL_proximate_SNP', 'LowFQ_DP_QUAL_proximate_SNP', 'LowFQ_QUAL_DP_proximate_SNP', 'HighFQ_DP_proximate_SNP', 'HighFQ_QUAL_proximate_SNP', 'HighFQ_DP_QUAL_proximate_SNP', 'HighFQ_QUAL_DP_proximate_SNP', 'HighFQ_proximate_SNP', '_proximate_SNP']
+        # lll = ['reference_unmapped_position', 'LowFQ', 'LowFQ_DP', 'LowFQ_QUAL', 'LowFQ_DP_QUAL', 'LowFQ_QUAL_DP', 'HighFQ_DP', 'HighFQ_QUAL', 'HighFQ_DP_QUAL', 'HighFQ_QUAL_DP', 'HighFQ', 'LowFQ_proximate_SNP', 'LowFQ_DP_proximate_SNP', 'LowFQ_QUAL_proximate_SNP', 'LowFQ_DP_QUAL_proximate_SNP', 'LowFQ_QUAL_DP_proximate_SNP', 'HighFQ_DP_proximate_SNP', 'HighFQ_QUAL_proximate_SNP', 'HighFQ_DP_QUAL_proximate_SNP', 'HighFQ_QUAL_DP_proximate_SNP', 'HighFQ_proximate_SNP', '_proximate_SNP']
+        lll = ['reference_unmapped_position', 'LowAF', 'LowAF_DP', 'LowAF_QUAL', 'LowAF_DP_QUAL', 'LowAF_QUAL_DP',
+               'HighAF_DP', 'HighAF_QUAL', 'HighAF_DP_QUAL', 'HighAF_QUAL_DP', 'HighAF', 'LowAF_proximate_SNP',
+               'LowAF_DP_proximate_SNP', 'LowAF_QUAL_proximate_SNP', 'LowAF_DP_QUAL_proximate_SNP',
+               'LowAF_QUAL_DP_proximate_SNP', 'HighAF_DP_proximate_SNP', 'HighAF_QUAL_proximate_SNP',
+               'HighAF_DP_QUAL_proximate_SNP', 'HighAF_QUAL_DP_proximate_SNP', 'HighAF_proximate_SNP', '_proximate_SNP']
         ref_var = ['reference_allele', 'VARIANT']
         with open("%s/temp_indel_label_final_raw.txt" % args.filter2_only_snp_vcf_dir, 'r') as csv_file:
             csv_reader = csv.reader(csv_file, delimiter='\t')
@@ -994,26 +1026,26 @@ def temp_generate_indel_position_label_data_matrix_All_label():
         csv_file.close()
         f33.close()
         """
-        Read temp_Only_filtered_positions_for_closely_matrix file and generate a matrix of positions that are being filtered just because of FQ
+        Read temp_Only_filtered_positions_for_closely_matrix file and generate a matrix of positions that are being filtered just because of AF
         """
-        temp_position_label_FQ = OrderedDict()
-        f44=open("%s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir, 'w+')
+        temp_position_label_AF = OrderedDict()
+        f44=open("%s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir, 'w+')
         with open("%s/temp_Only_filtered_indel_positions_for_closely_matrix.txt" % args.filter2_only_snp_vcf_dir, 'rU') as csv_file:
             keep_logging('Reading temporary Only_filtered_indel_positions label file: %s/temp_Only_filtered_indel_positions_for_closely_matrix.txt ' % args.filter2_only_snp_vcf_dir, 'Reading temporary Only_filtered_indel_positions label file: %s/temp_Only_filtered_indel_positions_for_closely_matrix.txt ' % args.filter2_only_snp_vcf_dir, logger, 'info')
             csv_reader = csv.reader(csv_file, delimiter='\t')
             next(csv_reader, None)
 
             for row in csv_reader:
-              temp_position_label_FQ[row[0]] = row[1:]
+              temp_position_label_AF[row[0]] = row[1:]
             print_string_header = "\t"
             for i in vcf_filenames:
               print_string_header = print_string_header + os.path.basename(i) + "\t"
             f44.write('\t' + print_string_header.strip() + '\n')
-            for value in temp_position_label_FQ:
-              lll = ['LowFQ']
-              if set(lll) & set(temp_position_label_FQ[value]):
+            for value in temp_position_label_AF:
+              lll = ['LowAF']
+              if set(lll) & set(temp_position_label_AF[value]):
                   print_string = ""
-                  for i in temp_position_label_FQ[value]:
+                  for i in temp_position_label_AF[value]:
                       print_string = print_string + "\t" + i
                   STRR2 = value + print_string + "\n"
                   f44.write(STRR2)
@@ -1024,30 +1056,30 @@ def temp_generate_indel_position_label_data_matrix_All_label():
         """
         Perform Sed on temp files. Find a faster way to do this.
         """
-        subprocess.call(["sed -i 's/_filter2_final.vcf_no_proximate_snp.vcf//g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/reference_unmapped_position/0/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/reference_allele/1/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/VARIANT/2/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_QUAL_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_DP_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_QUAL_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_DP_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_QUAL_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_DP_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_QUAL_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_DP_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ/3/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_FQ.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/_filter2_final.vcf_no_proximate_snp.vcf//g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/reference_unmapped_position/0/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/reference_allele/1/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/VARIANT/2/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_QUAL_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_DP_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_QUAL_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_DP_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_QUAL_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_DP_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_QUAL_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_DP_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF/3/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_AF.txt" % args.filter2_only_snp_vcf_dir], shell=True)
 
 
         """
@@ -1066,11 +1098,11 @@ def temp_generate_indel_position_label_data_matrix_All_label():
                 print_string_header = print_string_header + os.path.basename(i) + "\t"
             f44.write('\t' + print_string_header.strip() + '\n')
             for value in temp_position_label_DP:
-                lll = ['HighFQ_DP']
+                lll = ['HighAF_DP']
                 ref_var = ['reference_allele', 'VARIANT']
-                if set(lll) & set(temp_position_label_FQ[value]):
+                if set(lll) & set(temp_position_label_AF[value]):
                     print_string = ""
-                    for i in temp_position_label_FQ[value]:
+                    for i in temp_position_label_AF[value]:
                         print_string = print_string + "\t" + i
                     STRR2 = value + print_string + "\n"
                     f44.write(STRR2)
@@ -1084,26 +1116,26 @@ def temp_generate_indel_position_label_data_matrix_All_label():
         subprocess.call(["sed -i 's/reference_unmapped_position/0/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
         subprocess.call(["sed -i 's/reference_allele/1/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
         subprocess.call(["sed -i 's/VARIANT/2/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_QUAL_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_DP_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_QUAL_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_DP_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_QUAL_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_DP_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_QUAL_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_DP_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ_DP/3/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/LowFQ/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
-        subprocess.call(["sed -i 's/HighFQ/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_QUAL_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_DP_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_QUAL_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_DP_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_QUAL_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_DP_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_QUAL_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_DP_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_proximate_SNP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_QUAL_DP/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_DP_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_QUAL/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF_DP/3/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/LowAF/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
+        subprocess.call(["sed -i 's/HighAF/4/g' %s/temp_Only_filtered_indel_positions_for_closely_matrix_DP.txt" % args.filter2_only_snp_vcf_dir], shell=True)
 
 
     def barplot_indel_stats():
@@ -1120,22 +1152,22 @@ def barplot_indel_stats():
         end = len(vcf_filenames) + 1
         f_bar_count = open("%s/bargraph_indel_counts.txt" % args.filter2_only_snp_vcf_dir, 'w+')
         f_bar_perc = open("%s/bargraph_indel_percentage.txt" % args.filter2_only_snp_vcf_dir, 'w+')
-        f_bar_count.write("Sample\tunmapped_positions\treference_allele\ttrue_variant\tOnly_low_FQ\tOnly_DP\tOnly_low_MQ\tother\n")
-        f_bar_perc.write("Sample\tunmapped_positions_perc\ttrue_variant_perc\tOnly_low_FQ_perc\tOnly_DP_perc\tOnly_low_MQ_perc\tother_perc\n")
+        f_bar_count.write("Sample\tunmapped_positions\treference_allele\ttrue_variant\tOnly_low_AF\tOnly_DP\tOnly_low_MQ\tother\n")
+        f_bar_perc.write("Sample\tunmapped_positions_perc\ttrue_variant_perc\tOnly_low_AF_perc\tOnly_DP_perc\tOnly_low_MQ_perc\tother_perc\n")
         for i in xrange(1, end, 1):
             """ Bar Count Statistics: Variant Position Count Statistics """
             true_variant = columns[i].count('VARIANT')
             unmapped_positions = columns[i].count('reference_unmapped_position')
             reference_allele = columns[i].count('reference_allele')
-            Only_low_FQ = columns[i].count('LowFQ')
-            Only_DP = columns[i].count('HighFQ_DP')
-            Only_low_MQ = columns[i].count('HighFQ')
-            low_FQ_other_parameters = columns[i].count('LowFQ_QUAL_DP_proximate_SNP') + columns[i].count('LowFQ_DP_QUAL_proximate_SNP') + columns[i].count('LowFQ_QUAL_proximate_SNP') + columns[i].count('LowFQ_DP_proximate_SNP') + columns[i].count('LowFQ_proximate_SNP') + columns[i].count('LowFQ_QUAL_DP') + columns[i].count('LowFQ_DP_QUAL') + columns[i].count('LowFQ_QUAL') + columns[i].count('LowFQ_DP')
-            high_FQ_other_parameters = columns[i].count('HighFQ_QUAL_DP_proximate_SNP') + columns[i].count('HighFQ_DP_QUAL_proximate_SNP') + columns[i].count('HighFQ_QUAL_proximate_SNP') + columns[i].count('HighFQ_DP_proximate_SNP') + columns[i].count('HighFQ_proximate_SNP') + columns[i].count('HighFQ_QUAL_DP') + columns[i].count('HighFQ_DP_QUAL') + columns[i].count('HighFQ_QUAL')
-            other = low_FQ_other_parameters + high_FQ_other_parameters
-            total = true_variant + unmapped_positions + reference_allele + Only_low_FQ + Only_DP + low_FQ_other_parameters + high_FQ_other_parameters + Only_low_MQ
+            Only_low_AF = columns[i].count('LowAF')
+            Only_DP = columns[i].count('HighAF_DP')
+            Only_low_MQ = columns[i].count('HighAF')
+            low_AF_other_parameters = columns[i].count('LowAF_QUAL_DP_proximate_SNP') + columns[i].count('LowAF_DP_QUAL_proximate_SNP') + columns[i].count('LowAF_QUAL_proximate_SNP') + columns[i].count('LowAF_DP_proximate_SNP') + columns[i].count('LowAF_proximate_SNP') + columns[i].count('LowAF_QUAL_DP') + columns[i].count('LowAF_DP_QUAL') + columns[i].count('LowAF_QUAL') + columns[i].count('LowAF_DP')
+            high_AF_other_parameters = columns[i].count('HighAF_QUAL_DP_proximate_SNP') + columns[i].count('HighAF_DP_QUAL_proximate_SNP') + columns[i].count('HighAF_QUAL_proximate_SNP') + columns[i].count('HighAF_DP_proximate_SNP') + columns[i].count('HighAF_proximate_SNP') + columns[i].count('HighAF_QUAL_DP') + columns[i].count('HighAF_DP_QUAL') + columns[i].count('HighAF_QUAL')
+            other = low_AF_other_parameters + high_AF_other_parameters
+            total = true_variant + unmapped_positions + reference_allele + Only_low_AF + Only_DP + low_AF_other_parameters + high_AF_other_parameters + Only_low_MQ
             filename_count = i - 1
-            bar_string = "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (os.path.basename(vcf_filenames[filename_count].replace('_filter2_final.vcf_no_proximate_snp.vcf', '')), unmapped_positions, reference_allele, true_variant, Only_low_FQ, Only_DP, Only_low_MQ, other)
+            bar_string = "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (os.path.basename(vcf_filenames[filename_count].replace('_filter2_final.vcf_no_proximate_snp.vcf', '')), unmapped_positions, reference_allele, true_variant, Only_low_AF, Only_DP, Only_low_MQ, other)
             f_bar_count.write(bar_string)
 
             """ Bar Count Percentage Statistics: Variant Position Percentage Statistics """
@@ -1152,28 +1184,28 @@ def barplot_indel_stats():
             except ZeroDivisionError:
                 reference_allele_perc = 0
             try:
-                Only_low_FQ_perc = float((columns[i].count('LowFQ') * 100) / total)
+                Only_low_AF_perc = float((columns[i].count('LowAF') * 100) / total)
             except ZeroDivisionError:
-                Only_low_FQ_perc = 0
+                Only_low_AF_perc = 0
             try:
-                Only_DP_perc = float((columns[i].count('HighFQ_DP') * 100) / total)
+                Only_DP_perc = float((columns[i].count('HighAF_DP') * 100) / total)
             except ZeroDivisionError:
                 Only_DP_perc = 0
             try:
-                Only_low_MQ_perc = float((columns[i].count('HighFQ') * 100) / total)
+                Only_low_MQ_perc = float((columns[i].count('HighAF') * 100) / total)
             except ZeroDivisionError:
                 Only_low_MQ_perc = 0
             try:
-                low_FQ_other_parameters_perc = float(((columns[i].count('LowFQ_QUAL_DP_proximate_SNP') + columns[i].count('LowFQ_DP_QUAL_proximate_SNP') + columns[i].count('LowFQ_QUAL_proximate_SNP') + columns[i].count('LowFQ_DP_proximate_SNP') + columns[i].count('LowFQ_proximate_SNP') + columns[i].count('LowFQ_QUAL_DP') + columns[i].count('LowFQ_DP_QUAL') + columns[i].count('LowFQ_QUAL') + columns[i].count('LowFQ_DP'))  * 100) / total)
+                low_AF_other_parameters_perc = float(((columns[i].count('LowAF_QUAL_DP_proximate_SNP') + columns[i].count('LowAF_DP_QUAL_proximate_SNP') + columns[i].count('LowAF_QUAL_proximate_SNP') + columns[i].count('LowAF_DP_proximate_SNP') + columns[i].count('LowAF_proximate_SNP') + columns[i].count('LowAF_QUAL_DP') + columns[i].count('LowAF_DP_QUAL') + columns[i].count('LowAF_QUAL') + columns[i].count('LowAF_DP'))  * 100) / total)
             except ZeroDivisionError:
-                low_FQ_other_parameters_perc = 0
+                low_AF_other_parameters_perc = 0
             try:
-                high_FQ_other_parameters_perc = float(((columns[i].count('HighFQ_QUAL_DP_proximate_SNP') + columns[i].count('HighFQ_DP_QUAL_proximate_SNP') + columns[i].count('HighFQ_QUAL_proximate_SNP') + columns[i].count('HighFQ_DP_proximate_SNP') + columns[i].count('HighFQ_proximate_SNP') + columns[i].count('HighFQ_QUAL_DP') + columns[i].count('HighFQ_DP_QUAL') + columns[i].count('HighFQ_QUAL')) * 100) / total)
+                high_AF_other_parameters_perc = float(((columns[i].count('HighAF_QUAL_DP_proximate_SNP') + columns[i].count('HighAF_DP_QUAL_proximate_SNP') + columns[i].count('HighAF_QUAL_proximate_SNP') + columns[i].count('HighAF_DP_proximate_SNP') + columns[i].count('HighAF_proximate_SNP') + columns[i].count('HighAF_QUAL_DP') + columns[i].count('HighAF_DP_QUAL') + columns[i].count('HighAF_QUAL')) * 100) / total)
             except ZeroDivisionError:
-                high_FQ_other_parameters_perc = 0
+                high_AF_other_parameters_perc = 0
 
-            other_perc = float(low_FQ_other_parameters_perc + high_FQ_other_parameters_perc)
-            bar_perc_string = "%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (os.path.basename(vcf_filenames[filename_count].replace('_filter2_final.vcf_no_proximate_snp.vcf', '')), unmapped_positions_perc, true_variant_perc, Only_low_FQ_perc, Only_DP_perc, Only_low_MQ_perc, other_perc)
+            other_perc = float(low_AF_other_parameters_perc + high_AF_other_parameters_perc)
+            bar_perc_string = "%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (os.path.basename(vcf_filenames[filename_count].replace('_filter2_final.vcf_no_proximate_snp.vcf', '')), unmapped_positions_perc, true_variant_perc, Only_low_AF_perc, Only_DP_perc, Only_low_MQ_perc, other_perc)
             f_bar_perc.write(bar_perc_string)
         f_bar_count.close()
         f_bar_perc.close()
@@ -1706,6 +1738,7 @@ def FQ_analysis():
         (out, err) = proc.communicate()
         out = out.strip()
         reference_file = out.split(':')
+        # Change it to multiprocessing
         gatk_filter2_final_vcf_file = gatk_filter2(aln_mpileup_vcf_file, temp_dir, analysis, reference_file[1])
         #print gatk_filter2_final_vcf_file
         gatk_filter2_final_vcf_file_no_proximate_snp = remove_proximate_snps(gatk_filter2_final_vcf_file, temp_dir, analysis, reference_file[1])
@@ -1835,7 +1868,6 @@ def extract_only_ref_variant_fasta_from_reference():
     fp.write(final_fasta_string)
     fp.close()
 
-# This got affected by unsorted unique_positions_file. It is fixed now.
 def extract_only_ref_variant_fasta_from_reference_allele_variant():
     ffp = open("%s/unique_positions_file" % args.filter2_only_snp_vcf_dir).readlines()
     #unique_positions_array = []
@@ -1941,7 +1973,7 @@ def variant_annotation():
         raw_vcf = i.replace('_filter2_final.vcf_no_proximate_snp.vcf', '_aln_mpileup_raw.vcf')
         annotate_vcf_cmd = "java -Xmx4g -jar %s/%s/%s -csvStats %s_ANN.csv -dataDir %s/%s/data/ %s -c %s/snpEff.config %s %s > %s_ANN.vcf" % \
                            (ConfigSectionMap("bin_path", Config)['binbase'], ConfigSectionMap("snpeff", Config)['snpeff_bin'], ConfigSectionMap("snpeff", Config)['base_cmd'], raw_vcf, ConfigSectionMap("bin_path", Config)['binbase'], ConfigSectionMap("snpeff", Config)['snpeff_bin'], ConfigSectionMap("snpeff", Config)['snpeff_parameters'], args.filter2_only_snp_vcf_dir, snpeffdb, raw_vcf, raw_vcf)
-        #print annotate_vcf_cmd
+        print annotate_vcf_cmd
         annotate_vcf_cmd_array.append(annotate_vcf_cmd)
         final_vcf = i
         annotate_final_vcf_cmd = "java -Xmx4g -jar %s/%s/%s -csvStats %s_ANN.csv -dataDir %s/%s/data/ %s -c %s/snpEff.config %s %s > %s_ANN.vcf" % \
@@ -1978,8 +2010,6 @@ def indel_annotation():
         prepare_snpEff_db(reference_basename)
 
 
-
-
     annotate_vcf_cmd_array = []
     annotate_final_vcf_cmd_array = []
     for i in vcf_filenames:
@@ -2004,14 +2034,11 @@ def gatk_combine_variants(files_gatk, reference, out_path, merged_file_suffix, l
     #files_gatk = "--variant " + ' --variant '.join(vcf_files_array)
     keep_logging("java -jar %s -T CombineVariants -R %s %s -o %s/Final_vcf_gatk%s" % (base_cmd, reference, files_gatk, out_path, merged_file_suffix), "java -jar %s -T CombineVariants -R %s %s -o %s/Final_vcf_gatk%s" % (base_cmd, reference, files_gatk, out_path, merged_file_suffix), logger, 'debug')
     merge_gatk_commands_file = "%s/gatk_merge.sh" % args.filter2_only_snp_vcf_dir
-    with open(merge_gatk_commands_file, 'a+') as fopen:
+    with open(merge_gatk_commands_file, 'w+') as fopen:
         fopen.write("java -jar %s -T CombineVariants -R %s %s -o %s/Final_vcf_gatk%s" % (base_cmd, reference, files_gatk, out_path, merged_file_suffix) + '\n')
     fopen.close()
+    # Commenting out calling gatk combine variants with a custom logging call method, problem with python subprocess, OSError: [Errno 7] Argument list too long
     os.system("bash %s" % merge_gatk_commands_file)
-    # Commenting out calling gatk combine variants, problem with python subprocess, OSError: [Errno 7] Argument list too long
-    #call("java -jar %s -T CombineVariants -R %s %s -o %s/Final_vcf_gatk%s" % (base_cmd, reference, files_gatk, out_path, merged_file_suffix), logger)
-    #keep_logging("java -jar %s -T CombineVariants -R %s --variant %s -o %s/Final_vcf_gatk%s" % (base_cmd, reference, files_gatk, out_path, merged_file_suffix), "java -jar %s -T CombineVariants -R %s %s -o %s/Final_vcf_gatk%s" % (base_cmd, reference, files_gatk, out_path, merged_file_suffix), logger, 'debug')
-    #call("java -jar %s -T CombineVariants -R %s --variant %s -o %s/Final_vcf_gatk%s" % (base_cmd, reference, files_gatk, out_path, merged_file_suffix), logger)
     return "%s/Final_vcf_gatk%s" % (out_path, merged_file_suffix)
 
 def annotated_snp_matrix():
@@ -2021,14 +2048,23 @@ def annotated_snp_matrix():
     This dictionary will then be used to insert annotation into SNP/Indel matrix
     """
 
-    """Run SNPeff on all the raw and filtered vcf files"""
-    """ Run snpEff annotation step """
+    """Annotate all VCF file formats with SNPeff"""
+
     variant_annotation()
 
-    """ Run snpEff annotation step """
     indel_annotation()
 
-    """ Check if Reference genome Genbank file exists. """
+
+    """
+    
+    Extract Annotation information from Genbank file 
+    
+    - Check if Reference genome Genbank file exists.
+    - Initiate dictionaries that maps locus tag to gene name and product. This information will be used for annotating SNP/Indel Matrix
+    - Read the locus tag and gene annotations into a dictionary that maps locus tags to gene name/product name
+    
+    """
+
     reference_basename = (os.path.basename(args.reference)).split(".")
     if os.path.isfile("%s/%s.gbf" % (os.path.dirname(args.reference), reference_basename[0])):
         handle = open("%s/%s.gbf" % (os.path.dirname(args.reference), reference_basename[0]), 'rU')
@@ -2036,20 +2072,22 @@ def annotated_snp_matrix():
         raise IOError('%s/%s.gbf does not exist.' % (os.path.dirname(args.reference), reference_basename[0]))
         exit()
 
-    """ Initiate dictionaries to map locus tag to gene name and product"""
     locus_tag_to_gene_name = {}
     locus_tag_to_product = {}
+    locus_tag_to_strand = {}
     #locus_tag_to_uniprot = {}
     #locus_tag_to_ec_number = {}
 
-    """ Read the locus tag and gene annotations into a dictionary that maps locus tags to gene name/product name """
     keep_logging(
         'Reading annotations from Reference genome genbank file: %s/%s.gbf' % (os.path.dirname(args.reference), reference_basename[0]),
         'Reading annotations from Reference genome genbank file: %s/%s.gbf' % (os.path.dirname(args.reference), reference_basename[0]),
         logger, 'info')
     for record in SeqIO.parse(handle, 'genbank') :
         for feature in record.features:
+            location = str(feature.location)
+            strand = location.split('(')[1].replace(')', '')
             if 'locus_tag' in feature.qualifiers:
+                locus_tag_to_strand[str(feature.qualifiers['locus_tag'][0])] = strand
                 if 'gene' in feature.qualifiers:
                     locus_tag_to_gene_name[str(feature.qualifiers['locus_tag'][0])] = str(feature.qualifiers['gene'][0])
                 else:
@@ -2066,8 +2104,15 @@ def annotated_snp_matrix():
                     'Error: locus_tag specifications for the below feature doesnt exists. Please check the format of genbank file\n%s' % str(feature),
                     logger, 'exception')
 
-    #### Start
-    """ Merge Individual Annotated raw and filtered vcf files to generate a Final merged vcf file that will be used to parse and generate matrix """
+    print locus_tag_to_strand
+
+    """ Start of Merging Step:
+    
+    - Merge Individual Annotated raw and filtered vcf files to generate a Final merged vcf file using Gatk combine variants method.
+    - Parse this merged Final_vcf* file and generate a SNP/Indel matrix 
+    
+    """
+
     keep_logging('Merging Final Annotated VCF files into %s/Final_vcf_no_proximate_snp.vcf using bcftools' % args.filter2_only_snp_vcf_dir, 'Merging Final Annotated VCF files into %s/Final_vcf_no_proximate_snp.vcf using bcftools' % args.filter2_only_snp_vcf_dir, logger, 'info')
 
     files_for_tabix = glob.glob("%s/*.vcf_no_proximate_snp.vcf_ANN.vcf" % args.filter2_only_snp_vcf_dir)
@@ -2080,7 +2125,7 @@ def annotated_snp_matrix():
 
     """ bcftools merging is deprecated. Replaced with GATK combinevariants """
     merge_commands_file = "%s/bcftools_merge.sh" % args.filter2_only_snp_vcf_dir
-    #"_filter2_indel_final.vcf_ANN.vcf.gz")) + '\n')
+
     with open(merge_commands_file, 'w+') as fopen:
         fopen.write("%s/%s/bcftools merge -i ANN:join -m both -o %s/Final_vcf_no_proximate_snp.vcf -O v %s" % (ConfigSectionMap("bin_path", Config)['binbase'], ConfigSectionMap("bcftools", Config)['bcftools_bin'], args.filter2_only_snp_vcf_dir, files.replace("_filter2_final.vcf_no_proximate_snp.vcf", "_filter2_final.vcf_no_proximate_snp.vcf_ANN.vcf.gz")) + '\n')
         fopen.write("%s/%s/bcftools merge -i ANN:join -m both -o %s/Final_vcf_indel.vcf -O v %s" % (ConfigSectionMap("bin_path", Config)['binbase'], ConfigSectionMap("bcftools", Config)['bcftools_bin'], args.filter2_only_snp_vcf_dir,files.replace("_filter2_final.vcf_no_proximate_snp.vcf","_filter2_indel_final.vcf_ANN.vcf.gz")) + '\n')
@@ -2112,11 +2157,9 @@ def annotated_snp_matrix():
         files_gatk = files_gatk + " --variant " + i
     final_gatk_snp_merged_vcf = gatk_combine_variants(files_gatk.replace('_filter2_final.vcf_no_proximate_snp.vcf', '_filter2_final.vcf_no_proximate_snp.vcf_ANN.vcf.gz'), args.reference, args.filter2_only_snp_vcf_dir, merged_file_suffix, logger, Config)
 
-    # Test this merge and annote this merged file
-    merged_file_suffix = "_no_proximate_snp_1.vcf"
-    final_gatk_snp_merged_vcf_1 = gatk_combine_variants(files_gatk,
-                                                      args.reference, args.filter2_only_snp_vcf_dir, merged_file_suffix,
-                                                      logger, Config)
+    # Test this merge and annotate this merged file - Testing Mode Right now.
+    #merged_file_suffix = "_no_proximate_snp_1.vcf"
+    #final_gatk_snp_merged_vcf_1 = gatk_combine_variants(files_gatk,args.reference, args.filter2_only_snp_vcf_dir, merged_file_suffix, logger, Config)
     merged_file_suffix = "_indel.vcf"
     final_gatk_indel_merged_vcf = gatk_combine_variants(files_gatk.replace('_filter2_final.vcf_no_proximate_snp.vcf',
                                                                          '_filter2_indel_final.vcf_ANN.vcf.gz'),
@@ -2127,7 +2170,7 @@ def annotated_snp_matrix():
     files_for_tabix = glob.glob("%s/Final_vcf_*.vcf" % args.filter2_only_snp_vcf_dir)
     tabix(files_for_tabix, "vcf", logger, Config)
 
-    ####end
+    """ End of Merging Step. """
 
 
     """ Extract ANN information from bcftools Final vcf file. (There is a reason why i am using bcftools merged file to extract ANN information) """
@@ -2141,8 +2184,6 @@ def annotated_snp_matrix():
         indel_var_ann_dict[variants.POS] = variants.INFO.get('ANN')
 
 
-    # Generate a header of file names
-
     """ print_string_header will be the column names of SNP matrix. Column names = Sample names"""
     print_string_header = "\t"
     for i in vcf_filenames:
@@ -2189,7 +2230,9 @@ def annotated_snp_matrix():
                     for line in fphage:
                         phage_positions.append(line.strip())
                 fphage.close()
-
+            else:
+                raise IOError('%s/phage_region_positions.txt does not exist.' % args.filter2_only_snp_vcf_dir)
+                exit()
         # GET REPETITIVE REGIONS
         if ConfigSectionMap("functional_filters", Config)['find_repetitive_region'] == "yes":
             repetitive_positions_file = "%s/repeat_region_positions.txt" % args.filter2_only_snp_vcf_dir
@@ -2198,7 +2241,9 @@ def annotated_snp_matrix():
                     for line in frep:
                         repetitive_positions.append(line.strip())
                 frep.close()
-
+            else:
+                raise IOError('%s/repeat_region_positions.txt does not exist.' % args.filter2_only_snp_vcf_dir)
+                exit()
         # GET MASK REGIONS
         if ConfigSectionMap("functional_filters", Config)['mask_region'] == "yes":
             mask_positions_file = "%s/mask_positions.txt" % args.filter2_only_snp_vcf_dir
@@ -2207,6 +2252,9 @@ def annotated_snp_matrix():
                     for line in fmask:
                         mask_positions.append(line.strip())
                 fmask.close()
+            else:
+                raise IOError('%s/mask_positions.txt does not exist.' % args.filter2_only_snp_vcf_dir)
+                exit()
 
 
     """ Read and parse final GATK merged vcf file cyvcf library """
@@ -2219,10 +2267,6 @@ def annotated_snp_matrix():
         header_print_string = header_print_string + "\t" + sample
     header_print_string = header_print_string + "\n"
 
-    # header_print_string = header_print_string.replace(':::,', ':::')
-    # changing to tab-delimited
-    #header_print_string = header_print_string.replace(':::,', '\t')
-
 
     """ Prepare a All_indel_label_final_ordered_sorted.txt file with sorted unique variant positions. """
     paste_label_command = "paste %s/unique_positions_file " % args.filter2_only_snp_vcf_dir
@@ -2333,13 +2377,30 @@ def annotated_snp_matrix():
                              'Warning: position %s already present as a SNP' % row[0], logger, 'info')
     csv_file.close()
 
+    """ Generate mask_fq_mq_positions array with positions where a variant was filtered because of LowFQ or LowMQ"""
+    mask_fq_mq_positions = []
+    for key in position_label.keys():
+        label_sep_array = position_label[key].split(',')
+        for i in label_sep_array:
+            if "LowFQ" in i:
+                if key not in mask_fq_mq_positions:
+                    mask_fq_mq_positions.append(key)
+            if i == "HighFQ":
+                if key not in mask_fq_mq_positions:
+                    mask_fq_mq_positions.append(key)
+
+    print "Length of mask_fq_mq_positions array:%s" % len(mask_fq_mq_positions)
+
+
     """ Open Matrix files to write strings """
     fp_code = open("%s/SNP_matrix_code.csv" % args.filter2_only_snp_vcf_dir, 'w+')
-    fp_allele = open("%s/SNP_matrix_allele.csv" % args.filter2_only_snp_vcf_dir, 'w+')
+    fp_allele = open("%s/SNP_matrix_allele_outdated.csv" % args.filter2_only_snp_vcf_dir, 'w+')
     fp_allele_new = open("%s/SNP_matrix_allele_new.csv" % args.filter2_only_snp_vcf_dir, 'w+')
+    fp_allele_new_phage = open("%s/SNP_matrix_allele_unmasked.csv" % args.filter2_only_snp_vcf_dir, 'w+')
     fp_code.write(header_print_string)
     fp_allele.write(header_print_string)
     fp_allele_new.write(header_print_string)
+    fp_allele_new_phage.write(header_print_string)
 
     """ Parse variant positions from the loaded cyvcf VCF object and generate the matrix row information """
     for variants in VCF("%s/Final_vcf_gatk_no_proximate_snp.vcf.gz" % args.filter2_only_snp_vcf_dir):
@@ -2369,15 +2430,17 @@ def annotated_snp_matrix():
         code_string = position_label[str(variants.POS)]
         code_string = code_string.replace('reference_allele', '0')
         code_string = code_string.replace('reference_unmapped_position', '-1')
-        code_string = code_string.replace('LowFQ_QUAL_DP_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_DP_QUAL_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_QUAL_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_DP_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_QUAL_DP', '2')
-        code_string = code_string.replace('LowFQ_DP_QUAL', '2')
-        code_string = code_string.replace('LowFQ_QUAL', '2')
-        code_string = code_string.replace('LowFQ_DP', '2')
+        # Changing LowFQ code from 2 to -3
+        # Changing HighFQ but LowMQ code from 2 to -4
+        code_string = code_string.replace('LowFQ_QUAL_DP_proximate_SNP', '-3')
+        code_string = code_string.replace('LowFQ_DP_QUAL_proximate_SNP', '-3')
+        code_string = code_string.replace('LowFQ_QUAL_proximate_SNP', '-3')
+        code_string = code_string.replace('LowFQ_DP_proximate_SNP', '-3')
+        code_string = code_string.replace('LowFQ_proximate_SNP', '-3')
+        code_string = code_string.replace('LowFQ_QUAL_DP', '-3')
+        code_string = code_string.replace('LowFQ_DP_QUAL', '-3')
+        code_string = code_string.replace('LowFQ_QUAL', '-3')
+        code_string = code_string.replace('LowFQ_DP', '-3')
         code_string = code_string.replace('HighFQ_QUAL_DP_proximate_SNP', '2')
         code_string = code_string.replace('HighFQ_DP_QUAL_proximate_SNP', '2')
         code_string = code_string.replace('HighFQ_QUAL_proximate_SNP', '2')
@@ -2387,15 +2450,16 @@ def annotated_snp_matrix():
         code_string = code_string.replace('HighFQ_DP_QUAL', '2')
         code_string = code_string.replace('HighFQ_QUAL', '2')
         code_string = code_string.replace('HighFQ_DP', '2')
-        code_string = code_string.replace('LowFQ', '2')
-        code_string = code_string.replace('HighFQ', '2')
+        code_string = code_string.replace('LowFQ', '-3')
+        code_string = code_string.replace('HighFQ', '-4')
 
 
         if str(variants.POS) in core_positions:
             code_string = code_string.replace('VARIANT', '1')
         # Adding functional class status code to SNP matrix: 2018-07-24
         elif str(variants.POS) in functional_filter_pos_array:
-            code_string = code_string.replace('VARIANT', '2')
+            # Changing Functional class filter code to -2 from 2: 2018-12-04
+            code_string = code_string.replace('VARIANT', '-2')
         else:
             code_string = code_string.replace('VARIANT', '3')
 
@@ -2409,8 +2473,8 @@ def annotated_snp_matrix():
             if len(variants.ALT) > 1 and snp_var_ann_dict[variants.POS]:
                 #print variants.ALT
                 #print ';'.join(set(snp_var_ann_dict[variants.POS].split(',')))
-                print variants.POS
-                print set(snp_var_ann_dict[variants.POS])
+                #print variants.POS
+                #print set(snp_var_ann_dict[variants.POS])
                 if "protein_coding" in set(snp_var_ann_dict[variants.POS].split(',')):
                     snp_type = "Coding SNP"
                 else:
@@ -2448,6 +2512,7 @@ def annotated_snp_matrix():
                             extra_tags_prot = extra_tags_prot + "None" + ","
                     ann_string = ann_string + '|'.join([i_split[0],i_split[1],i_split[2],i_split[3],i_split[9], i_split[10], i_split[11], i_split[13], extra_tags, extra_tags_prot]) + ";"
                 elif tag == "":
+                    print "ERROR: Issues with this locus tag. Check this tag in genbank file"
                     print list(set(ann_array))
                 else:
                     if tag in locus_tag_to_gene_name.keys() and tag in locus_tag_to_product.keys():
@@ -2463,19 +2528,166 @@ def annotated_snp_matrix():
                 #print ';'.join(set(snp_var_ann_dict[variants.POS].split(',')))
 
                 ann_string = ";%s" % ';'.join(set(snp_var_ann_dict[variants.POS].split(',')))
-                print snp_var_ann_dict[variants.POS].split(',')
-                print ann_string
+                #print snp_var_ann_dict[variants.POS].split(',')
+                #print ann_string
             else:
                 ann_string = ";None"
 
-        # print_string generator no. 3
-        print_string = print_string + " locus_tag=" + tag + ann_string
+
+
+        # SNP Matrix Bug
         ann_string_split = ann_string.split(';')
-        ann_string_split_2 = ann_string_split[0].split('|')
-        
-        if len(ann_string_split_2) > 10 and len(ann_string_split_2) != 20:
-            print ann_string
-            print len(ann_string_split)
+        #print len(ann_string_split)
+        if len(ann_string_split) == 3:
+            first_allele_ann_string_split = ann_string_split[1].split('|')
+            second_allele_ann_string_split = ann_string_split[2].split('|')
+            # if "KPNIH1_26205" in first_allele_ann_string_split:
+            #     #print len(first_allele_ann_string_split)
+            #     print ann_string
+            if len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) == 10:
+                ann_string = ann_string
+            elif len(first_allele_ann_string_split) > 10 and len(second_allele_ann_string_split) == 10:
+                # if "KPNIH1_26205" in first_allele_ann_string_split:
+                #     print len(first_allele_ann_string_split)
+                if first_allele_ann_string_split[14] == "" and first_allele_ann_string_split[15] == "":
+                    prod = first_allele_ann_string_split[3] + first_allele_ann_string_split[15]
+                else:
+                    prod = first_allele_ann_string_split[14] + "|" + first_allele_ann_string_split[15]
+                new_first_allele_ann_string = ";" + first_allele_ann_string_split[0] + "|" + first_allele_ann_string_split[1] + "|" + first_allele_ann_string_split[2] + "|" + first_allele_ann_string_split[4] + "|" + first_allele_ann_string_split[9] + "|" + first_allele_ann_string_split[10] + "|" + first_allele_ann_string_split[11] + "|" + first_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                ann_string = new_first_allele_ann_string + str(ann_string_split[2])
+
+            elif len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) > 10:
+
+                if second_allele_ann_string_split[14] == "" and second_allele_ann_string_split[15] == "":
+                    prod = second_allele_ann_string_split[3] + second_allele_ann_string_split[15]
+                else:
+                    prod = second_allele_ann_string_split[14] + "|" + second_allele_ann_string_split[15]
+                new_second_allele_ann_string = second_allele_ann_string_split[0] + "|" + second_allele_ann_string_split[1] + "|" + second_allele_ann_string_split[2] + "|" + \
+                second_allele_ann_string_split[4] + "|" + second_allele_ann_string_split[9] + "|" + \
+                second_allele_ann_string_split[10] + "|" + second_allele_ann_string_split[11] + "|" + \
+                second_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                ann_string = str(ann_string_split[1]) + new_second_allele_ann_string
+            elif len(first_allele_ann_string_split) > 10 and len(second_allele_ann_string_split) > 10:
+                #print ann_string
+
+                if first_allele_ann_string_split[14] == "" and first_allele_ann_string_split[15] == "":
+                    prod = first_allele_ann_string_split[3] + first_allele_ann_string_split[15]
+                else:
+                    prod = first_allele_ann_string_split[14] + "|" + first_allele_ann_string_split[15]
+                new_first_allele_ann_string = ";" + first_allele_ann_string_split[0] + "|" + first_allele_ann_string_split[1] + "|" + first_allele_ann_string_split[2] + "|" + first_allele_ann_string_split[4] + "|" + first_allele_ann_string_split[9] + "|" + first_allele_ann_string_split[10] + "|" + first_allele_ann_string_split[11] + "|" + first_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                if second_allele_ann_string_split[14] == "" and second_allele_ann_string_split[15] == "":
+                    prod = second_allele_ann_string_split[3] + second_allele_ann_string_split[15]
+                else:
+                    prod = second_allele_ann_string_split[14] + "|" + second_allele_ann_string_split[15]
+                new_second_allele_ann_string = second_allele_ann_string_split[0] + "|" + second_allele_ann_string_split[1] + "|" + second_allele_ann_string_split[2] + "|" + \
+                second_allele_ann_string_split[4] + "|" + second_allele_ann_string_split[9] + "|" + \
+                second_allele_ann_string_split[10] + "|" + second_allele_ann_string_split[11] + "|" + \
+                second_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                ann_string = new_first_allele_ann_string + new_second_allele_ann_string
+                #print ann_string
+            #print ann_string
+
+        if len(ann_string_split) > 3:
+            #ann_string = ""
+            # print ann_string
+            # print len(ann_string_split)
+            first_allele_ann_string_split = ann_string_split[1].split('|')
+            second_allele_ann_string_split = ann_string_split[2].split('|')
+            third_allele_ann_string_split = ann_string_split[3].split('|')
+            # print first_allele_ann_string_split
+            # print second_allele_ann_string_split
+            if len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) == 10 and len(third_allele_ann_string_split) == 10:
+                ann_string = ann_string
+
+            elif len(first_allele_ann_string_split) > 10 and len(second_allele_ann_string_split) == 10 and len(third_allele_ann_string_split) == 10:
+                if first_allele_ann_string_split[14] == "" and first_allele_ann_string_split[15] == "":
+                    prod = first_allele_ann_string_split[3] + first_allele_ann_string_split[15]
+                else:
+                    prod = first_allele_ann_string_split[14] + "|" + first_allele_ann_string_split[15]
+                new_first_allele_ann_string = ";" + first_allele_ann_string_split[0] + "|" + first_allele_ann_string_split[1] + "|" + first_allele_ann_string_split[2] + "|" + first_allele_ann_string_split[4] + "|" + first_allele_ann_string_split[9] + "|" + first_allele_ann_string_split[10] + "|" + first_allele_ann_string_split[11] + "|" + first_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                ann_string = new_first_allele_ann_string + str(ann_string_split[2]) + str(ann_string_split[3])
+
+            elif len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) > 10 and len(third_allele_ann_string_split) == 10:
+
+                if second_allele_ann_string_split[14] == "" and second_allele_ann_string_split[15] == "":
+                    prod = second_allele_ann_string_split[3] + second_allele_ann_string_split[15]
+                else:
+                    prod = second_allele_ann_string_split[14] + "|" + second_allele_ann_string_split[15]
+                new_second_allele_ann_string = second_allele_ann_string_split[0] + "|" + second_allele_ann_string_split[1] + "|" + second_allele_ann_string_split[2] + "|" + \
+                second_allele_ann_string_split[4] + "|" + second_allele_ann_string_split[9] + "|" + \
+                second_allele_ann_string_split[10] + "|" + second_allele_ann_string_split[11] + "|" + \
+                second_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                ann_string = str(ann_string_split[1]) + new_second_allele_ann_string + str(ann_string_split[3])
+
+            elif len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) == 10 and len(third_allele_ann_string_split) > 10:
+
+                if third_allele_ann_string_split[14] == "" and third_allele_ann_string_split[15] == "":
+                    prod = third_allele_ann_string_split[3] + third_allele_ann_string_split[15]
+                else:
+                    prod = third_allele_ann_string_split[14] + "|" + third_allele_ann_string_split[15]
+                new_third_allele_ann_string = third_allele_ann_string_split[0] + "|" + third_allele_ann_string_split[1] + "|" + third_allele_ann_string_split[2] + "|" + \
+                                              third_allele_ann_string_split[4] + "|" + third_allele_ann_string_split[9] + "|" + \
+                                              third_allele_ann_string_split[10] + "|" + third_allele_ann_string_split[11] + "|" + \
+                                              third_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                ann_string = str(ann_string_split[1]) + str(ann_string_split[2]) + new_third_allele_ann_string
+
+            elif len(first_allele_ann_string_split) > 10 and len(second_allele_ann_string_split) > 10 and len(third_allele_ann_string_split) > 10:
+                #print ann_string
+                if first_allele_ann_string_split[14] == "" and first_allele_ann_string_split[15] == "":
+                    prod = first_allele_ann_string_split[3] + first_allele_ann_string_split[15]
+                else:
+                    prod = first_allele_ann_string_split[14] + "|" + first_allele_ann_string_split[15]
+                new_first_allele_ann_string = ";" + first_allele_ann_string_split[0] + "|" + first_allele_ann_string_split[1] + "|" + first_allele_ann_string_split[2] + "|" + first_allele_ann_string_split[4] + "|" + first_allele_ann_string_split[9] + "|" + first_allele_ann_string_split[10] + "|" + first_allele_ann_string_split[11] + "|" + first_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                if second_allele_ann_string_split[14] == "" and second_allele_ann_string_split[15] == "":
+                    prod = second_allele_ann_string_split[3] + second_allele_ann_string_split[15]
+                else:
+                    prod = second_allele_ann_string_split[14] + "|" + second_allele_ann_string_split[15]
+                new_second_allele_ann_string = second_allele_ann_string_split[0] + "|" + second_allele_ann_string_split[1] + "|" + second_allele_ann_string_split[2] + "|" + \
+                second_allele_ann_string_split[4] + "|" + second_allele_ann_string_split[9] + "|" + \
+                second_allele_ann_string_split[10] + "|" + second_allele_ann_string_split[11] + "|" + \
+                second_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                if third_allele_ann_string_split[14] == "" and third_allele_ann_string_split[15] == "":
+                    prod = third_allele_ann_string_split[3] + third_allele_ann_string_split[15]
+                else:
+                    prod = third_allele_ann_string_split[14] + "|" + third_allele_ann_string_split[15]
+                new_third_allele_ann_string = third_allele_ann_string_split[0] + "|" + third_allele_ann_string_split[1] + "|" + third_allele_ann_string_split[2] + "|" + \
+                                              third_allele_ann_string_split[4] + "|" + third_allele_ann_string_split[9] + "|" + \
+                                              third_allele_ann_string_split[10] + "|" + third_allele_ann_string_split[11] + "|" + \
+                                              third_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                ann_string = first_allele_ann_string_split + second_allele_ann_string_split + new_third_allele_ann_string
+
+                #print ann_string
+
+
+        # if len(ann_string_split) > 4:
+        #     print ann_string
+
+        # ann_string_split = ann_string.split(';')
+        # for i in ann_string_split:
+        #     if len(i.split('|')) != 10 and len(i.split('|')) != 1:
+        #         print i
+
+        # print_string generator no. 3
+
+        strandness = " strand="
+        if "-" in tag:
+            tagsplit = tag.split('-')
+            for i in tagsplit:
+                strandness = strandness + locus_tag_to_strand[i] + "/"
+        else:
+            strandness = strandness + locus_tag_to_strand[tag]
+        print_string = print_string + " locus_tag=" + tag + strandness + ann_string
+        print_string_phage = print_string
 
         """ Go over each genotype for a variant and generate a gt_string variable """
         gt_string = ""
@@ -2496,6 +2708,14 @@ def annotated_snp_matrix():
         # print_string generator no. 4
         # Replace various seperators that were used in old matrix. Clean up this block of code
         final_allele_string = print_string + gt_string.replace(',', '\t') + '\n'
+        # Replace code at Phage Positions with -2
+        if str(variants.POS) in functional_filter_pos_array:
+            code_string_array = code_string.split(',')
+            code_string = ""
+            for i in code_string_array:
+                code_string = code_string + "," + "-2"
+
+
         final_code_string = print_string + "\t" + code_string.replace(',', '\t') + '\n'
         final_allele_string = final_allele_string.replace(',|', '|')
         # final_allele_string = final_allele_string.replace(',;,', ':::')
@@ -2507,6 +2727,9 @@ def annotated_snp_matrix():
         # final_code_string = final_code_string.replace(';,', ':::')
         final_code_string = final_code_string.replace(',;,', ':::')
         final_code_string = final_code_string.replace(';,', ':::')
+        final_code_string = final_code_string.replace(';\t\t', ';\t')
+        final_code_string = final_code_string.replace('\t\t', '\t')
+        final_allele_string = final_allele_string.replace('\t\t', '\t')
         fp_allele.write(final_allele_string)
         fp_code.write(final_code_string)
 
@@ -2514,34 +2737,74 @@ def annotated_snp_matrix():
         # print gt_string[1:]
 
         ntd_string = ""
+        ntd_string_phage = ""
         count = 0
         code_string_array = code_string.split(',')
         gt_string_array = gt_string[1:].split(',')
         # print code_string_array
         # print gt_string_array
         for i in gt_string_array:
-            # Fixing this: 2018-07-21
             if str(code_string_array[count]) == "0" or str(code_string_array[count]) == "1" or str(code_string_array[count]) == "3":
                 ntd_string = ntd_string + "\t" + str(i)
+                ntd_string_phage = ntd_string_phage + "\t" + str(i)
             if code_string_array[count] == "-1":
                 ntd_string = ntd_string + "\t" + "-"
-            # Fixing this: 2018-07-21
-            if str(code_string_array[count]) == "2":
+                ntd_string_phage = ntd_string_phage + "\t" + "-"
+            # Changing Functional class filter code to -2 from 2 and replacing variant allele with N: 2018-12-04
+            if str(code_string_array[count]) == "2" or str(code_string_array[count]) == "-2" or str(code_string_array[count]) == "-3" or str(code_string_array[count]) == "-4":
                 ntd_string = ntd_string + "\t" + "N"
+            if str(code_string_array[count]) == "2":
+                ntd_string_phage = ntd_string_phage + "\t" + "N"
+            if str(code_string_array[count]) == "-2":
+                ntd_string_phage = ntd_string_phage + "\t" + str(i)
             count += 1
 
-        # print_string generator no. 5
+        # """ Generate mask_fq_mq_positions array with positions where a variant was filtered because of LowFQ or LowMQ"""
+        # mask_fq_mq_positions = []
+        # for i in position_label[str(variants.POS)]:
+        #     if "LowFQ" in i:
+        #         mask_fq_mq_positions.append(str(variants.POS))
+        #     if i == "HighFQ":
+        #         mask_fq_mq_positions.append(str(variants.POS))
+        #
+        # print "Length of mask_fq_mq_positions array:%s" % len(mask_fq_mq_positions)
+
+        """ Mask Phage positions and LowFQ/MQ positions in SNP_matrix_allele_new.csv. This is the default matrix. """
+        if str(variants.POS) in functional_filter_pos_array:
+            ntd_string_array = ntd_string.split('\t')
+            ntd_string = ""
+            for i in ntd_string_array:
+                ntd_string = ntd_string + "\t" + "N"
+            #print ntd_string
+
+        if str(variants.POS) in mask_fq_mq_positions:
+            ntd_string_array = ntd_string.split('\t')
+            ntd_string = ""
+            for i in ntd_string_array:
+                ntd_string = ntd_string + "\t" + "N"
+            #print ntd_string
+
+        """ Generate a print_string for each of the matrix - SNP_matrix_allele_new.csv and SNP_matrix_allele_phage.csv """
         print_string = print_string + ntd_string + "\n"
+
+        print_string_phage = print_string_phage + ntd_string_phage + "\n"
+
+        """ This is a hardcoded solution. Find the root cause of these strings getting into the print_strint variable """
         print_string.replace(',;,', '\t')
         print_string.replace(';,', '\t')
+        print_string_phage.replace(',;,', '\t')
+        print_string_phage.replace(';,', '\t')
+
         fp_allele_new.write(print_string)
+        fp_allele_new_phage.write(print_string_phage)
 
     fp_code.close()
     fp_allele.close()
     fp_allele_new.close()
+    fp_allele_new_phage.close()
 
-
-    ## Indel SNP matrix generation steps
+######################################3
+    """ Indel matrix """
     header_print_string = "Type of SNP at POS > ALT; ALT|Effect|Impact|GeneID|Nrchange|Aachange|Nrgenepos|AAgenepos"
     final_merge_anno_file = VCF("%s/Final_vcf_gatk_indel.vcf.gz" % args.filter2_only_snp_vcf_dir)
     for sample in final_merge_anno_file.samples:
@@ -2575,32 +2838,33 @@ def annotated_snp_matrix():
         code_string = position_indel_label[str(variants.POS)]
         code_string = code_string.replace('reference_allele', '0')
         code_string = code_string.replace('reference_unmapped_position', '-1')
-        code_string = code_string.replace('LowFQ_QUAL_DP_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_DP_QUAL_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_QUAL_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_DP_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_proximate_SNP', '2')
-        code_string = code_string.replace('LowFQ_QUAL_DP', '2')
-        code_string = code_string.replace('LowFQ_DP_QUAL', '2')
-        code_string = code_string.replace('LowFQ_QUAL', '2')
-        code_string = code_string.replace('LowFQ_DP', '2')
-        code_string = code_string.replace('HighFQ_QUAL_DP_proximate_SNP', '2')
-        code_string = code_string.replace('HighFQ_DP_QUAL_proximate_SNP', '2')
-        code_string = code_string.replace('HighFQ_QUAL_proximate_SNP', '2')
-        code_string = code_string.replace('HighFQ_DP_proximate_SNP', '2')
-        code_string = code_string.replace('HighFQ_proximate_SNP', '2')
-        code_string = code_string.replace('HighFQ_QUAL_DP', '2')
-        code_string = code_string.replace('HighFQ_DP_QUAL', '2')
-        code_string = code_string.replace('HighFQ_QUAL', '2')
-        code_string = code_string.replace('HighFQ_DP', '2')
-        code_string = code_string.replace('LowFQ', '2')
-        code_string = code_string.replace('HighFQ', '2')
+        code_string = code_string.replace('LowAF_QUAL_DP_proximate_SNP', '2')
+        code_string = code_string.replace('LowAF_DP_QUAL_proximate_SNP', '2')
+        code_string = code_string.replace('LowAF_QUAL_proximate_SNP', '2')
+        code_string = code_string.replace('LowAF_DP_proximate_SNP', '2')
+        code_string = code_string.replace('LowAF_proximate_SNP', '2')
+        code_string = code_string.replace('LowAF_QUAL_DP', '2')
+        code_string = code_string.replace('LowAF_DP_QUAL', '2')
+        code_string = code_string.replace('LowAF_QUAL', '2')
+        code_string = code_string.replace('LowAF_DP', '2')
+        code_string = code_string.replace('HighAF_QUAL_DP_proximate_SNP', '2')
+        code_string = code_string.replace('HighAF_DP_QUAL_proximate_SNP', '2')
+        code_string = code_string.replace('HighAF_QUAL_proximate_SNP', '2')
+        code_string = code_string.replace('HighAF_DP_proximate_SNP', '2')
+        code_string = code_string.replace('HighAF_proximate_SNP', '2')
+        code_string = code_string.replace('HighAF_QUAL_DP', '2')
+        code_string = code_string.replace('HighAF_DP_QUAL', '2')
+        code_string = code_string.replace('HighAF_QUAL', '2')
+        code_string = code_string.replace('HighAF_DP', '2')
+        code_string = code_string.replace('LowAF', '2')
+        code_string = code_string.replace('HighAF', '2')
 
         if str(variants.POS) in indel_core_positions:
             code_string = code_string.replace('VARIANT', '1')
         # Adding functional class status code to SNP matrix: 2018-07-24
         elif str(variants.POS) in functional_filter_pos_array:
-            code_string = code_string.replace('VARIANT', '2')
+            # Changing Functional class filter code to -2 from 2: 2018-12-04
+            code_string = code_string.replace('VARIANT', '-2')
         else:
             code_string = code_string.replace('VARIANT', '3')
 
@@ -2673,14 +2937,220 @@ def annotated_snp_matrix():
             else:
                 ann_string = ";None"
 
-        print_string = print_string + ann_string
+                # SNP Matrix Bug
+                ann_string_split = ann_string.split(';')
+                # print len(ann_string_split)
+                if len(ann_string_split) == 3:
+                    first_allele_ann_string_split = ann_string_split[1].split('|')
+                    second_allele_ann_string_split = ann_string_split[2].split('|')
+                    # if "KPNIH1_26205" in first_allele_ann_string_split:
+                    #     #print len(first_allele_ann_string_split)
+                    #     print ann_string
+                    if len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) == 10:
+                        ann_string = ann_string
+                    elif len(first_allele_ann_string_split) > 10 and len(second_allele_ann_string_split) == 10:
+                        # if "KPNIH1_26205" in first_allele_ann_string_split:
+                        #     print len(first_allele_ann_string_split)
+                        if first_allele_ann_string_split[14] == "" and first_allele_ann_string_split[15] == "":
+                            prod = first_allele_ann_string_split[3] + first_allele_ann_string_split[15]
+                        else:
+                            prod = first_allele_ann_string_split[14] + "|" + first_allele_ann_string_split[15]
+                        new_first_allele_ann_string = ";" + first_allele_ann_string_split[0] + "|" + \
+                                                      first_allele_ann_string_split[1] + "|" + \
+                                                      first_allele_ann_string_split[2] + "|" + \
+                                                      first_allele_ann_string_split[4] + "|" + \
+                                                      first_allele_ann_string_split[9] + "|" + \
+                                                      first_allele_ann_string_split[10] + "|" + \
+                                                      first_allele_ann_string_split[11] + "|" + \
+                                                      first_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                        ann_string = new_first_allele_ann_string + str(ann_string_split[2])
+
+                    elif len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) > 10:
+
+                        if second_allele_ann_string_split[14] == "" and second_allele_ann_string_split[15] == "":
+                            prod = second_allele_ann_string_split[3] + second_allele_ann_string_split[15]
+                        else:
+                            prod = second_allele_ann_string_split[14] + "|" + second_allele_ann_string_split[15]
+                        new_second_allele_ann_string = second_allele_ann_string_split[0] + "|" + \
+                                                       second_allele_ann_string_split[1] + "|" + \
+                                                       second_allele_ann_string_split[2] + "|" + \
+                                                       second_allele_ann_string_split[4] + "|" + \
+                                                       second_allele_ann_string_split[9] + "|" + \
+                                                       second_allele_ann_string_split[10] + "|" + \
+                                                       second_allele_ann_string_split[11] + "|" + \
+                                                       second_allele_ann_string_split[
+                                                           13] + "|" + prod + "|" + prod + ";"
+
+                        ann_string = str(ann_string_split[1]) + new_second_allele_ann_string
+                    elif len(first_allele_ann_string_split) > 10 and len(second_allele_ann_string_split) > 10:
+                        # print ann_string
+
+                        if first_allele_ann_string_split[14] == "" and first_allele_ann_string_split[15] == "":
+                            prod = first_allele_ann_string_split[3] + first_allele_ann_string_split[15]
+                        else:
+                            prod = first_allele_ann_string_split[14] + "|" + first_allele_ann_string_split[15]
+                        new_first_allele_ann_string = ";" + first_allele_ann_string_split[0] + "|" + \
+                                                      first_allele_ann_string_split[1] + "|" + \
+                                                      first_allele_ann_string_split[2] + "|" + \
+                                                      first_allele_ann_string_split[4] + "|" + \
+                                                      first_allele_ann_string_split[9] + "|" + \
+                                                      first_allele_ann_string_split[10] + "|" + \
+                                                      first_allele_ann_string_split[11] + "|" + \
+                                                      first_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                        if second_allele_ann_string_split[14] == "" and second_allele_ann_string_split[15] == "":
+                            prod = second_allele_ann_string_split[3] + second_allele_ann_string_split[15]
+                        else:
+                            prod = second_allele_ann_string_split[14] + "|" + second_allele_ann_string_split[15]
+                        new_second_allele_ann_string = second_allele_ann_string_split[0] + "|" + \
+                                                       second_allele_ann_string_split[1] + "|" + \
+                                                       second_allele_ann_string_split[2] + "|" + \
+                                                       second_allele_ann_string_split[4] + "|" + \
+                                                       second_allele_ann_string_split[9] + "|" + \
+                                                       second_allele_ann_string_split[10] + "|" + \
+                                                       second_allele_ann_string_split[11] + "|" + \
+                                                       second_allele_ann_string_split[
+                                                           13] + "|" + prod + "|" + prod + ";"
+
+                        ann_string = new_first_allele_ann_string + new_second_allele_ann_string
+                        # print ann_string
+                    # print ann_string
+
+                if len(ann_string_split) > 3:
+                    # ann_string = ""
+                    # print ann_string
+                    # print len(ann_string_split)
+                    first_allele_ann_string_split = ann_string_split[1].split('|')
+                    second_allele_ann_string_split = ann_string_split[2].split('|')
+                    third_allele_ann_string_split = ann_string_split[3].split('|')
+                    # print first_allele_ann_string_split
+                    # print second_allele_ann_string_split
+                    if len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) == 10 and len(
+                            third_allele_ann_string_split) == 10:
+                        ann_string = ann_string
+
+                    elif len(first_allele_ann_string_split) > 10 and len(second_allele_ann_string_split) == 10 and len(
+                            third_allele_ann_string_split) == 10:
+                        if first_allele_ann_string_split[14] == "" and first_allele_ann_string_split[15] == "":
+                            prod = first_allele_ann_string_split[3] + first_allele_ann_string_split[15]
+                        else:
+                            prod = first_allele_ann_string_split[14] + "|" + first_allele_ann_string_split[15]
+                        new_first_allele_ann_string = ";" + first_allele_ann_string_split[0] + "|" + \
+                                                      first_allele_ann_string_split[1] + "|" + \
+                                                      first_allele_ann_string_split[2] + "|" + \
+                                                      first_allele_ann_string_split[4] + "|" + \
+                                                      first_allele_ann_string_split[9] + "|" + \
+                                                      first_allele_ann_string_split[10] + "|" + \
+                                                      first_allele_ann_string_split[11] + "|" + \
+                                                      first_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                        ann_string = new_first_allele_ann_string + str(ann_string_split[2]) + str(ann_string_split[3])
+
+                    elif len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) > 10 and len(
+                            third_allele_ann_string_split) == 10:
+
+                        if second_allele_ann_string_split[14] == "" and second_allele_ann_string_split[15] == "":
+                            prod = second_allele_ann_string_split[3] + second_allele_ann_string_split[15]
+                        else:
+                            prod = second_allele_ann_string_split[14] + "|" + second_allele_ann_string_split[15]
+                        new_second_allele_ann_string = second_allele_ann_string_split[0] + "|" + \
+                                                       second_allele_ann_string_split[1] + "|" + \
+                                                       second_allele_ann_string_split[2] + "|" + \
+                                                       second_allele_ann_string_split[4] + "|" + \
+                                                       second_allele_ann_string_split[9] + "|" + \
+                                                       second_allele_ann_string_split[10] + "|" + \
+                                                       second_allele_ann_string_split[11] + "|" + \
+                                                       second_allele_ann_string_split[
+                                                           13] + "|" + prod + "|" + prod + ";"
+
+                        ann_string = str(ann_string_split[1]) + new_second_allele_ann_string + str(ann_string_split[3])
+
+                    elif len(first_allele_ann_string_split) == 10 and len(second_allele_ann_string_split) == 10 and len(
+                            third_allele_ann_string_split) > 10:
+
+                        if third_allele_ann_string_split[14] == "" and third_allele_ann_string_split[15] == "":
+                            prod = third_allele_ann_string_split[3] + third_allele_ann_string_split[15]
+                        else:
+                            prod = third_allele_ann_string_split[14] + "|" + third_allele_ann_string_split[15]
+                        new_third_allele_ann_string = third_allele_ann_string_split[0] + "|" + \
+                                                      third_allele_ann_string_split[1] + "|" + \
+                                                      third_allele_ann_string_split[2] + "|" + \
+                                                      third_allele_ann_string_split[4] + "|" + \
+                                                      third_allele_ann_string_split[9] + "|" + \
+                                                      third_allele_ann_string_split[10] + "|" + \
+                                                      third_allele_ann_string_split[11] + "|" + \
+                                                      third_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                        ann_string = str(ann_string_split[1]) + str(ann_string_split[2]) + new_third_allele_ann_string
+
+                    elif len(first_allele_ann_string_split) > 10 and len(second_allele_ann_string_split) > 10 and len(
+                            third_allele_ann_string_split) > 10:
+                        # print ann_string
+                        if first_allele_ann_string_split[14] == "" and first_allele_ann_string_split[15] == "":
+                            prod = first_allele_ann_string_split[3] + first_allele_ann_string_split[15]
+                        else:
+                            prod = first_allele_ann_string_split[14] + "|" + first_allele_ann_string_split[15]
+                        new_first_allele_ann_string = ";" + first_allele_ann_string_split[0] + "|" + \
+                                                      first_allele_ann_string_split[1] + "|" + \
+                                                      first_allele_ann_string_split[2] + "|" + \
+                                                      first_allele_ann_string_split[4] + "|" + \
+                                                      first_allele_ann_string_split[9] + "|" + \
+                                                      first_allele_ann_string_split[10] + "|" + \
+                                                      first_allele_ann_string_split[11] + "|" + \
+                                                      first_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                        if second_allele_ann_string_split[14] == "" and second_allele_ann_string_split[15] == "":
+                            prod = second_allele_ann_string_split[3] + second_allele_ann_string_split[15]
+                        else:
+                            prod = second_allele_ann_string_split[14] + "|" + second_allele_ann_string_split[15]
+                        new_second_allele_ann_string = second_allele_ann_string_split[0] + "|" + \
+                                                       second_allele_ann_string_split[1] + "|" + \
+                                                       second_allele_ann_string_split[2] + "|" + \
+                                                       second_allele_ann_string_split[4] + "|" + \
+                                                       second_allele_ann_string_split[9] + "|" + \
+                                                       second_allele_ann_string_split[10] + "|" + \
+                                                       second_allele_ann_string_split[11] + "|" + \
+                                                       second_allele_ann_string_split[
+                                                           13] + "|" + prod + "|" + prod + ";"
+
+                        if third_allele_ann_string_split[14] == "" and third_allele_ann_string_split[15] == "":
+                            prod = third_allele_ann_string_split[3] + third_allele_ann_string_split[15]
+                        else:
+                            prod = third_allele_ann_string_split[14] + "|" + third_allele_ann_string_split[15]
+                        new_third_allele_ann_string = third_allele_ann_string_split[0] + "|" + \
+                                                      third_allele_ann_string_split[1] + "|" + \
+                                                      third_allele_ann_string_split[2] + "|" + \
+                                                      third_allele_ann_string_split[4] + "|" + \
+                                                      third_allele_ann_string_split[9] + "|" + \
+                                                      third_allele_ann_string_split[10] + "|" + \
+                                                      third_allele_ann_string_split[11] + "|" + \
+                                                      third_allele_ann_string_split[13] + "|" + prod + "|" + prod + ";"
+
+                        ann_string = first_allele_ann_string_split + second_allele_ann_string_split + new_third_allele_ann_string
+
+                        # print ann_string
+
+
+
+
+        #print_string = print_string + ann_string
+        #print_string = print_string + " locus_tag=" + tag + ann_string
+        strandness = " strand="
+        if "-" in tag:
+            tagsplit = tag.split('-')
+            for i in tagsplit:
+                strandness = strandness + locus_tag_to_strand[i] + "/"
+        else:
+            strandness = strandness + locus_tag_to_strand[tag]
+        print_string = print_string + " locus_tag=" + tag + strandness + ann_string
 
         gt_string = ""
         for gt in variants.gt_bases:
             gt = gt.replace('./.', '.')
             if "/" in gt:
                 gt_split = gt.split('/')
-                gt = gt_split[0]
+                gt = gt_split[1]
             gt_string = gt_string + "," + gt
         gt_string = gt_string.replace('.', variants.REF)
 
@@ -2698,6 +3168,8 @@ def annotated_snp_matrix():
         # final_code_string = final_code_string.replace(';,', ':::')
         final_code_string = final_code_string.replace(',;,', ':::')
         final_code_string = final_code_string.replace(';,', ':::')
+        final_code_string = final_code_string.replace('\t\t', '\t')
+        final_allele_string = final_allele_string.replace('\t\t', '\t')
         fp_allele.write(final_allele_string)
         fp_code.write(final_code_string)
     fp_code.close()
@@ -2857,7 +3329,6 @@ def someOtherFunc(data, key):
     global logger
     analysis_name_log = "step_" + str(args.steps)
     logger = generate_logger(args.filter2_only_snp_vcf_dir, analysis_name_log, log_unique_time)
-    keep_logging('test log', 'test log', logger, 'info')
     keep_logging('\nThe Script started at: %s' % start_time, '\nThe Script started at: %s' % start_time, logger, 'info')
     print_details = "This step will parse final vcf files(*_no_proximate_snp.vcf) generated at the end of Variant Calling Pipeline. At the end of this step, the following results will be generated and placed in output directory:\n\n" \
           "1. Final Core SNP Positions list(Variant positions that were not filtered out in any of the samples and passed all the filters)\n" \
@@ -2907,26 +3378,29 @@ def someOtherFunc(data, key):
         # Gather SNP positions from each final *_no_proximate_snp.vcf file (that passed the variant filter parameters from variant calling pipeline) and write to *_no_proximate_snp.vcf_position files for use in downstream methods
         keep_logging('Gathering SNP position information from each final *_no_proximate_snp.vcf file...', 'Gathering SNP position information from each final *_no_proximate_snp.vcf file...', logger, 'info')
 
-        # Extract All the unique SNO and Indel position list from final filtered *_no_proximate_snp.vcf files.
+        # # Extract All the unique SNO and Indel position list from final filtered *_no_proximate_snp.vcf files.
         unique_position_file = create_positions_filestep(vcf_filenames)
         unique_indel_position_file = create_indel_positions_filestep(vcf_filenames)
 
-        # bgzip and tabix all the vcf files in core_temp_dir.
+        # # bgzip and tabix all the vcf files in core_temp_dir.
         files_for_tabix = glob.glob("%s/*.vcf" % args.filter2_only_snp_vcf_dir)
         tabix(files_for_tabix, "vcf", logger, Config)
 
-        if ConfigSectionMap("functional_filters", Config)['apply_functional_filters'] == "yes":
-            keep_logging('Functional class filter is set to yes. Preparing Functional class filters\n', 'Functional class filter is set to yes. Preparing Functional class filters\n', logger,
-                         'info')
-            if ConfigSectionMap("functional_filters", Config)['find_phage_region'] == "yes":
-                # Submit Phaster jobs to find ProPhage region in reference genome.
-                run_phaster(args.reference, args.filter2_only_snp_vcf_dir, logger, Config)
-
-        # Get the cluster option; create and run jobs based on given parameter. The jobs will parse all the intermediate vcf file to extract information such as if any unique variant position was unmapped in a sample, if it was filtered out dur to DP,MQ, FQ, proximity to indel, proximity to other SNPs and other variant filter parameters set in config file.
+        # # Get the cluster option; create and run jobs based on given parameter. The jobs will parse all the intermediate vcf file to extract information such as if any unique variant position was unmapped in a sample, if it was filtered out dur to DP,MQ, FQ, proximity to indel, proximity to other SNPs and other variant filter parameters set in config file.
         tmp_dir = "/tmp/temp_%s/" % log_unique_time
         create_job(args.jobrun, vcf_filenames, unique_position_file, tmp_dir)
         create_indel_job(args.jobrun, vcf_filenames, unique_indel_position_file, tmp_dir)
 
+        # If Phaster Summary file doesn't exist in reference genome folder
+        if not os.path.isfile("%s/summary.txt" % os.path.dirname(args.reference)):
+            if ConfigSectionMap("functional_filters", Config)['apply_functional_filters'] == "yes":
+                keep_logging('Functional class filter is set to yes. Preparing Functional class filters\n',
+                             'Functional class filter is set to yes. Preparing Functional class filters\n', logger,
+                             'info')
+                if ConfigSectionMap("functional_filters", Config)['find_phage_region'] == "yes":
+                    # Submit Phaster jobs to find ProPhage region in reference genome.
+                    run_phaster(args.reference, args.filter2_only_snp_vcf_dir, logger, Config)
+
         call("cp %s %s/Logs/core_prep/" % (log_file_handle, os.path.dirname(os.path.dirname(args.filter2_only_snp_vcf_dir))), logger)
 
     if "2" in args.steps:
@@ -3029,8 +3503,8 @@ def someOtherFunc(data, key):
         call("rm %s/*vcf_no_proximate_snp.vcf_filter2_consensus.fa" % args.filter2_only_snp_vcf_dir, logger)
 
         # Generate DP barplots data and Analyze the FQ values of all the unique variant
-        #DP_analysis_barplot()
-        #FQ_analysis()
+        # DP_analysis_barplot()
+        # FQ_analysis()
 
         # Set up Report and results directories to transfer the final results.
         # Set up Report and results directories to transfer the final results.
@@ -3054,25 +3528,25 @@ def someOtherFunc(data, key):
         make_sure_path_exists(consensus_var_dir)
         make_sure_path_exists(core_vcf_dir)
         make_sure_path_exists(consensus_allele_var_dir)
-        make_sure_path_exists(consensus_ref_allele_var_dir)
+        #make_sure_path_exists(consensus_ref_allele_var_dir)
         make_sure_path_exists(consensus_ref_var_dir)
         make_sure_path_exists(consensus_ref_allele_unmapped_variant_dir)
         reference_base = os.path.basename(args.reference).split('.')[0]
         # Move results to the results directory
-        move_data_matrix_results = "cp -r %s/*.csv %s/*.txt %s/temp* %s/All* %s/Only* %s/*.R %s/R_scripts/generate_diagnostics_plots.R %s/" % (args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, os.path.dirname(os.path.abspath(__file__)), data_matrix_dir)
+        move_data_matrix_results = "cp -r %s/unique_positions_file %s/unique_indel_positions_file %s/*.csv %s/*.txt %s/temp_* %s/All* %s/Only* %s/*.R %s/R_scripts/generate_diagnostics_plots.R %s/" % (args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, os.path.dirname(os.path.abspath(__file__)), data_matrix_dir)
         #move_core_vcf_fasta_results = "cp %s/*_core.vcf.gz %s/*.fa %s/*_variants.fa %s/" % (args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, core_vcf_fasta_dir)
-        move_core_vcf_fasta_results = "mv %s/*_core.vcf.gz %s/*.fa %s/" % (args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, core_vcf_fasta_dir)
+        move_core_vcf_fasta_results = "mv %s/*_core.vcf.gz* %s/*_ANN* %s/*.fa %s/" % (args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir, core_vcf_fasta_dir)
         move_consensus_var_fasta_results = "mv %s/*_variants.fa %s/" % (core_vcf_fasta_dir, consensus_var_dir)
         move_consensus_ref_var_fasta_results = "mv %s/*.fa %s/" % (core_vcf_fasta_dir, consensus_ref_var_dir)
-        move_core_vcf = "mv %s/*_core.vcf.gz %s/" % (core_vcf_fasta_dir, core_vcf_dir)
+        move_core_vcf = "mv %s/*_core.vcf.gz %s/*vcf_core.vcf.gz.tbi %s/" % (core_vcf_fasta_dir, core_vcf_fasta_dir, core_vcf_dir)
         move_consensus_allele_var_fasta_results = "mv %s/*allele_variants.fa %s/" % (consensus_var_dir, consensus_allele_var_dir)
-        move_consensus_ref_allele_var_fasta_results = "mv %s/*_ref_allele_variants.fa %s/" % (
-            consensus_allele_var_dir, consensus_ref_allele_var_dir)
-        move_consensus_ref_allele_unmapped_var_fasta_results = "mv %s/*_ref_allele_unmapped_variants.fa %s/" % (
-            consensus_var_dir, consensus_ref_allele_unmapped_variant_dir)
+        remove_ref_allele = "rm %s/*_ref_allele_variants.fa" % consensus_allele_var_dir
+        #move_consensus_ref_allele_var_fasta_results = "mv %s/*_ref_allele_variants.fa %s/" % (consensus_allele_var_dir, consensus_ref_allele_var_dir)
+        move_consensus_ref_allele_unmapped_var_fasta_results = "mv %s/*_ref_allele_unmapped_variants.fa %s/" % (consensus_var_dir, consensus_ref_allele_unmapped_variant_dir)
         move_snpeff_results = "mv %s/*ANN* %s/" % (data_matrix_dir, data_matrix_snpeff_dir)
+        move_snpeff_vcf_results = "mv %s/*ANN* %s/" % (core_vcf_fasta_dir, data_matrix_snpeff_dir)
         copy_reference = "cp %s %s/%s.fa" % (args.reference, consensus_ref_var_dir, reference_base)
-        copy_reference_2 = "cp %s %s/%s.fa" % (args.reference, consensus_ref_allele_var_dir, reference_base)
+        #copy_reference_2 = "cp %s %s/%s.fa" % (args.reference, consensus_ref_allele_var_dir, reference_base)
 
         call("%s" % move_data_matrix_results, logger)
         call("%s" % move_core_vcf_fasta_results, logger)
@@ -3080,11 +3554,13 @@ def someOtherFunc(data, key):
         call("%s" % move_consensus_ref_var_fasta_results, logger)
         call("%s" % move_core_vcf, logger)
         call("%s" % move_consensus_allele_var_fasta_results, logger)
-        call("%s" % move_consensus_ref_allele_var_fasta_results, logger)
+        call("%s" % remove_ref_allele, logger)
+        #call("%s" % move_consensus_ref_allele_var_fasta_results, logger)
         call("%s" % move_consensus_ref_allele_unmapped_var_fasta_results, logger)
         call("%s" % copy_reference, logger)
-        call("%s" % copy_reference_2, logger)
+        #call("%s" % copy_reference_2, logger)
         call("%s" % move_snpeff_results, logger)
+        call("%s" % move_snpeff_vcf_results, logger)
         subprocess.call(["sed -i 's/title_here/%s/g' %s/generate_diagnostics_plots.R" % (os.path.basename(args.results_dir), data_matrix_dir)], shell=True)
 
         # Sanity Check if the variant consensus files generated are of same length
@@ -3121,7 +3597,7 @@ def someOtherFunc(data, key):
         make_sure_path_exists(logs_dir)
         call("mv *.log.txt %s" % logs_dir, logger)
         call("mv summary.txt detail.txt Functional_class_filter_positions.txt inexact_repeat_region_positions.txt phage_region_positions.txt repeat_region_positions.txt %s" % functional_ann_dir, logger)
-        call("mv temp_* All* Only* SNP_matrix_* Indel* extract_DP_positions.txt header.txt %s" % matrices_dir, logger)
+        call("mv temp_* All* Only* SNP_matrix_* Indel* extract_DP_positions.txt header.txt unique_indel_positions_file unique_positions_file %s" % matrices_dir, logger)
         call("mv annotated_no_proximate_snp_* %s/snpEff_results/" % data_matrix_dir, logger)
         call("mv bargraph* generate_diagnostics_plots.R %s" % plots_dir, logger)
         call("cp %s/temp_Only_filtered_positions_for_closely_matrix_FQ.txt %s/" % (matrices_dir, plots_dir), logger)
@@ -3141,26 +3617,24 @@ def someOtherFunc(data, key):
         #make_sure_path_exists(tree_dir)
 
 
-        prepare_ref_var_consensus_input = "%s/gubbins/%s_%s_ref_var_consensus.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
-        prepare_var_consensus_input = "%s/gubbins/%s_%s_var_consensus.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
-        prepare_allele_var_consensus_input = "%s/gubbins/%s_%s_allele_var_consensus.fa" % (
+        prepare_ref_var_consensus_input = "%s/gubbins/%s_%s_genome_aln_w_ref_allele_gubbins.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
+        prepare_var_consensus_input = "%s/gubbins/%s_%s_core_var_aln.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
+        prepare_allele_var_consensus_input = "%s/gubbins/%s_%s_noncore_plus_core_variants_aln.fa" % (
         args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''),
         reference_base)
-        prepare_ref_allele_var_consensus_input = "%s/gubbins/%s_%s_ref_allele_var_consensus.fa" % (
-            args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''),
-            reference_base)
-        prepare_ref_allele_unmapped_consensus_input = "%s/gubbins/%s_%s_ref_allele_unmapped_consensus.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
+        #prepare_ref_allele_var_consensus_input = "%s/gubbins/%s_%s_ref_allele_var_consensus.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''),reference_base)
+        prepare_ref_allele_unmapped_consensus_input = "%s/gubbins/%s_%s_genome_aln_w_alt_allele_unmapped_gubbins.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
+
         prepare_ref_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_ref_variant_positions/*.fa > %s" % (args.results_dir, prepare_ref_var_consensus_input)
         prepare_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_variant_positions/*_variants.fa > %s" % (args.results_dir, prepare_var_consensus_input)
         prepare_allele_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_allele_variant_positions/*_allele_variants.fa > %s" % (
         args.results_dir, prepare_allele_var_consensus_input)
-        prepare_ref_allele_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_ref_allele_variant_positions/*.fa > %s" % (
-            args.results_dir, prepare_ref_allele_var_consensus_input)
+        #prepare_ref_allele_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_ref_allele_variant_positions/*.fa > %s" % (args.results_dir, prepare_ref_allele_var_consensus_input)
         prepare_ref_allele_unmapped_consensus_input_cmd = "cat %s %s/core_snp_consensus/consensus_ref_allele_unmapped_variant/*.fa > %s" % (args.reference, args.results_dir, prepare_ref_allele_unmapped_consensus_input)
         call("%s" % prepare_ref_var_consensus_input_cmd, logger)
         call("%s" % prepare_var_consensus_input_cmd, logger)
         call("%s" % prepare_allele_var_consensus_input_cmd, logger)
-        call("%s" % prepare_ref_allele_var_consensus_input_cmd, logger)
+        #call("%s" % prepare_ref_allele_var_consensus_input_cmd, logger)
         call("%s" % prepare_ref_allele_unmapped_consensus_input_cmd, logger)
         # os.system(prepare_ref_var_consensus_input_cmd)
         # os.system(prepare_var_consensus_input_cmd)
@@ -3176,10 +3650,10 @@ def someOtherFunc(data, key):
 
     if "4" in args.steps:
         """ 
-        tree step 
+        Gubbins/Raxml step
         """
 
-        keep_logging('Step 4: Generate FastTree and RAxML trees for core consensus fasta files.', 'Step 4: Generate FastTree and RAxML trees for core consensus fasta files.', logger, 'info')
+        keep_logging('Step 4: Run Gubbins on core alignments and generate iqtree/RaxML trees.', 'Step 4: Run Gubbins on core alignments and generate iqtree/RaxML trees.', logger, 'info')
 
         #parse_phaster(args.reference)
         reference_base = os.path.basename(args.reference).split('.')[0]
@@ -3190,52 +3664,35 @@ def someOtherFunc(data, key):
         #make_sure_path_exists(tree_dir)
 
 
-        prepare_ref_var_consensus_input = "%s/gubbins/%s_%s_ref_var_consensus.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
-        prepare_var_consensus_input = "%s/gubbins/%s_%s_var_consensus.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
-        prepare_allele_var_consensus_input = "%s/gubbins/%s_%s_allele_var_consensus.fa" % (
-        args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''),
-        reference_base)
-        prepare_ref_allele_var_consensus_input = "%s/gubbins/%s_%s_ref_allele_var_consensus.fa" % (
-            args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''),
-            reference_base)
-        prepare_ref_allele_unmapped_consensus_input = "%s/gubbins/%s_%s_ref_allele_unmapped_consensus.fa" % (
+        prepare_ref_var_consensus_input = "%s/gubbins/%s_%s_genome_aln_w_ref_allele_gubbins.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
+        prepare_var_consensus_input = "%s/gubbins/%s_%s_core_var_aln.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
+        prepare_allele_var_consensus_input = "%s/gubbins/%s_%s_noncore_plus_core_variants_aln.fa" % (
         args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''),
         reference_base)
+        #prepare_ref_allele_var_consensus_input = "%s/gubbins/%s_%s_ref_allele_var_consensus.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''),reference_base)
+        prepare_ref_allele_unmapped_consensus_input = "%s/gubbins/%s_%s_genome_aln_w_alt_allele_unmapped_gubbins.fa" % (args.results_dir, (os.path.basename(os.path.normpath(args.results_dir))).replace('_core_results', ''), reference_base)
+
         prepare_ref_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_ref_variant_positions/*.fa > %s" % (args.results_dir, prepare_ref_var_consensus_input)
         prepare_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_variant_positions/*_variants.fa > %s" % (args.results_dir, prepare_var_consensus_input)
         prepare_allele_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_allele_variant_positions/*_allele_variants.fa > %s" % (
         args.results_dir, prepare_allele_var_consensus_input)
-        prepare_ref_allele_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_ref_allele_variant_positions/*.fa > %s" % (
-            args.results_dir, prepare_ref_allele_var_consensus_input)
-        prepare_ref_allele_unmapped_consensus_input_cmd = "cat %s %s/core_snp_consensus/consensus_ref_allele_unmapped_variant/*.fa > %s" % (
-        args.reference, args.results_dir, prepare_ref_allele_unmapped_consensus_input)
+        #prepare_ref_allele_var_consensus_input_cmd = "cat %s/core_snp_consensus/consensus_ref_allele_variant_positions/*.fa > %s" % (args.results_dir, prepare_ref_allele_var_consensus_input)
+        prepare_ref_allele_unmapped_consensus_input_cmd = "cat %s %s/core_snp_consensus/consensus_ref_allele_unmapped_variant/*.fa > %s" % (args.reference, args.results_dir, prepare_ref_allele_unmapped_consensus_input)
         call("%s" % prepare_ref_var_consensus_input_cmd, logger)
         call("%s" % prepare_var_consensus_input_cmd, logger)
         call("%s" % prepare_allele_var_consensus_input_cmd, logger)
-        call("%s" % prepare_ref_allele_var_consensus_input_cmd, logger)
         call("%s" % prepare_ref_allele_unmapped_consensus_input_cmd, logger)
-        # os.system(prepare_ref_var_consensus_input_cmd)
-        # os.system(prepare_var_consensus_input_cmd)
 
-        # Uncommenting the generation of pre-gubbins filtered Fasttree and Raxml trees.
-        # fasttree(tree_dir, prepare_ref_var_consensus_input, args.jobrun, logger, Config)
-        # fasttree(tree_dir, prepare_var_consensus_input, args.jobrun, logger, Config)
-        # fasttree(tree_dir, prepare_allele_var_consensus_input, args.jobrun, logger, Config)
-        # fasttree(tree_dir, prepare_ref_allele_var_consensus_input, args.jobrun, logger, Config)
-        #
-        # raxml(tree_dir, prepare_ref_var_consensus_input, args.jobrun, logger, Config)
-        # raxml(tree_dir, prepare_var_consensus_input, args.jobrun, logger, Config)
-        # raxml(tree_dir, prepare_allele_var_consensus_input, args.jobrun, logger, Config)
-        # raxml(tree_dir, prepare_ref_allele_var_consensus_input, args.jobrun, logger, Config)
 
         if args.gubbins and args.gubbins == "yes":
+            os.chdir(gubbins_dir)
             call("%s/scripts/gubbins_iqtree_raxml.sh %s 1" % (os.path.dirname(os.path.abspath(__file__)), prepare_ref_var_consensus_input), logger)
-            #call("%s/scripts/gubbins_iqtree_raxml.sh %s 1" % (os.path.dirname(os.path.abspath(__file__)), prepare_ref_allele_unmapped_consensus_input), logger)
+            call("%s/scripts/gubbins_iqtree_raxml.sh %s 1" % (os.path.dirname(os.path.abspath(__file__)), prepare_ref_allele_unmapped_consensus_input), logger)
             #call("%s/scripts/gubbins_iqtree_raxml.sh %s 1" % (os.path.dirname(os.path.abspath(__file__)), prepare_ref_allele_var_consensus_input), logger)
         else:
+            keep_logging('The gubbins argument is set to No.', 'The gubbins argument is set to No.', logger, 'info')
             print "%s/scripts/gubbins_iqtree_raxml.sh %s 0" % (os.path.dirname(os.path.abspath(__file__)), prepare_ref_var_consensus_input)
             print "%s/scripts/gubbins_iqtree_raxml.sh %s 0" % (os.path.dirname(os.path.abspath(__file__)), prepare_ref_allele_unmapped_consensus_input)
-            print "%s/scripts/gubbins_iqtree_raxml.sh %s 0" % (os.path.dirname(os.path.abspath(__file__)), prepare_ref_allele_var_consensus_input)
 
         call("cp %s %s/Logs/tree/" % (
             log_file_handle, os.path.dirname(os.path.dirname(args.filter2_only_snp_vcf_dir))), logger)
@@ -3256,9 +3713,9 @@ def someOtherFunc(data, key):
         # # Read new allele matrix and generate fasta; generate a seperate function
         keep_logging('Generating Fasta from Variant Alleles...\n', 'Generating Fasta from Variant Alleles...\n', logger, 'info')
 
-        create_job_allele_variant_fasta(args.jobrun, vcf_filenames, args.filter2_only_snp_vcf_dir, config_file)
-
-        extract_only_ref_variant_fasta_from_reference_allele_variant()
+        # create_job_allele_variant_fasta(args.jobrun, vcf_filenames, args.filter2_only_snp_vcf_dir, config_file)
+        #
+        # extract_only_ref_variant_fasta_from_reference_allele_variant()
 
         call("cp %s %s/Logs/core/" % (
             log_file_handle, os.path.dirname(os.path.dirname(args.filter2_only_snp_vcf_dir))), logger)
diff --git a/modules/variant_diagnostics/extract_only_ref_variant_fasta_unique_positions.py b/modules/variant_diagnostics/extract_only_ref_variant_fasta_unique_positions.py
index ca18acf..7394cb3 100644
--- a/modules/variant_diagnostics/extract_only_ref_variant_fasta_unique_positions.py
+++ b/modules/variant_diagnostics/extract_only_ref_variant_fasta_unique_positions.py
@@ -106,18 +106,24 @@ def extract_only_ref_variant_fasta_unique_positions():
                     variant_allele = variant_allele + ntd
             #print variant_allele
             print_string = print_string + str(variant_allele) + "\n"
+
             allele_variant_fasta = open("%s/%s_allele_variants.fa" % (args.filter2_only_snp_vcf_dir, sample_name_re), 'w+')
+            allele_variant_fasta.write(print_string)
+            allele_variant_fasta.close()
+
             allele_ref_variant_fasta = open("%s/%s_ref_allele_variants.fa" % (args.filter2_only_snp_vcf_dir, sample_name_re), 'w+')
             allele_ref_variant_vcf = open("%s/%s_ref_allele_variants.vcf" % (args.filter2_only_snp_vcf_dir, sample_name_re), 'w+')
             allele_ref_variant_vcf.write(vcf_header)
-            allele_variant_fasta.write(print_string)
-            allele_variant_fasta.close()
+
             variant_allele_array = []
             variant_allele_array.append(columns[i][1:])
+
             get_sample_reference = Fasta("%s/%s_allele_variants.fa" % (args.filter2_only_snp_vcf_dir, sample_name_re))
             if len(get_sample_reference.keys()) == 1:
                 sample_ref_id = get_sample_reference.keys()
+
             for positions in unique_position_array:
+
                 pos_index = unique_position_array.index(positions)
 
                 if "/" in str(variant_allele_array[0][pos_index]):
@@ -125,9 +131,11 @@ def extract_only_ref_variant_fasta_unique_positions():
                     #print allele_var
                 else:
                     allele_var = str(variant_allele_array[0][pos_index])
+
                 ref_allele = str(get_reference.sequence({'chr': str(get_reference.keys()[0]), 'start': int(positions), 'stop': int(positions)}))
                 generate_vcf_string = "%s\t%s\t.\t%s\t%s\t221.999\t.\t.\t.\n" % (ref_id[0].split(' ')[0], positions, ref_allele, allele_var)
                 allele_ref_variant_vcf.write(generate_vcf_string)
+
             allele_ref_variant_vcf.close()
             filename = "%s/consensus_ref_allele_variant.sh" % args.filter2_only_snp_vcf_dir
 
@@ -159,20 +167,18 @@ def extract_only_ref_variant_fasta_unique_positions():
 
 
 def extract_only_ref_variant_fasta_unique_positions_with_unmapped():
-    #print "here"
-
-    # Get reference genome ID
+    # Get reference genome ID from reference fasta file
     get_reference = Fasta(args.reference)
     if len(get_reference.keys()) == 1:
         ref_id = get_reference.keys()
 
-
+    # Read in the SNP Matrix file and seperate the columns.
     c_reader = csv.reader(open('%s/SNP_matrix_allele_new.csv' % args.filter2_only_snp_vcf_dir, 'r'), delimiter='\t')
     c_reader_2 = csv.reader(open('%s/SNP_matrix_allele_new.csv' % args.filter2_only_snp_vcf_dir, 'r'), delimiter='\t')
     columns = list(zip(*c_reader))
     ncol = len(next(c_reader_2))
 
-
+    # Generate an array of all the unique variant positions that were called in all the samples
     unique_position_array = []
     for i in columns[0][1:]:
         replace_string = i.split(' ')
@@ -180,10 +186,10 @@ def extract_only_ref_variant_fasta_unique_positions_with_unmapped():
             unique_position_array.append(int(replace_string[3]))
         else:
             unique_position_array.append(int(replace_string[2]))
-    #print unique_position_array
 
     counts = 1
     end = ncol
+    # Loop over each column, check if the column name matches the sample name provided with argument args.filter2_only_snp_vcf_filename
     for i in xrange(1, end, 1):
         print_string = ""
         ref_print_string = ""
diff --git a/modules/variant_diagnostics/mask_gubbins_variants.py b/modules/variant_diagnostics/mask_gubbins_variants.py
index 6c246ef..343327e 100644
--- a/modules/variant_diagnostics/mask_gubbins_variants.py
+++ b/modules/variant_diagnostics/mask_gubbins_variants.py
@@ -26,19 +26,17 @@
                     (1) Masked whole genome alignment file in FASTA format
                     (2) Text file with list of masked positions in each genome
                     ''')
-parser.add_argument('-a', '--alignment', metavar='FASTA', type=str,
-                    required=True,
+parser.add_argument('fasta', metavar='FASTA', type=str,
                     help='whole genome alignment file in FASTA format')
-parser.add_argument('-g', '--gff', metavar='GFF', type=str,
-                    required=True,
+parser.add_argument('gff', metavar='GFF', type=str,
                     help='gubbins GFF output file')
-parser.add_argument('-o', '--outdir', metavar='OUTFILE', type=str,
+parser.add_argument('-o', '--outdir', metavar='OUTDIR', type=str,
                     default='.',
                     help='''output fasta file directory
                     (default: .)''')
 args = parser.parse_args()
 
-aln_path = args.alignment
+aln_path = args.fasta
 gubbins_gff_path = args.gff
 outdir = args.outdir
 
diff --git a/modules/variant_diagnostics/reason_job_indel_debug.py b/modules/variant_diagnostics/reason_job_indel_debug.py
index 1dd6b5f..0772a49 100644
--- a/modules/variant_diagnostics/reason_job_indel_debug.py
+++ b/modules/variant_diagnostics/reason_job_indel_debug.py
@@ -150,13 +150,13 @@ def get_reason():
                     st = "HighFQ"
                     if positions_mpileup_vcf[int(j)][2] < 100.00:
                         st = st + "_QUAL"
-                    if positions_mpileup_vcf[int(j)][0] < 15:
+                    if positions_mpileup_vcf[int(j)][0] < 10:
                         st = st + "_DP"
                 else:
                     st = "LowFQ"
                     if positions_mpileup_vcf[int(j)][2] < 100.00:
                         st = st + "_QUAL"
-                    if positions_mpileup_vcf[int(j)][0] < 15:
+                    if positions_mpileup_vcf[int(j)][0] < 10:
                         st = st + "_DP"
                 st = st + pst + "\n"
                 f1.write(st)
diff --git a/modules/variant_diagnostics/reason_job_indel_debug_gatk.py b/modules/variant_diagnostics/reason_job_indel_debug_gatk.py
new file mode 100755
index 0000000..01bf438
--- /dev/null
+++ b/modules/variant_diagnostics/reason_job_indel_debug_gatk.py
@@ -0,0 +1,173 @@
+__author__ = 'alipirani'
+
+import argparse
+import re
+import os
+import csv
+import subprocess
+from collections import OrderedDict
+from collections import defaultdict
+from joblib import Parallel, delayed
+import multiprocessing
+from cyvcf2 import VCF
+import timeit
+import ConfigParser
+from config_settings import ConfigSectionMap
+from logging_subprocess import *
+from log_modules import *
+
+parser = argparse.ArgumentParser(description='Creating Label files individual jobs')
+parser.add_argument('-filter2_only_snp_vcf_dir', action='store', dest="filter2_only_snp_vcf_dir",
+                    help='Directory where all the filter2 only SNP vcf files are saved.')
+parser.add_argument('-filter2_only_snp_vcf_file', action='store', dest="filter2_only_snp_vcf_file",
+                    help='Names of filter2 only SNP vcf file')
+parser.add_argument('-unique_position_file', action='store', dest="unique_position_file",
+                    help='Names of unique positions file')
+parser.add_argument('-tmp_dir', action='store', dest="tmp_dir",
+                    help='Names of temporary directory')
+args = parser.parse_args()
+
+indel_file = (args.filter2_only_snp_vcf_file).replace('_final.vcf_no_proximate_snp.vcf', '_indel_final.vcf')
+
+"""Set variables and set up the tmp directories"""
+dir = args.filter2_only_snp_vcf_dir
+unique_positions_file = args.unique_position_file
+os.system("mkdir %s" % args.tmp_dir)
+os.system("cp %s %s/%s" % (indel_file, args.tmp_dir, os.path.basename(indel_file)))
+
+""" Generate unique positions array"""
+position_array_sort = []
+f = open(unique_positions_file, 'r+')
+for line in f:
+    line = line.strip()
+    position_array_sort.append(line)
+f.close()
+
+""" Prepare output label file """
+file = args.tmp_dir + "/" + os.path.basename(indel_file)
+print "Processing %s" % file
+out_file_name = indel_file + "_indel_positions_label"
+
+""" Get the prefix for all the arrays """
+array_name = os.path.basename(out_file_name)
+
+#Changed 8 March
+""" Generate proximate, unmapped, variant positions array"""
+ori_unmapped_file = out_file_name.replace("filter2_indel_final.vcf_indel_positions_label", "unmapped.bed_positions")
+ori_proximate_file = out_file_name.replace("filter2_indel_final.vcf_indel_positions_label", "filter2_final.vcf_no_proximate_snp.vcf_positions_array")
+ori_variant_position_file = out_file_name.replace("filter2_indel_final.vcf_indel_positions_label", "filter2_indel_final.vcf")
+#ori_mpileup_file = out_file_name.replace("filter2_indel_final.vcf_indel_positions_label", "aln_mpileup_raw.vcf")
+ori_mpileup_file = out_file_name.replace("filter2_indel_final.vcf_indel_positions_label", "filter2_indel_gatk.vcf")
+
+
+current_unmapped_file = args.tmp_dir + "/%s" % (os.path.basename(ori_unmapped_file))
+current_proximate_file = args.tmp_dir + "/%s" % (os.path.basename(ori_proximate_file))
+current_variant_position_file = args.tmp_dir + "/%s" % (os.path.basename(ori_variant_position_file))
+current_mpileup_file = args.tmp_dir + "/%s" % (os.path.basename(ori_mpileup_file))
+
+os.system("cp %s %s/" % (ori_unmapped_file, args.tmp_dir))
+os.system("cp %s %s/" % (ori_proximate_file, args.tmp_dir))
+os.system("cp %s %s/" % (ori_variant_position_file, args.tmp_dir))
+os.system("cp %s %s/" % (ori_mpileup_file, args.tmp_dir))
+
+
+
+#variant position array
+variant_position_array = "variant_" + str(array_name)
+variant_position_array = []
+with open(current_variant_position_file, 'rU') as csv_file:
+    csv_reader = csv.reader(csv_file, delimiter='\t')
+    for row in csv_reader:
+        position = row[0]
+        if not position.startswith('#'):
+            variant_position_array.append(row[1])
+csv_file.close()
+
+#unmapped position array
+unmapped_array = "unmapped_" + str(array_name)
+unmapped_array = {}
+with open(current_unmapped_file, 'rU') as fp1:
+    for line in fp1:
+        line = line.strip()
+        unmapped_array[line] = ""
+fp1.close()
+
+#proximate position array
+proximate_array = "proximate_" + str(array_name)
+proximate_array = {}
+with open(current_proximate_file, 'rU') as fp2:
+    for liness in fp2:
+        liness = liness.strip()
+        proximate_array[liness] = ""
+fp2.close()
+
+""" Prepare cyvcf vcf files """
+#bgzip_cmd = "for i in %s/*.vcf; do bgzip -c $i > $i%s; done" % (args.filter2_only_snp_vcf_dir, ".gz")
+#tabix_cmd = "for i in %s/*.vcf.gz; do tabix $i; done" % (args.filter2_only_snp_vcf_dir)
+#os.system(bgzip_cmd)
+#os.system(tabix_cmd)
+
+
+
+""" Load Cyvcf objects """
+vcf_final_file = VCF(indel_file + ".gz")
+mpileup_file = VCF(ori_mpileup_file + ".gz")
+
+reference_genome = vcf_final_file.seqnames[0]
+
+positions_final_vcf = defaultdict(list)
+positions_mpileup_vcf = defaultdict(list)
+
+for variants in VCF(indel_file + ".gz"):
+    positions_final_vcf[int(variants.POS)].append(variants.INFO.get('DP'))
+
+for variants in VCF(ori_mpileup_file + ".gz"):
+    positions_mpileup_vcf[int(variants.POS)].append(variants.INFO.get('DP'))
+    #positions_mpileup_vcf[int(variants.POS)].append(variants.INFO.get('FQ'))
+    #positions_mpileup_vcf[int(variants.POS)].append(variants.QUAL)
+    positions_mpileup_vcf[int(variants.POS)].append(variants.INFO.get('QD'))
+    positions_mpileup_vcf[int(variants.POS)].append(variants.INFO.get('MQ'))
+    positions_mpileup_vcf[int(variants.POS)].append(variants.INFO.get('AF'))
+    #positions_mpileup_vcf[int(variants.POS)].append(variants.INFO.get('AF1'))
+
+
+
+""" Generate label files and check why the positions were filtered out from the final vcf file """
+def get_reason():
+    f1=open(out_file_name, 'w+')
+    for j in position_array_sort:
+        """ Check if the unique position is present in the final no_proximate_snp.vcf file """
+        if int(j) not in positions_final_vcf.keys():
+            if int(j) not in positions_mpileup_vcf.keys():
+                if j in unmapped_array.keys():
+                    st = "reference_unmapped_position\n"
+                    f1.write(st)
+                else:
+                    st = "reference_allele\n"
+                    f1.write(st)
+            else:
+                if j in proximate_array.keys():
+                    pst = "_proximate_SNP"
+                else:
+                    pst = ""
+                if positions_mpileup_vcf[int(j)][3] < 0.9:
+                    st = "LowAF"
+                    if positions_mpileup_vcf[int(j)][1] < 2.00:
+                        st = st + "_QUAL"
+                    if positions_mpileup_vcf[int(j)][0] < 10:
+                        st = st + "_DP"
+                else:
+                    st = "HighAF"
+                    if positions_mpileup_vcf[int(j)][1] < 2.00:
+                        st = st + "_QUAL"
+                    if positions_mpileup_vcf[int(j)][0] < 15:
+                        st = st + "_DP"
+                st = st + pst + "\n"
+                f1.write(st)
+        else:
+            st = "VARIANT" + "\n"
+            f1.write(st)
+    f1.close()
+
+
+print "Time taken to execute this code block: %s" % (timeit.timeit(get_reason, number=1))
diff --git a/modules/variant_diagnostics/scripts/gubbins_iqtree_raxml.sh b/modules/variant_diagnostics/scripts/gubbins_iqtree_raxml.sh
index 598547a..3a27066 100755
--- a/modules/variant_diagnostics/scripts/gubbins_iqtree_raxml.sh
+++ b/modules/variant_diagnostics/scripts/gubbins_iqtree_raxml.sh
@@ -13,6 +13,10 @@
 # get prefix for output files
 pref=$(echo $1 | cut -d. -f1 | rev | cut -d/ -f1 | rev)
 
+# get working directory
+wd=$(dirname $1)
+cd $wd
+
 # modules to load (some of these might not be necessary)
 modules=$(echo python-anaconda2/201607 biopython fasttree dendropy reportlab RAxML raxml bioperl fastml/gub gubbins openmpi/1.10.2/gcc/4.8.5 gcc/4.8.5)
 
@@ -25,6 +29,9 @@ else
   acct=esnitkin_flux
 fi
 
+mkdir iqtree_results
+mkdir raxml_results
+
 # if gubbins
 if [ $2 = 1 ]; then
   echo Will run gubbins.
@@ -33,27 +40,39 @@ if [ $2 = 1 ]; then
   echo $gub > ${pref}_gubbins_command.sh
 
   # raxml command
-  raxml=$(echo mpirun -np 2 raxmlHPC-HYBRID-SSE3 -f a -x 12345 -p 12345 -N autoMRE -m ASC_GTRGAMMA --asc-corr=lewis -s $pref.filtered_polymorphic_sites.fasta -n $pref.filtered_polymorphic_sites_raxML-fa-x12345-p12345-NautoMRE-mGTRGAM-lew -T 6)
+  raxml=$(echo mpirun -np 2 raxmlHPC-HYBRID-SSE3 -f a -x 12345 -p 12345 -N autoMRE -m ASC_GTRGAMMA --asc-corr=lewis -s ../$pref.filtered_polymorphic_sites.fasta -n $pref.filtered_polymorphic_sites_raxML -T 6)
 echo $raxml > ${pref}_raxml_command.sh
 
   # iqtree command
-  iqtree=$(echo /nfs/esnitkin/bin_group/anaconda3/bin/iqtree -s $pref.filtered_polymorphic_sites.fasta -nt AUTO -bb 1000 -m MFP -pre $pref)
+  iqtree=$(echo /nfs/esnitkin/bin_group/anaconda3/bin/iqtree -s ../$pref.filtered_polymorphic_sites.fasta -nt AUTO -bb 1000 -m MFP -pre $pref)
   echo $iqtree > ${pref}_iqtree_command.sh
   
   # generate pbs scripts for gubbins, raxml, iqtree
-  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_gubbins_command.sh -o ${pref}_gubbins.pbs -M "$modules" -a $acct
-  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_iqtree_command.sh -o ${pref}_iqtree.pbs -M "$modules" -a $acct
-  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_raxml_command.sh -o ${pref}_raxml.pbs -M "$modules" -a $acct
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/scripts/pbs_script_maker.py -c ${pref}_gubbins_command.sh -o ${pref}_gubbins.pbs -M "$modules" -a $acct -wd $wd
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/scripts/pbs_script_maker.py -c ${pref}_iqtree_command.sh -o ${pref}_iqtree.pbs -M "$modules" -a $acct -wd $wd/iqtree_results
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/scripts/pbs_script_maker.py -c ${pref}_raxml_command.sh -o ${pref}_raxml.pbs -M "$modules" -a $acct -wd $wd/raxml_results
 
   # start gubbins, iqtree, raxml jobs
   echo qsub ${pref}_gubbins.pbs
   gub_qsub=$(qsub ${pref}_gubbins.pbs)
   gub_id=$(echo $gub_qsub | cut -d. -f1)
   echo gubbins job id: $gub_id
+
+  mv ${pref}_iqtree.pbs iqtree_results
+  mv ${pref}_iqtree_command.sh iqtree_results
+  cd iqtree_results
   echo qsub -W depend=afterok:$gub_id ${pref}_iqtree.pbs
-  echo qsub -W depend=afterok:$gub_id ${pref}_raxml.pbs
   iqtree_qsub=$(qsub -W depend=afterok:$gub_id ${pref}_iqtree.pbs)
+
+  cd $wd
+  mv ${pref}_raxml.pbs raxml_results
+  mv ${pref}_raxml_command.sh raxml_results
+  cd raxml_results
+  echo qsub -W depend=afterok:$gub_id ${pref}_raxml.pbs
   raxml_qsub=$(qsub -W depend=afterok:$gub_id ${pref}_raxml.pbs)
+
+  cd $wd
+
 else
   echo Will not run gubbins.
 
@@ -61,22 +80,33 @@ else
   /nfs/esnitkin/bin_group/anaconda3/bin/snp-sites -o ${pref}_varSites.fa $1
 
   # raxml command
-  raxml=$(echo mpirun -np 2 raxmlHPC-HYBRID-SSE3 -f a -x 12345 -p 12345 -N autoMRE -m ASC_GTRGAMMA --asc-corr=lewis -s ${pref}_varSites.fa -n $pref_raxML-fa-x12345-p12345-NautoMRE-mGTRGAM-lew -T 6)
+  raxml=$(echo mpirun -np 2 raxmlHPC-HYBRID-SSE3 -f a -x 12345 -p 12345 -N autoMRE -m ASC_GTRGAMMA --asc-corr=lewis -s ../${pref}_varSites.fa -n ${pref}_raxML -T 6)
   echo $raxml > ${pref}_raxml_command.sh
 
   # iqtree command
-  iqtree=$(echo /nfs/esnitkin/bin_group/anaconda3/bin/iqtree -s ${pref}_varSites.fa -nt AUTO -bb 1000 -m MFP+ASC -pre ${pref}_varSites)
+  iqtree=$(echo /nfs/esnitkin/bin_group/anaconda3/bin/iqtree -s ../${pref}_varSites.fa -nt AUTO -bb 1000 -m MFP+ASC -pre ${pref}_varSites)
   echo $iqtree > ${pref}_iqtree_command.sh
 
   # generate pbs scripts for iqtree, raxml
-  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_iqtree_command.sh -o ${pref}_iqtree.pbs -M "$modules" -a $acct
-  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_raxml_command.sh -o ${pref}_raxml.pbs -M "$modules" -a $acct
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/scripts/pbs_script_maker.py -c ${pref}_iqtree_command.sh -o ${pref}_iqtree.pbs -M "$modules" -a $acct -wd $wd/iqtree_results
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/scripts/pbs_script_maker.py -c ${pref}_raxml_command.sh -o ${pref}_raxml.pbs -M "$modules" -a $acct -wd $wd/raxml_results
 
   # start iqtree, raxml jobs
+
+  mv ${pref}_iqtree.pbs iqtree_results
+  mv ${pref}_iqtree_command.sh iqtree_results
+  cd iqtree_results
   echo qsub ${pref}_iqtree.pbs
-  echo qsub ${pref}_raxml.pbs
   iqtree_qsub=$(qsub ${pref}_iqtree.pbs)
+  
+  cd $wd
+  mv ${pref}_raxml.pbs raxml_results
+  mv ${pref}_raxml_command.sh raxml_results
+  cd raxml_results
+  echo qsub ${pref}_raxml.pbs
   raxml_qsub=$(qsub ${pref}_raxml.pbs)
 
+  cd $wd
+
 fi
 
diff --git a/modules/variant_diagnostics/scripts/gubbins_iqtree_raxml_backup_2018_10_31.sh b/modules/variant_diagnostics/scripts/gubbins_iqtree_raxml_backup_2018_10_31.sh
new file mode 100755
index 0000000..598547a
--- /dev/null
+++ b/modules/variant_diagnostics/scripts/gubbins_iqtree_raxml_backup_2018_10_31.sh
@@ -0,0 +1,82 @@
+#!/bin/bash
+
+# $1 = path to fasta file
+# $2 = gubbins (1) or no gubbins (0)
+# $3 = flux account to submit to (default: esnitkin_fluxod)
+#
+# Usage:
+# Gubbins:
+# gubbins_iqtree_raxml.sh /path/to/whole/genome/alignment/fasta/file 1
+# No gubbins:
+# gubbins_iqtree_raxml.sh /path/to/snps/only/fasta/file 0
+
+# get prefix for output files
+pref=$(echo $1 | cut -d. -f1 | rev | cut -d/ -f1 | rev)
+
+# modules to load (some of these might not be necessary)
+modules=$(echo python-anaconda2/201607 biopython fasttree dendropy reportlab RAxML raxml bioperl fastml/gub gubbins openmpi/1.10.2/gcc/4.8.5 gcc/4.8.5)
+
+# get account
+if [ $3 != '' ]; then
+  echo Will submit jobs to $3.
+  acct=$3
+else
+  echo Will submit jobs to esnitkin_flux.
+  acct=esnitkin_flux
+fi
+
+# if gubbins
+if [ $2 = 1 ]; then
+  echo Will run gubbins.
+  # gubbins command
+  gub=$(echo run_gubbins.py --prefix $pref --threads 12 $1)
+  echo $gub > ${pref}_gubbins_command.sh
+
+  # raxml command
+  raxml=$(echo mpirun -np 2 raxmlHPC-HYBRID-SSE3 -f a -x 12345 -p 12345 -N autoMRE -m ASC_GTRGAMMA --asc-corr=lewis -s $pref.filtered_polymorphic_sites.fasta -n $pref.filtered_polymorphic_sites_raxML-fa-x12345-p12345-NautoMRE-mGTRGAM-lew -T 6)
+echo $raxml > ${pref}_raxml_command.sh
+
+  # iqtree command
+  iqtree=$(echo /nfs/esnitkin/bin_group/anaconda3/bin/iqtree -s $pref.filtered_polymorphic_sites.fasta -nt AUTO -bb 1000 -m MFP -pre $pref)
+  echo $iqtree > ${pref}_iqtree_command.sh
+  
+  # generate pbs scripts for gubbins, raxml, iqtree
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_gubbins_command.sh -o ${pref}_gubbins.pbs -M "$modules" -a $acct
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_iqtree_command.sh -o ${pref}_iqtree.pbs -M "$modules" -a $acct
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_raxml_command.sh -o ${pref}_raxml.pbs -M "$modules" -a $acct
+
+  # start gubbins, iqtree, raxml jobs
+  echo qsub ${pref}_gubbins.pbs
+  gub_qsub=$(qsub ${pref}_gubbins.pbs)
+  gub_id=$(echo $gub_qsub | cut -d. -f1)
+  echo gubbins job id: $gub_id
+  echo qsub -W depend=afterok:$gub_id ${pref}_iqtree.pbs
+  echo qsub -W depend=afterok:$gub_id ${pref}_raxml.pbs
+  iqtree_qsub=$(qsub -W depend=afterok:$gub_id ${pref}_iqtree.pbs)
+  raxml_qsub=$(qsub -W depend=afterok:$gub_id ${pref}_raxml.pbs)
+else
+  echo Will not run gubbins.
+
+  echo Finding and removing invariant sites.
+  /nfs/esnitkin/bin_group/anaconda3/bin/snp-sites -o ${pref}_varSites.fa $1
+
+  # raxml command
+  raxml=$(echo mpirun -np 2 raxmlHPC-HYBRID-SSE3 -f a -x 12345 -p 12345 -N autoMRE -m ASC_GTRGAMMA --asc-corr=lewis -s ${pref}_varSites.fa -n $pref_raxML-fa-x12345-p12345-NautoMRE-mGTRGAM-lew -T 6)
+  echo $raxml > ${pref}_raxml_command.sh
+
+  # iqtree command
+  iqtree=$(echo /nfs/esnitkin/bin_group/anaconda3/bin/iqtree -s ${pref}_varSites.fa -nt AUTO -bb 1000 -m MFP+ASC -pre ${pref}_varSites)
+  echo $iqtree > ${pref}_iqtree_command.sh
+
+  # generate pbs scripts for iqtree, raxml
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_iqtree_command.sh -o ${pref}_iqtree.pbs -M "$modules" -a $acct
+  /nfs/esnitkin/bin_group/anaconda3/bin/python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline_dev/modules/variant_diagnostics/scripts/pbs_script_maker.py -c ${pref}_raxml_command.sh -o ${pref}_raxml.pbs -M "$modules" -a $acct
+
+  # start iqtree, raxml jobs
+  echo qsub ${pref}_iqtree.pbs
+  echo qsub ${pref}_raxml.pbs
+  iqtree_qsub=$(qsub ${pref}_iqtree.pbs)
+  raxml_qsub=$(qsub ${pref}_raxml.pbs)
+
+fi
+
diff --git a/other/FQ_analysis.py b/other/FQ_analysis.py
deleted file mode 100755
index afb40b4..0000000
--- a/other/FQ_analysis.py
+++ /dev/null
@@ -1,78 +0,0 @@
-__author__ = 'alipirani'
-
-import sys
-import os
-import argparse
-import errno
-
-
-################################################### Command Line Argument Parsing ###################################################
-parser = argparse.ArgumentParser(description='FQ analysis: Applying modified vcf filters to retail low FQ values for further analysis')
-parser.add_argument('-final_raw_vcf', action='store', dest="final_raw_vcf", help='Path to final_raw_vcf(5bp snp indel vcf file)', required=True)
-parser.add_argument('-o', action='store', dest="output_folder", help='Output Path ending with output directory name to save the results', required=True)
-parser.add_argument('-index', action='store', dest="index", help='Reference Index Name. Full path to reference', required=True)
-args = parser.parse_args()
-############################################################### End #################################################################
-
-analysis = args.final_raw_vcf.replace("_aln_mpileup_raw.vcf_5bp_indel_removed.vcf", "")
-out_path = args.output_folder
-reference = args.index
-final_raw_vcf = args.final_raw_vcf
-
-def gatk_filter2(final_raw_vcf, out_path, analysis, reference):
-    gatk_filter2_parameter_expression = "MQ > 50 && QUAL > 100 && DP > 15"
-    gatk_filter2_command = "java -jar ~/bin/GenomeAnalysisTK-3.3-0/GenomeAnalysisTK.jar -T VariantFiltration -R %s -o %s/%s_filter2_gatk.vcf --variant %s --filterExpression \"%s\" --filterName PASS_filter2" % (reference, out_path, analysis, final_raw_vcf, gatk_filter2_parameter_expression)
-    print "\n\nRunning Command: [%s]\n\n" % gatk_filter2_command
-    os.system(gatk_filter2_command)
-    filter_flag_command = "grep '#\|PASS_filter2' %s/%s_filter2_gatk.vcf > %s/%s_filter2_final.vcf" % (out_path, analysis, out_path, analysis)
-    os.system(filter_flag_command)
-    gatk_filter2_final_vcf = "%s/%s_filter2_final.vcf" % (out_path, analysis)
-    return gatk_filter2_final_vcf
-
-
-
-def remove_proximate_snps(gatk_filter2_final_vcf_file, out_path, analysis, reference):
-    all_position = []
-    remove_proximate_position_array = []
-    gatk_filter2_final_vcf_file_no_proximate_snp = gatk_filter2_final_vcf_file + "_no_proximate_snp.vcf"
-    with open(gatk_filter2_final_vcf_file, 'rU') as csv_file:
-        for line in csv_file:
-            if not line.startswith('#'):
-                line_array = line.split('\t')
-                all_position.append(line_array[1])
-    for position in all_position:
-        position_index = all_position.index(position)
-        next_position_index = position_index + 1
-
-        if next_position_index < len(all_position):
-            diff = int(all_position[next_position_index]) - int(position)
-            if diff < 10:
-                #print position + "  " + all_position[next_position_index]
-                if position not in remove_proximate_position_array and all_position[next_position_index] not in remove_proximate_position_array:
-                    remove_proximate_position_array.append(int(position))
-                    remove_proximate_position_array.append(int(all_position[next_position_index]))
-    #print remove_proximate_position_array
-    f1=open(gatk_filter2_final_vcf_file_no_proximate_snp, 'w+')
-    with open(gatk_filter2_final_vcf_file, 'rU') as csv_file2:
-        for line in csv_file2:
-            if line.startswith('gi') or line.startswith('MRSA_8058'):
-               line_array = line.split('\t')
-               if int(line_array[1]) not in remove_proximate_position_array:
-                   #print line_array[1]
-                   #print line_array[1]
-                   print_string = line
-                   f1.write(print_string)
-            else:
-                print_string = line
-                f1.write(print_string)
-    gatk_filter2_final_vcf_file_no_proximate_snp_positions = gatk_filter2_final_vcf_file + "_no_proximate_snp.vcf_positions_array"
-    f2=open(gatk_filter2_final_vcf_file_no_proximate_snp_positions, 'w+')
-    for i in remove_proximate_position_array:
-        position_print_string = str(i) + "\n"
-        f2.write(position_print_string)
-    return gatk_filter2_final_vcf_file_no_proximate_snp
-#remove_proximate_snps(gatk_filter2_final_vcf_file, out_path, analysis)
-
-gatk_filter2_final_vcf_file = gatk_filter2(final_raw_vcf, out_path, analysis, reference)
-
-remove_proximate_snps(gatk_filter2_final_vcf_file, out_path, analysis, reference)
diff --git a/other/README.md b/other/README.md
deleted file mode 100755
index edb0d76..0000000
--- a/other/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-# varcall_umich
-VariantCalling
diff --git a/other/__init__.py b/other/__init__.py
deleted file mode 100755
index 0b188d7..0000000
--- a/other/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__author__ = 'alipirani'
diff --git a/other/allele_frequency_analysis.py b/other/allele_frequency_analysis.py
deleted file mode 100755
index 26d1f36..0000000
--- a/other/allele_frequency_analysis.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from __future__ import division
-__author__ = 'alipirani'
-
-##Pending Changes
-
-import sys
-import os
-import argparse
-import errno
-import subprocess
-
-################################################### Command Line Argument Parsing ###################################################
-parser = argparse.ArgumentParser(description='Allele Frequency analysis: For the given variant positions; extract DP4 information from raw vcf file and calculate reference allele fraction and variant allele fraction')
-parser.add_argument('-raw_mpileup_file', action='store', dest="raw_mpileup_file", help='Path to raw_mpileup_file', required=True)
-parser.add_argument('-o', action='store', dest="output_folder", help='Output Path ending with output directory name to save the results', required=True)
-parser.add_argument('-position', action='store', dest="position", help='File containing positions', required=True)
-args = parser.parse_args()
-############################################################### End #################################################################
-print "Processing: %s..." % args.raw_mpileup_file
-position_array = []
-
-with open(args.position) as fp:
-        for line in fp:
-            line = line.strip()
-            position_array.append(int(line))
-out_file = args.output_folder + args.raw_mpileup_file + "_ref_fraction"
-#print position_array
-f1=open(out_file, 'w+')
-for i in position_array:
-    cmd = "grep -w \'" + str(i) + "\' " + args.raw_mpileup_file
-    proc = subprocess.Popen([cmd], stdout=subprocess.PIPE, shell=True)
-    (out, err) = proc.communicate()
-    if not out:
-        ref_fraction = "REF"
-	    f1.write(str(ref_fraction) + "\n")
-    else:
-        line_array = out.split('\t')
-        #print line_array[1]
-        format_string_array = line_array[7].split(';')
-        #print format_string_array
-        for i in format_string_array:
-            if i.startswith('DP4='):
-                allele_freq = i.split(',')
-                forward_ref = allele_freq[0].replace('DP4=', '')
-                reverse_ref = allele_freq[1]
-                forward_nonref = allele_freq[2]
-                reverse_nonref = allele_freq[3]
-                total = int(forward_ref) + int(reverse_ref) + int(forward_nonref) + int(reverse_nonref)
-                #print total
-                ref_alleles = (int(forward_ref) + int(reverse_ref))
-		        ref_fraction = ref_alleles / total
-                #print ref_alleles
-                #print total
-                f1.write(str(ref_fraction) + "\n")
-                #print ref_fraction
-
-
-
-
-
-
-
-
-
-
-
-
-
-#with open(args.raw_mpileup_file, 'rU') as csv_file2:
-        # f1=open(out_file, 'w+')
-        # for line in csv_file2:
-         #    if line.startswith('gi') or line.startswith('MRSA_8058'):
-         #        #print line
-         #        line_array = line.split('\t')
-		# #print line_array[1]
-         #        if int(line_array[1]) in position_array:
-         #            print line_array[1]
-         #            format_string_array = line_array[7].split(';')
-         #            print format_string_array
-         #            for i in format_string_array:
-         #                if i.startswith('DP4='):
-         #                    allele_freq = i.split(',')
-         #                    forward_ref = allele_freq[0].replace('DP4=', '')
-         #                    reverse_ref = allele_freq[1]
-         #                    forward_nonref = allele_freq[2]
-         #                    reverse_nonref = allele_freq[3]
-         #                    total = int(forward_ref) + int(reverse_ref) + int(forward_nonref) + int(reverse_nonref)
-		# 	                #print total
-         #                    ref_alleles = (int(forward_ref) + int(reverse_ref))
-         #                    print ref_alleles
-         #                    print total
-         #                    f1.write(str(ref_fraction) + "\n")
-         #                    #print ref_fraction
diff --git a/other/config_backup b/other/config_backup
deleted file mode 100755
index f0047a9..0000000
--- a/other/config_backup
+++ /dev/null
@@ -1,160 +0,0 @@
-# Set which tools to use in pipeline:
-[pipeline]
-# Options for Aligner:bwa / smalt / bowtie
-aligner: bwa
-# Options for variant_caller: samtoolswithpostalignbam / gatkhaplotypecaller /samtools
-variant_caller: samtools
-########################################################################################################################
-
-# Set bin folder path. Please make sure all the executables are placed in bin folder. Also make sure the path for individual tools are correct.
-[bin_path]
-binbase: /Users/alipirani/Documents/bin/bin_varcall/
-
-########################################################################################################################
-
-# Set Parameters for individual tools. Set the binbase of each tool: This should be the folder name of respective tools where the executables for each resp. tool resides.
-# Set parameter for Trimmomatic
-[Trimmomatic]
-trimmomatic_bin: /Trimmomatic/
-adaptor_filepath: adapters/TruSeq3-PE.fa
-seed_mismatches: 2
-palindrome_clipthreshold: 30
-simple_clipthreshold: 10
-minadapterlength: 8
-#change this to true and see the effect on alignment
-keep_both_reads: true
-window_size: 4
-window_size_quality: 20
-minlength: 40
-#headcrop_length: 8
-colon: :
-#clean_filenames: forward_paired.fq.gz,forward_unpaired.fq.gz,reverse_paired.fq.gz,reverse_unpaired.fq.gz
-targetlength: 125
-crop_length: 40
-f_p: forward_paired.fq.gz
-f_up: forward_unpaired.fq.gz
-r_p: reverse_paired.fq.gz
-r_up: reverse_unpaired.fq.gz
-
-[bwa]
-bwa_bin: /bwa-0.7.12/
-cores: 8
-base_cmd: bwa
-algorithm: mem
-index: index
-RG_header: -R
-Mark_splithits: -M
-
-[samtools]
-samtools_bin: /samtools-1.2/
-base_cmd: samtools
-#minimum mapping quality
-#change parameter S to -t SP and D to -t DP
-mpileup_parameters: -ug -f
-faiindex: faidx
-#-q30 -B -E -C50
-
-[bcftools]
-bcftools_bin: /bcftools-1.2/
-base_cmd: bcftools
-call_parameters: -vg
-
-[picard]
-picard_bin: /picard-tools-1.130/
-base_cmd: picard.jar
-
-[gatk]
-gatk_bin: /GenomeAnalysisTK-3.3-0/
-base_cmd: GenomeAnalysisTK.jar
-haplotype_parameters: -T HaplotypeCaller --genotyping_mode DISCOVERY
-#changes: 12th August
-gatk_filter1_parameter_expression = FQ < 40.00 && MQ > 20 && QUAL > 50 && DP > 15
-gatk_filter2_parameter_expression = FQ < 0.025 && MQ > 50 && QUAL > 100 && DP > 15
-#changed gatk_filter2_parameter_expression DP 15 from 10 that was used in VRE samples.
-
-
-
-
-[vcftools]
-vcftools_perl_bin: /vcftools_0.1.12b/perl/
-tabix_bin: /tabix-0.2.6/
-vcftools_bin: /vcftools_0.1.12b/bin/
-
-
-[qualimap]
-qualimap_bin: /qualimap/
-base_cmd: qualimap
-
-
-
-
-
-
-
-
-########################################################################################################################
-
-# Reference Genome to be used for pipeline
-# Set path for already indexed reference genome
-
-#Name of the reference genome. Provide this value with -index_name option.
-[KP_NTUH_chr]
-# Name of reference genome fasta file.
-Ref_Name: KP_NTUH_chr.fasta
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/bin_varcall/reference/KP_NTUH_chr/
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[KPNIH1]
-# Name of reference genome fasta file.
-Ref_Name: KPNIH1.fasta
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference/KPNIH1/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[KPNIH32]
-# Name of reference genome fasta file.
-Ref_Name: KPNIH32.fasta
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//KPNIH32/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[aus]
-# Name of reference genome fasta file.
-Ref_Name: Efae_aus0004_genome.fa
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//aus/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[EPI51]
-# Name of reference genome fasta file.
-Ref_Name: Efae_EPI51_060509_genome.fa
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//EPI51/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[EnGen0178]
-# Name of reference genome fasta file.
-Ref_Name: EnGen0178_genome.fa
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//EnGen0178/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[EnGen0377]
-# Name of reference genome fasta file.
-Ref_Name: EnGen0377_genome.fa
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//EnGen0377/
-
-[ECNIH3]
-# Name of reference genome fasta file.
-Ref_Name: ECNIH3.fa
-# path to the reference genome fasta file.
-Ref_Path: /home/apirani/bin/reference//ECNIH3/
-
-########################################################################################################################
\ No newline at end of file
diff --git a/other/config_default_21dec b/other/config_default_21dec
deleted file mode 100755
index 4e905a4..0000000
--- a/other/config_default_21dec
+++ /dev/null
@@ -1,172 +0,0 @@
-# Set which tools to use in pipeline:
-[pipeline]
-# Options for Aligner:bwa / smalt / bowtie
-aligner: bwa
-# Options for variant_caller: samtoolswithpostalignbam / gatkhaplotypecaller /samtools
-variant_caller: samtools
-########################################################################################################################
-
-# Set bin folder path. Please make sure all the executables are placed in bin folder. Also make sure the path for individual tools are correct.
-[bin_path]
-binbase: /home2/apirani/bin/
-
-########################################################################################################################
-
-# Set Parameters for individual tools. Set the binbase of each tool: This should be the folder name of respective tools where the executables for each resp. tool resides.
-# Set parameter for Trimmomatic
-[Trimmomatic]
-trimmomatic_bin: /Trimmomatic/
-adaptor_filepath: adapters/TruSeq3-PE.fa
-seed_mismatches: 2
-palindrome_clipthreshold: 30
-simple_clipthreshold: 10
-minadapterlength: 8
-#change this to true and see the effect on alignment
-keep_both_reads: true
-window_size: 4
-window_size_quality: 20
-minlength: 40
-headcrop_length: 18
-colon: :
-#clean_filenames: forward_paired.fq.gz,forward_unpaired.fq.gz,reverse_paired.fq.gz,reverse_unpaired.fq.gz
-targetlength: 125
-crop_length: 40
-f_p: forward_paired.fq.gz
-f_up: forward_unpaired.fq.gz
-r_p: reverse_paired.fq.gz
-r_up: reverse_unpaired.fq.gz
-
-[bwa]
-bwa_bin: /bwa-0.7.12/
-cores: 8
-base_cmd: bwa
-algorithm: mem
-index: index
-RG_header: -R
-Mark_splithits: -M
-
-[samtools]
-samtools_bin: /samtools-1.2/
-base_cmd: samtools
-#minimum mapping quality
-#change parameter S to -t SP and D to -t DP
-mpileup_parameters: -ug -f
-faiindex: faidx
-#-q30 -B -E -C50
-
-[bcftools]
-bcftools_bin: /bcftools-1.2/
-base_cmd: bcftools
-call_parameters: -vg
-
-[picard]
-picard_bin: /picard-tools-1.130/
-base_cmd: picard.jar
-
-[gatk]
-gatk_bin: /GenomeAnalysisTK-3.3-0/
-base_cmd: GenomeAnalysisTK.jar
-haplotype_parameters: -T HaplotypeCaller --genotyping_mode DISCOVERY
-#changes: 12th August
-gatk_filter1_parameter_expression = FQ < 40.00 && MQ > 20 && QUAL > 50 && DP > 15
-gatk_filter2_parameter_expression = FQ < 0.025 && MQ > 50 && QUAL > 100 && DP > 15
-#changed gatk_filter2_parameter_expression DP 15 from 10 that was used in VRE samples.
-
-
-
-
-[vcftools]
-#vcftools_perl_bin: /vcftools_0.1.12b/perl/
-vcftools_bin: /vcftools_0.1.12b/bin/
-tabix_bin: /tabix-0.2.6/
-#vcftools_bin: /vcftools_0.1.12b/bin/
-vcftools_perl_bin: /vcftools_0.1.12b/perl/
-
-
-[qualimap]
-qualimap_bin: /qualimap_v2.1/
-base_cmd: qualimap
-
-
-
-
-[bedtools]
-bedtools_bin: /bedtools2-master/bin/
-base_cmd: bedtools
-
-
-
-########################################################################################################################
-
-# Reference Genome to be used for pipeline
-# Set path for already indexed reference genome
-
-#Name of the reference genome. Provide this value with -index_name option.
-[KP_NTUH_chr]
-# Name of reference genome fasta file.
-Ref_Name: KP_NTUH_chr.fasta
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/bin_varcall/reference/KP_NTUH_chr/
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[KPNIH1]
-# Name of reference genome fasta file.
-Ref_Name: KPNIH1.fasta
-# path to the reference genome fasta file.
-Ref_Path: /home2/apirani/bin/reference/KPNIH1/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[KPNIH32]
-# Name of reference genome fasta file.
-Ref_Name: KPNIH32.fasta
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//KPNIH32/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[aus]
-# Name of reference genome fasta file.
-Ref_Name: Efae_aus0004_genome.fa
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//aus/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[EPI51]
-# Name of reference genome fasta file.
-Ref_Name: Efae_EPI51_060509_genome.fa
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//EPI51/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[EnGen0178]
-# Name of reference genome fasta file.
-Ref_Name: EnGen0178_genome.fa
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//EnGen0178/
-
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[EnGen0377]
-# Name of reference genome fasta file.
-Ref_Name: EnGen0377_genome.fa
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/bin_varcall/reference//EnGen0377/
-
-[ECNIH3]
-# Name of reference genome fasta file.
-Ref_Name: ECNIH3.fa
-# path to the reference genome fasta file.
-Ref_Path: /home/apirani/bin/reference//ECNIH3/
-
-
-[MRSA_USA_300]
-# Name of reference genome fasta file.
-Ref_Name: MRSA_USA_300.fasta
-# path to the reference genome fasta file.
-Ref_Path: /home2/apirani/bin/reference/MRSA_USA_300
-
-
-########################################################################################################################
diff --git a/other/config_gatk b/other/config_gatk
deleted file mode 100755
index 3241fe7..0000000
--- a/other/config_gatk
+++ /dev/null
@@ -1,93 +0,0 @@
-# Set which tools to use in pipeline:
-[pipeline]
-# Options for Aligner:bwa / smalt / bowtie
-aligner: bwa
-# Options for variant_caller: samtoolswithpostalignbam / gatkhaplotypecaller /samtools
-variant_caller: gatkhaplotypecaller
-########################################################################################################################
-
-# Set bin folder path. Please make sure all the executables are placed in bin folder. Also make sure the path for individual tools are correct.
-[bin_path]
-binbase: /Users/alipirani/Documents/bin/
-
-########################################################################################################################
-
-# Set Parameters for individual tools. Set the binbase of each tool: This should be the folder name of respective tools where the executables for each resp. tool resides.
-# Set parameter for Trimmomatic
-[Trimmomatic]
-trimmomatic_bin: /Trimmomatic/
-adaptor_filepath: adapters/TruSeq3-PE.fa
-seed_mismatches: 2
-palindrome_clipthreshold: 30
-simple_clipthreshold: 10
-minadapterlength: 8
-keep_both_reads: false
-window_size: 4
-window_size_quality: 15
-minlength: 40
-headcrop_length: 8
-colon: :
-#clean_filenames: forward_paired.fq.gz,forward_unpaired.fq.gz,reverse_paired.fq.gz,reverse_unpaired.fq.gz
-targetlength: 125
-crop_length: 40
-f_p: forward_paired.fq.gz
-f_up: forward_unpaired.fq.gz
-r_p: reverse_paired.fq.gz
-r_up: reverse_unpaired.fq.gz
-
-[bwa]
-bwa_bin: /bwa-0.7.12/
-cores: 8
-base_cmd: bwa
-algorithm: mem
-index: index
-RG_header: -R
-Mark_splithits: -M
-
-[samtools]
-samtools_bin: /samtools-1.2/
-base_cmd: samtools
-#minimum mapping quality
-#change parameter S to -t SP and D to -t DP
-mpileup_parameters: -ug -f
-faiindex: faidx
-#-q30 -B -E -C50
-
-[bcftools]
-bcftools_bin: /bcftools-1.2/
-base_cmd: bcftools
-call_parameters: -vg
-
-[picard]
-picard_bin: /picard-tools-1.130/
-base_cmd: picard.jar
-
-[gatk]
-gatk_bin: /GenomeAnalysisTK-3.3-0/
-base_cmd: GenomeAnalysisTK.jar
-haplotype_parameters: -T HaplotypeCaller --genotyping_mode DISCOVERY
-
-[vcftools]
-vcftools_bin: /vcftools_0.1.12b/perl/
-tabix_bin: /tabix-0.2.6/
-
-########################################################################################################################
-
-# Reference Genome to be used for pipeline
-# Set path for already indexed reference genome
-
-#Name of the reference genome. Provide this value with -index_name option.
-[KP_NTUH_chr]
-# Name of reference genome fasta file.
-Ref_Name: KP_NTUH_chr.fasta
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/reference/KP_NTUH_chr/
-
-# Name of the reference genome. Provide this value with -index_name argument.
-[KPNIH1]
-# Name of reference genome fasta file.
-Ref_Name: KPNIH1.fasta
-# path to the reference genome fasta file.
-Ref_Path: /Users/alipirani/Documents/bin/reference/KPNIH1/
-
-########################################################################################################################
\ No newline at end of file
diff --git a/other/pipeline.py.save b/other/pipeline.py.save
deleted file mode 100755
index 301b4cb..0000000
--- a/other/pipeline.py.save
+++ /dev/null
@@ -1,139 +0,0 @@
-__author__ = 'alipirani'
-
-import sys
-import os
-import argparse
-import errno
-from config_settings import ConfigSectionMap
-from modules/check_subroutines import *
-from modules/stages import *
-from modules/remove_5_bp_snp_indel import *
-from modules/bedtools import *
-
-################################################### Command Line Argument Parsing ###################################################
-parser = argparse.ArgumentParser(description='Variant Calling pipeline for Illumina PE data.')
-parser.add_argument('-PE1', action='store', dest="forward_raw", help='Path to Paired End file 1', required=True)
-parser.add_argument('-PE2', action='store', dest="reverse_raw", help='Path to Paired End file 2', required=True)
-parser.add_argument('-o', action='store', dest="output_folder", help='Output Path ending with output directory name to save the results', required=True)
-parser.add_argument('-analysis', action='store', dest="analysis_name", help='Unique analysis name to save the results', required=True)
-parser.add_argument('-c', action='store', dest="croplength", help='Crop Length in case needed')
-parser.add_argument('-f', action='store', dest="bam_input", help='Input Bam')
-parser.add_argument('-index', action='store', dest="index", help='Reference Index Name. Change this argument in config file and mention the reference header name such as KP_NTUH_chr/KPNIH1/KPNIH32.', required=True)
-#parser.add_argument('-r', action='store', dest="reference", help='Reference Fasta File to build index in case index not built')
-args = parser.parse_args()
-############################################################### End #################################################################
-
-print "\n################## Checking Dependencies ##################\n"
-################################################### Check Subroutines: Arguments, Input files, Reference Index ######################
-reference = ConfigSectionMap(args.index)['ref_path'] + "/" + ConfigSectionMap(args.index)['ref_name']
-out_path = args.output_folder + "/"
-make_sure_path_exists(out_path)
-file_exists(args.forward_raw, args.reverse_raw, reference)
-java_check()
-config_file_backup = "cp ./config %s/%s_config_log" % (out_path, args.analysis_name)
-os.system(config_file_backup)
-################################################################### End ############################################################
-print "\n############# End: Checking Dependencies ##################\n"
-
-
-print "\n################## Cleaning Raw reads using Trimmomatic ##################\n"
-################################################### Stages: Raw data Pre-processing using Trimmomatic ###############################
-trimmomatic(args.forward_raw, args.reverse_raw, out_path, args.croplength)
-####################################################################### End #########################################################
-print "\n############# End: Cleaning Raw reads using Trimmomatic ##################\n"
-
-
-print "\n################## Mapping Reads using BWA ##################\n"
-#################################################### Stages: Alignment ##############################################################
-split_field = prepare_readgroup(args.forward_raw)
-files_to_delete = []
-out_sam = align(args.bam_input, args.output_folder, args.index, split_field, args.analysis_name, files_to_delete)
-#out_sam = "/nfs/esnitkin/Ali/Nextera_XT_lib_samples/varcall/snitkin6_S180_L001_R2_001/snitkin6_S180_L001_R2_001_aln.sam"
-out_sorted_bam = prepare_bam(out_sam, out_path, args.analysis_name, files_to_delete)
-#out_sorted_bam = "%s/%s_aln_marked.bam" % (out_path, args.analysis_name)
-############################################################### End #################################################################
-print "\n############# End: Mapping Reads using BWA ##################\n"
-
-
-print "\n################## Creating BedGraph Coverage ##################\n"
-#################################################### Stages: BedGraph Coverage ######################################################
-bedgraph_coverage(out_sorted_bam, out_path, args.analysis_name, reference)
-only_unmapped_positions_file = bedtools(out_sorted_bam, out_path, args.analysis_name)
-######################################################### End #######################################################################
-print "\n############# End: Creating BedGraph Coverage ##################\n"
-
-
-print "\n################## Variant Calling ########################\n"
-##################################################### Stages: Variant Calling #######################################################
-caller = ConfigSectionMap("pipeline")['variant_caller']
-if caller == "samtoolswithpostalignbam":
-    print "\n################## Variant Calling using Samtools and post-align bam input files ##################\n"
-    out_finalbam = post_align_bam(out_sorted_bam, out_path, args.index, args.analysis_name)
-    final_raw_vcf = variant_calling(out_finalbam, out_path, args.index, args.analysis_name)
-    print "The final raw VCF file: %s" % final_raw_vcf
-    print "\n############################ END: samtoolswithpostalignbam Variant Calling. #######################\n"
-elif caller == "gatkhaplotypecaller":
-    print "\n################## Variant Calling using GATK haplotyper and post-align bam input files ##################\n"
-    out_finalbam = post_align_bam(out_sorted_bam, out_path, args.index, args.analysis_name)
-    final_raw_vcf = variant_calling(out_finalbam, out_path, args.index, args.analysis_name)
-    print "The final raw VCF file: %s" % final_raw_vcf
-    print "\n############################# END: gatkhaplotypecaller Variant Calling. ###################################\n"
-elif caller == "samtools":
-    print "\n################## Variant Calling using Samtools without post-align bam input files.##################\n"
-    final_raw_vcf_mpileup = variant_calling(out_sorted_bam, out_path, args.index, args.analysis_name)
-    #final_raw_vcf_mpileup = "%s/%s_aln_mpileup_raw.vcf" % (out_path, args.analysis_name)
-    final_raw_vcf = remove_5_bp_snp_indel(final_raw_vcf_mpileup, out_path, args.analysis_name, reference)
-    print "The final raw VCF file: %s" % final_raw_vcf
-    print "\n################################ END: samtools Variant Calling. ########################################\n"
-else:
-    print "\nPlease provide Variant Caller name in config file under the section [pipeline].\n Options for Variant caller:\n samtools\nsamtoolswithpostalignbam\ngatkhaplotypecaller\n"
-    exit()
-    usage()
-############################################################ End ####################################################################
-print "\n################## End: Variant Calling ##################\n"
-
-
-print "\n################## Variant Filteration #######################\n"
-#################################################### Stages: Variant Filteration ####################################################
-#filter1_variants(final_raw_vcf, out_path, args.analysis_name, args.index)
-filter2_variants(final_raw_vcf, out_path, args.analysis_name, args.index)
-################################################################## END ##############################################################
-print "\n################## End: Variant Filteration ##################\n"
-
-
-
-print "\n################## Generating Alignment and VCF Statistics #######################\n"
-#################################################### Stages: Statistics #############################################################
-alignment_stats_file = alignment_stats(out_sorted_bam, out_path, args.analysis_name)
-########################################################## End ######################################################################
-print "\n################## Calculating Depth of Coverage ##################################\n"
-gatk_DepthOfCoverage(out_sorted_bam, out_path, args.analysis_name, reference)
-########################################################## End ######################################################################
-################################################### Stages: VCF Statistics ##########################################################
-vcf_stats_file = vcf_stats(final_raw_vcf, out_path, args.analysis_name)
-############################################################################ End ####################################################
-print "\n############### END: Generating Alignment and VCF Statistics ######################\n"
-
-
-
-print "\n################## Generating Qualimap Report #######################\n"
-################################################### Stages: Qualimap ################################################################
-qualimap_report = qualimap(out_sorted_bam, out_path, args.analysis_name)
-########################################################## End ######################################################################
-
-################################################### Stages: Remove Unwanted Intermediate files ######################################
-# print "Removing Imtermediate Files...\n%s" % files_to_delete
-# for files in files_to_delete:
-#     os.remove(files)
-# print "Removing Imtermediate Files...\n%s" % files_to_delete
-# for files in files_to_delete:
-#     os.remove(files)
-############################################################################ End ####################################################
-
-
-
-
-
-
-
-
diff --git a/other/pipeline_gatk.py b/other/pipeline_gatk.py
deleted file mode 100755
index ab8d12d..0000000
--- a/other/pipeline_gatk.py
+++ /dev/null
@@ -1,101 +0,0 @@
-__author__ = 'alipirani'
-# Back up file
-import sys
-import os
-import argparse
-from config_settings import ConfigSectionMap
-from modules.check_subroutines import *
-from modules.stages import *
-
-################################################### Command Line Argument Parsing ########################################################################################################################
-parser = argparse.ArgumentParser(description='Assembly pipeline for Illumina PE data')
-parser.add_argument('-PE1', action='store', dest="forward_raw", help='Paired End file 1', required=True)
-parser.add_argument('-PE2', action='store', dest="reverse_raw", help='Paired End file 2', required=True)
-parser.add_argument('-o', action='store', dest="output_folder", help='Output Path ending with output directory name to save the results', required=True)
-parser.add_argument('-analysis', action='store', dest="analysis_name", help='Unique analysis name to save the results', required=True)
-parser.add_argument('-c', action='store', dest="croplength", help='Crop Length in case needed')
-parser.add_argument('-f', action='store', dest="bam_input", help='Input Bam')
-parser.add_argument('-index_name', action='store', dest="index", help='Reference Index Name', required=True)
-parser.add_argument('-r', action='store', dest="reference", help='Reference Fasta File to build index in case index not built')
-args = parser.parse_args()
-############################################################################ End #########################################################################################################################
-
-################################################### Check Subroutines: Arguments, Input files, Reference Index ###########################################################################################
-reference = ConfigSectionMap(args.index)['ref_path'] + "/" + ConfigSectionMap(args.index)['ref_name']
-out_path = args.output_folder + "/"
-make_sure_path_exists(out_path)
-file_exists(args.forward_raw, args.reverse_raw, reference)
-#fileformat(args.forward_raw, args.reverse_raw, out_path)
-java_check()
-############################################################################ End #########################################################################################################################
-
-
-
-#
-# ################################################### Stages: Raw data Pre-processing using Trimmomatic ####################################################################################################
-# trimmomatic(args.forward_raw, args.reverse_raw, out_path, args.croplength)
-# ############################################################################ End #########################################################################################################################
-#
-#
-# # ################################################### Stages: Alignment ####################################################################################################################################
-# split_field = prepare_readgroup(args.forward_raw)
-# out_sam = align(args.bam_input, args.output_folder, args.index, split_field, args.analysis_name)
-# #out_sam = "~/Desktop/test/aln.sam"
-# out_sorted_bam = prepare_bam(out_sam, out_path, args.analysis_name)
-# #out_sorted_bam = "/Users/alipirani/Desktop/sample_19/aln_sort.bam"
-# # ############################################################################ End #########################################################################################################################
-#
-#
-
-# ################################################### Stages: Variant Calling ##############################################################################################################################
-
-out_sorted_bam = out_path + "/" + args.analysis_name + "_aln_sort_bam"
-caller = ConfigSectionMap("pipeline")['variant_caller']
-#print caller
-if caller == "samtoolswithpostalignbam":
-    print "\n################## Variant Calling using Samtools and post-align bam input files ##################\n"
-    out_finalbam = post_align_bam(out_sorted_bam, out_path, args.index, args.analysis_name)
-    final_raw_vcf = variant_calling(out_finalbam, out_path, args.index, args.analysis_name)
-    print "The final raw VCF file: %s" % final_raw_vcf
-    print "\n############################ END: samtoolswithpostalignbam Variant Calling. #######################\n"
-elif caller == "gatkhaplotypecaller":
-    print "\n################## Variant Calling using GATK haplotyper and post-align bam input files ##################\n"
-    out_finalbam = post_align_bam(out_sorted_bam, out_path, args.index, args.analysis_name)
-    final_raw_vcf = variant_calling(out_finalbam, out_path, args.index, args.analysis_name)
-    print "The final raw VCF file: %s" % final_raw_vcf
-    print "\n############################# END: gatkhaplotypecaller Variant Calling. ###################################\n"
-elif caller == "samtools":
-    print "\n################## Variant Calling using Samtools without post-align bam input files.##################\n"
-    final_raw_vcf = variant_calling(out_sorted_bam, out_path, args.index, args.analysis_name)
-    print "The final raw VCF file: %s" % final_raw_vcf
-    print "\n################################ END: samtools Variant Calling. ########################################\n"
-else:
-    print "\nPlease provide Variant Caller name in config file under the section [pipeline].\n Options for Variant caller:\n samtools\nsamtoolswithpostalignbam\ngatkhaplotypecaller\n"
-    exit()
-############################################################################ End #########################################################################################################################
-
-################################################### Stages: Statistics ##############################################################################################################################
-alignment_stats_file = alignment_stats(out_sorted_bam, out_path)
-#final_vcf_stats = vcf_stats(final_raw_vcf, out_path)
-############################################################################ End #########################################################################################################################
-
-################################################### Stages: VCF Statistics ##############################################################################################################################
-#final_raw_vcf = "/Users/alipirani/Desktop/testing_analysis_name///test_purpose_aln_mpileup_raw.vcf"
-#print final_raw_vcf
-vcf_stats_file = vcf_stats(final_raw_vcf, out_path)
-############################################################################ End #########################################################################################################################
-
-
-################################################### Stages: Remove Unwanted Intermediate files ##############################################################################################################################
-#out_sam = "/Users/alipirani/Desktop/testing_analysis_name///test_purpose_aln.sam"
-#remove_files(args.analysis_name, out_path, out_sam, out_sorted_bam)
-############################################################################ End #########################################################################################################################
-
-
-
-
-
-
-
-
-
diff --git a/pipeline.png b/pipeline.png
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diff --git a/pipeline.py b/pipeline.py
index 4dc4899..c0ef03b 100755
--- a/pipeline.py
+++ b/pipeline.py
@@ -38,7 +38,7 @@ def parser():
                                                                      '2.   clean,align,post-align,varcall,filter,stats : This will also run all steps starting from cleaning to variant calling. \nYou can also run part of the pipeline by giving "align,post-align,varcall,filter,stats" which will skip the cleaning part.\nThe order is required to be sequential. Also, while skipping any of the step make sure you have results already present in your output folder.\n'
                                                                      '3.   coverage_depth_stats: Run Only Depth of Coverage Stats module after cleaning and read mapping steps')
     optional.add_argument('-cluster', action='store', dest='cluster', help='Run pipeline on cluster/parallel-local/local. Make Sure to check if the [CLUSTER] section in config file is set up correctly.')
-    #optional.add_argument('-noclean', action='store', dest="noclean", help='Do not clean up intermediate files. Default: OFF')
+    optional.add_argument('-clean', action="store_true", help='clean up intermediate files. Default: OFF')
     return parser
 
 # Main Pipeline method
@@ -118,29 +118,31 @@ def post_align(out_sam):
     def varcall():
         keep_logging('START: Variant Calling', 'START: Variant Calling', logger, 'info')
         caller = ConfigSectionMap("pipeline", Config)['variant_caller']
-        if caller == "samtoolswithpostalignbam":
-            keep_logging('START: Variant Calling using Samtools and post-align bam input files', 'START: Variant Calling using Samtools and post-align bam input files', logger, 'info')
-            out_finalbam = post_align_bam(out_sorted_bam, args.output_folder, args.index, args.analysis_name)
-            final_raw_vcf = variant_calling(out_finalbam, args.output_folder, args.index, args.analysis_name)
-            keep_logging('The final raw VCF file: {}'.format(final_raw_vcf), 'The final raw VCF file: {}'.format(final_raw_vcf), logger, 'debug')
-            keep_logging('END: Variant Calling using Samtools and post-align bam input files', 'END: Variant Calling using Samtools and post-align bam input files', logger, 'info')
-        elif caller == "gatkhaplotypecaller":
-            keep_logging('START: Variant Calling using GATK haplotyper and post-align bam input files', 'START: Variant Calling using GATK haplotyper and post-align bam input files', logger, 'info')
-            out_finalbam = post_align_bam(out_sorted_bam, args.output_folder, args.index, args.analysis_name)
-            final_raw_vcf = variant_calling(out_finalbam, args.output_folder, args.index, args.analysis_name)
+        if caller == "gatkhaplotypecaller":
+            keep_logging('START: Variant Calling using GATK haplotyper.', 'START: Variant Calling using GATK haplotyper.', logger, 'info')
+            final_raw_vcf_mpileup = variant_calling(out_sorted_bam, args.output_folder, args.index, args.analysis_name, logger, Config)
+            #final_raw_vcf_mpileup = "%s/%s_aln_mpileup_raw.vcf" % (args.output_folder, args.analysis_name)
+            final_raw_vcf = remove_5_bp_snp_indel(final_raw_vcf_mpileup, args.output_folder, args.analysis_name, reference, logger, Config)
+            final_raw_indel_vcf = prepare_indel(final_raw_vcf_mpileup, args.output_folder, args.analysis_name, reference, logger, Config)
             keep_logging('The final raw VCF file: {}'.format(final_raw_vcf), 'The final raw VCF file: {}'.format(final_raw_vcf), logger, 'debug')
-            keep_logging('END: Variant Calling using GATK haplotyper and post-align bam input files', 'END: Variant Calling using GATK haplotyper and post-align bam input files', logger, 'info')
+            keep_logging('The final raw Indel VCF file: {}'.format(final_raw_indel_vcf),
+                         'The final raw Indel VCF file: {}'.format(final_raw_indel_vcf), logger, 'debug')
+            keep_logging('END: Variant Calling using Samtools without post-align bam input files.', 'END: Variant Calling using Samtools without post-align bam input files.', logger, 'info')
+            return final_raw_vcf, final_raw_indel_vcf
+
         elif caller == "samtools":
             keep_logging('START: Variant Calling using Samtools without post-align bam input files.', 'START: Variant Calling using Samtools without post-align bam input files.', logger, 'info')
+            final_raw_indel_vcf = prepare_indel_gatk(out_sorted_bam, args.output_folder, args.analysis_name, args.index, logger, Config)
             final_raw_vcf_mpileup = variant_calling(out_sorted_bam, args.output_folder, args.index, args.analysis_name, logger, Config)
             #final_raw_vcf_mpileup = "%s/%s_aln_mpileup_raw.vcf" % (args.output_folder, args.analysis_name)
             final_raw_vcf = remove_5_bp_snp_indel(final_raw_vcf_mpileup, args.output_folder, args.analysis_name, reference, logger, Config)
-            final_raw_indel_vcf = prepare_indel(final_raw_vcf_mpileup, args.output_folder, args.analysis_name, reference, logger, Config)
+            # GATK indel calling integration
+            #final_raw_indel_vcf = prepare_indel(final_raw_vcf_mpileup, args.output_folder, args.analysis_name, reference, logger, Config)
             keep_logging('The final raw VCF file: {}'.format(final_raw_vcf), 'The final raw VCF file: {}'.format(final_raw_vcf), logger, 'debug')
             keep_logging('END: Variant Calling using Samtools without post-align bam input files.', 'END: Variant Calling using Samtools without post-align bam input files.', logger, 'info')
             return final_raw_vcf, final_raw_indel_vcf
         else:
-            keep_logging('Please provide Variant Caller name in config file under the section [pipeline]. Options for Variant caller: 1. samtools 2. samtoolswithpostalignbam 3. gatkhaplotypecaller', 'Please provide Variant Caller name in config file under the section [pipeline]. Options for Variant caller: 1. samtools 2. samtoolswithpostalignbam 3. gatkhaplotypecaller', logger, 'info')
+            keep_logging('Please provide Variant Caller name in config file under the section [pipeline]. Options for Variant caller: 1. samtools 2. gatkhaplotypecaller', 'Please provide Variant Caller name in config file under the section [pipeline]. Options for Variant caller: 1. samtools 2. gatkhaplotypecaller', logger, 'info')
             exit()
         keep_logging('END: Variant Calling', 'END: Variant Calling', logger, 'info')
 
@@ -148,7 +150,7 @@ def varcall():
     def filter(gatk_depth_of_coverage_file):
         keep_logging('START: Variant Filteration', 'START: Variant Filteration', logger, 'info')
         final_raw_vcf_mpileup = "%s/%s_aln_mpileup_raw.vcf" % (args.output_folder, args.analysis_name)
-        final_raw_indel_vcf = prepare_indel(final_raw_vcf_mpileup, args.output_folder, args.analysis_name, reference, logger, Config)
+        #final_raw_indel_vcf = prepare_indel(final_raw_vcf_mpileup, args.output_folder, args.analysis_name, reference, logger, Config)
         if not os.path.isfile(gatk_depth_of_coverage_file):
             file_basename = os.path.basename(gatk_depth_of_coverage_file)
             keep_logging('The input file {} does not exists. Please provide another file with full path or check the files path.\n'.format(file_basename), 'The input file {} does not exists. Please provide another file or check the files path.\n'.format(file_basename), logger, 'exception')
@@ -158,8 +160,9 @@ def filter(gatk_depth_of_coverage_file):
         (out, err) = proc.communicate()
         Avg_dp = float(out)
         print "The Average Depth per reference genome base is: %s" % Avg_dp
-        filter2_variants(final_raw_vcf, args.output_folder, args.analysis_name, args.index, logger, Config, Avg_dp)
-        filter2_indels(final_raw_indel_vcf, args.output_folder, args.analysis_name, args.index, logger, Config, Avg_dp)
+        filter_variants(final_raw_vcf, args.output_folder, args.analysis_name, args.index, logger, Config, Avg_dp)
+        final_raw_indel_vcf = final_raw_vcf_mpileup + "_indel.vcf"
+        filter_indels(final_raw_indel_vcf, args.output_folder, args.analysis_name, args.index, logger, Config, Avg_dp)
         keep_logging('END: Variant Filteration', 'END: Variant Filteration', logger, 'info')
 
     ## 6. Stages: Statistics
@@ -190,7 +193,7 @@ def stats():
         if steps_list[0] == "filter":
             #Sanity Check Post-varcall vcf and other files here
             out_sorted_bam = "%s/%s_aln_sort.bam" % (args.output_folder, args.analysis_name)
-            final_raw_vcf = "%s/%s_aln_mpileup_raw.vcf_5bp_indel_removed.vcf.gz" % (args.output_folder, args.analysis_name)
+            final_raw_vcf = "%s/%s_aln_mpileup_raw.vcf_5bp_indel_removed.vcf" % (args.output_folder, args.analysis_name)
             gatk_depth_of_coverage_file = "%s/%s_depth_of_coverage.sample_summary" % (args.output_folder, args.analysis_name)
             final_raw_vcf_mpileup = "%s/%s_aln_mpileup_raw.vcf" % (args.output_folder, args.analysis_name)
             if not os.path.exists(gatk_depth_of_coverage_file):
@@ -217,6 +220,17 @@ def stats():
         elif steps_list[0] == "bedtools":
                 out_sorted_bam = "%s/%s_aln_sort.bam" % (args.output_folder, args.analysis_name)
                 only_unmapped_positions_file = bedtools(out_sorted_bam, args.output_folder, args.analysis_name, logger, Config)
+
+        elif steps_list[0] == "varcall":
+            out_sorted_bam = "%s/%s_aln_sort.bam" % (args.output_folder, args.analysis_name)
+            gatk_depth_of_coverage_file = "%s/%s_depth_of_coverage.sample_summary" % (
+            args.output_folder, args.analysis_name)
+            if not os.path.exists(gatk_depth_of_coverage_file):
+                gatk_depth_of_coverage_file = coverage_depth_stats()
+            final_raw_vcf, final_raw_indel_vcf = varcall()
+
+
+
     # Run individual variant calling steps: clean, align, post-align, varcall, filter, stats etc
     else:
 
@@ -261,7 +275,7 @@ def stats():
                 gatk_depth_of_coverage_file = coverage_depth_stats()
             final_raw_vcf, final_raw_indel_vcf = varcall()
             filter(gatk_depth_of_coverage_file)
-            #stats()
+            stats()
 
         elif steps_list[0] == "filter":
             #Sanity Check Post-varcall vcf and other files here
@@ -434,6 +448,18 @@ def picard_seqdict(dict_name, reference):
         keep_logging('Error in Picard Sequence Dictionary creation step. Exiting.', 'Error in Picard Sequence Dictionary creation step. Exiting.', logger, 'exception')
         sys.exit(1)
 
+def cleanup(args, logger):
+    if args.clean:
+        keep_logging('Removing Intermediate Temporary files', 'Removing Intermediate Temporary files', logger, 'info')
+        os.chdir(args.output_folder)
+        os.system("rm %s/*.fq.gz %s/*.sam %s/*_aln.bam %s/*_marked.bam %s/*_marked.bai %s/*_unmapped.bed %s/*_temp_paste_file.txt" % (args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder))
+    make_sure_path_exists("%s/%s_stats_results" % (args.output_folder, args.analysis_name))
+    os.system("mv %s/*depth_of_coverage* %s/*_stats %s/*_markduplicates_metrics %s/*_values.txt %s/*_INFO.txt %s/%s_stats_results" % (args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.analysis_name))
+    make_sure_path_exists("%s/%s_logs" % (args.output_folder, args.analysis_name))
+    os.system("mv %s/*.log.txt %s/%s_logs" % (args.output_folder, args.output_folder, args.analysis_name))
+    make_sure_path_exists("%s/%s_vcf_results" % (args.output_folder, args.analysis_name))
+    os.system("mv %s/header.txt %s/*.vcf* %s/%s_vcf_results" % (args.output_folder, args.output_folder, args.output_folder, args.analysis_name))
+
 # Start of Main Method/Pipeline
 if __name__ == '__main__':
     start_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
@@ -455,7 +481,8 @@ def picard_seqdict(dict_name, reference):
     files_to_delete = []
     Config = ConfigParser.ConfigParser()
     Config.read(config_file)
-    pipeline(args, logger)
+    #pipeline(args, logger)
+    cleanup(args, logger)
     keep_logging('End: Pipeline', 'End: Pipeline', logger, 'info')
     time_taken = datetime.now() - start_time_2
     keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
diff --git a/plots/Sample_barplot.png b/plots/Sample_barplot.png
deleted file mode 100644
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V$JKN%`+IjkCHZ%+zss6_{V!1^9BTjo

diff --git a/variant_call.py b/variant_call.py
index ea74f0f..f2769c5 100644
--- a/variant_call.py
+++ b/variant_call.py
@@ -29,17 +29,14 @@ def parser():
     required.add_argument('-outdir', action='store', dest="output_folder", help='Output Folder Path ending with output directory name to save the results. Creates a new output directory path if it doesn\'t exist. NOTE: Provide full/absolute path.', required=True)
     required.add_argument('-index', action='store', dest="index", help='Reference Index Name. Most Frequently used reference genomes index options: KPNIH1 | MRSA_USA_300 | MRSA_USA_100 | CDIFF_630 | paris Make sure the paths are properly set in config file', required=True)
     required.add_argument('-steps', action='store', dest="steps", help='Variant Calling Steps in sequential order.\n'
-                                                                     '1.   All: This will run all the steps starting from cleaning the reads to variant calling;\n'
-                                                                     '2.   clean,align,post-align,varcall,filter,stats : This will also run all steps starting from cleaning to variant calling. \nYou can also run part of the pipeline by giving "align,post-align,varcall,filter,stats" which will skip the cleaning part.\nThe order is required to be sequential while using this option. Also, while skipping any of the step make sure you have results already present in your output folder.\n'
-                                                                     '3.   coverage_depth_stats: Run Only Depth of Coverage Stats module after cleaning and read mapping steps\n'
-                                                                     '4.   core_prep: Run this step before running the core steps. This will prepare the data required for generating core SNPs\n'
-                                                                     '5.   core: extract core snps and generate diagnostics plot data matrices to explore filtered snps.')
+                                                                     '1.   All: Run all variant calling  steps starting from trimming the reads, mapping, post-processing the alignments and calling variants;\n'
+                                                                     '2.   core_All: Extract core snps and generate different types of alignments, SNP/Indel Matrices and diagnostics plots.')
     required.add_argument('-analysis', action='store', dest="analysis_name", help='Unique analysis name that will be used as prefix to saving results and log files.', required=True)
     optional.add_argument('-config', action='store', dest="config", help='Path to Config file, Make sure to check config settings before running pipeline', required=False)
     optional.add_argument('-suffix', action='store', dest="suffix", help='Fastq reads suffix such as fastq, fastq.gz, fq.gz, fq; Default: fastq.gz', required=False)
     optional.add_argument('-filenames', action='store', dest="filenames", help='fastq filenames with one single-end filename per line. \nIf the type is set to PE, it will detect the second paired-end filename with the suffix from first filename. \nUseful for running variant calling pipeline on selected files in a reads directory or extracting core snps for selected samples in input reads directory. \nOtherwise the pipeline will consider all the samples available in reads directory.', required=False)
-    optional.add_argument('-cluster', action='store', dest='cluster', help='Run variant calling pipeline in one of the four modes. Default: local. Suggested mode for core snp is cluster that will run all the steps in parallel with the available cores. Make sure to provide a large memory node for this option\nThe possible modes are: cluster/parallel-cluster/parallel-local/local\ncluster: Runs all the jobs on a single large cluster. This will mimic the local run but rather on a large compute node.\nparallel-cluster: Submit variant call jobs for each sample in parallel on compute nodes. This mode is no available for core snp extraction step.\nparallel-local: Run variant call jobs for each sample in parallel locally.\nlocal: Run variant call jobs locally.\nMake Sure to check if the [scheduler] section in config file is set up correctly for your cluster.')
-    optional.add_argument('-clean', action='store', dest="clean", help='clean up intermediate files. Default: OFF')
+    optional.add_argument('-cluster', action='store', dest='cluster', help='Run variant calling pipeline in one of the four modes. Default: local. Suggested mode for core snp is \"cluster\" that will run all the steps in parallel with the available cores. Make sure to provide a large memory node for this option\nThe possible modes are: cluster/parallel-local/local\nSet your specific hpc cluster parameters in config file under the [scheduler] section. Supports only PBS scheduling system. ')
+    optional.add_argument('-clean', action="store_true", help='clean up intermediate files. Default: OFF')
     optional.add_argument('-extract_unmapped', action='store', dest="extract_unmapped", help='Extract unmapped reads, assemble it and detect AMR genes using ariba')
     optional.add_argument('-datadir', action='store', dest="datadir", help='Path to snpEff data directory')
     optional.add_argument('-snpeff_db', action='store', dest="snpeff_db", help='Name of pre-build snpEff database to use for Annotation')
@@ -164,9 +161,20 @@ def create_varcall_jobs(filenames_array, type, output_folder, reference, steps,
             if not steps:
                 steps == "All"
             if type == "SE":
-                command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -o %s/%s -analysis %s -index %s -type SE -config %s -steps %s" % (os.path.dirname(os.path.abspath(__file__)), first_file, output_folder, first_part, first_part, reference, config_file, steps)
+                if args.clean:
+                    command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -o %s/%s -analysis %s -index %s -type SE -config %s -steps %s -clean" % (
+                    os.path.dirname(os.path.abspath(__file__)), first_file, output_folder, first_part, first_part,
+                    reference, config_file, steps)
+                else:
+                    command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -o %s/%s -analysis %s -index %s -type SE -config %s -steps %s" % (os.path.dirname(os.path.abspath(__file__)), first_file, output_folder, first_part, first_part, reference, config_file, steps)
+
             else:
-                command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -PE2 %s -o %s/%s -analysis %s -index %s -type PE -config %s -steps %s" % (os.path.dirname(os.path.abspath(__file__)), first_file, second_file, output_folder, first_part, first_part, reference, config_file, steps)
+                if args.clean:
+                    command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -PE2 %s -o %s/%s -analysis %s -index %s -type PE -config %s -steps %s -clean" % (
+                    os.path.dirname(os.path.abspath(__file__)), first_file, second_file, output_folder, first_part,
+                    first_part, reference, config_file, steps)
+                else:
+                    command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -PE2 %s -o %s/%s -analysis %s -index %s -type PE -config %s -steps %s" % (os.path.dirname(os.path.abspath(__file__)), first_file, second_file, output_folder, first_part, first_part, reference, config_file, steps)
 
             with open(job_name, 'w') as out:
                 job_title = "#PBS -N %s" % first_part
@@ -264,32 +272,40 @@ def run_varcall_jobs(list_of_jobs, cluster, log_unique_time, analysis_name, outp
 
     #cluster mode
     if cluster == "cluster":
-        keep_logging('Running Job in cluster mode', 'Running Job in cluster mode', logger, 'info')
-        cluster_job_dir = output_folder + "/temp_jobs/cluster/"
-        make_sure_path_exists(cluster_job_dir)
-        Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-        job_name = cluster_job_dir + log_unique_time + "_" + analysis_name + ".pbs"
-        with open(job_name, 'w') as out:
-            job_title = "#PBS -N %s_%s" % (log_unique_time, analysis_name)
-            out.write(job_title+'\n')
-            out.write(Pbs_model_lines+'\n')
-            out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-            out.write("echo $PBS_O_WORKDIR" + '\n')
-            out.write("cd %s/temp_jobs" % output_folder + '\n')
-            out.write(command_list+'\n')
-        out.close()
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        #call("qsub %s" % job_name, logger)
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-        keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
+        # Removing support for single cluster mode and replacing it with parallel-cluster
+        # keep_logging('Running Job in cluster mode', 'Running Job in cluster mode', logger, 'info')
+        # cluster_job_dir = output_folder + "/temp_jobs/cluster/"
+        # make_sure_path_exists(cluster_job_dir)
+        # Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
+        #               % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
+        # job_name = cluster_job_dir + log_unique_time + "_" + analysis_name + ".pbs"
+        # with open(job_name, 'w') as out:
+        #     job_title = "#PBS -N %s_%s" % (log_unique_time, analysis_name)
+        #     out.write(job_title+'\n')
+        #     out.write(Pbs_model_lines+'\n')
+        #     out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+        #     out.write("echo $PBS_O_WORKDIR" + '\n')
+        #     out.write("cd %s/temp_jobs" % output_folder + '\n')
+        #     out.write(command_list+'\n')
+        # out.close()
+        # keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
+        # #call("qsub %s" % job_name, logger)
+        # qid = subprocess.check_output("qsub %s" % job_name, shell=True)
+        # print qid.split('.')[0]
+        # keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
+
+        keep_logging('Running Jobs in cluster mode', 'Running Jobs in cluster mode', logger, 'info')
+        for job in command_list_qsub:
+            keep_logging('Submitting Job: qsub %s' % job, 'Submitting Job: qsub %s' % job, logger, 'info')
+            call("qsub %s" % job, logger)
+        # keep_logging('You can check the job status with: qstat -u USERNAME',
+        #              'You can check the job status with: qstat -u USERNAME', logger, 'info')
     elif cluster == "parallel-cluster":
         keep_logging('Running Jobs in parallel-cluster mode', 'Running Jobs in parallel-cluster mode', logger, 'info')
         for job in command_list_qsub:
             keep_logging('Submitting Job: qsub %s' % job, 'Submitting Job: qsub %s' % job, logger, 'info')
             call("qsub %s" % job, logger)
-        keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
+        #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
     elif cluster == "parallel-local":
         keep_logging('Running Jobs in parallel-local mode', 'Running Jobs in parallel-local mode', logger, 'info')
         cluster_job_dir = output_folder + "/temp_jobs/parallel-local/"
@@ -330,32 +346,45 @@ def run_core_prep_analysis(core_temp_dir, reference, analysis_name, log_unique_t
                          ConfigSectionMap("scheduler", Config)['queue'],
                          ConfigSectionMap("scheduler", Config)['flux_account'])
 
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_prep_pipeline+'\n')
-    out.close()
+    # with open(job_name, 'w') as out:
+    #     job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
+    #     out.write(job_title+'\n')
+    #     out.write(Pbs_model_lines+'\n')
+    #     out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+    #     out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
+    #     out.write("cd %s" % core_temp_dir + '\n')
+    #     out.write(core_prep_pipeline+'\n')
+    # out.close()
     if cluster == "local":
         keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
         call("bash %s" % job_name, logger)
     elif cluster == "parallel-local":
-        keep_logging('Running parallel-local mode: bash %s' % job_name, 'Running parallel-local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
+        keep_logging('Running in parallel-local mode: bash %s' % job_name, 'Running in parallel-local mode: bash %s' % job_name, logger, 'info')
+        #call("bash %s" % job_name, logger)
+        os.system("bash %s" % job_name)
     elif cluster == "cluster":
         #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
+        keep_logging('Generating core_prep command: \n%s\n' % core_prep_pipeline, 'Generating core_prep command: \n%s\n' % core_prep_pipeline, logger, 'info')
+        #qid = subprocess.check_output("qsub %s" % job_name, shell=True)
+        #print qid.split('.')[0]
     elif cluster == "parallel-cluster":
         #call("qsub %s" % job_name, logger)
+        with open(job_name, 'w') as out:
+            job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
+            out.write(job_title + '\n')
+            out.write(Pbs_model_lines + '\n')
+            out.write(
+                "#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+            out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
+            out.write("cd %s" % core_temp_dir + '\n')
+            out.write(core_prep_pipeline + '\n')
+        out.close()
         keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
         qid = subprocess.check_output("qsub %s" % job_name, shell=True)
         print qid.split('.')[0]
-    keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
+    #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
+
+    return core_prep_pipeline
 
 def run_core_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, core_results_dir, config_file):
     file_exists(reference)
@@ -379,15 +408,15 @@ def run_core_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
                          ConfigSectionMap("scheduler", Config)['queue'],
                          ConfigSectionMap("scheduler", Config)['flux_account'])
     
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_pipeline+'\n')
-    out.close()
+    # with open(job_name, 'w') as out:
+    #     job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
+    #     out.write(job_title+'\n')
+    #     out.write(Pbs_model_lines+'\n')
+    #     out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+    #     out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
+    #     out.write("cd %s" % core_temp_dir + '\n')
+    #     out.write(core_pipeline+'\n')
+    # out.close()
     if cluster == "local":
         keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
         call("bash %s" % job_name, logger)
@@ -395,15 +424,25 @@ def run_core_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
         call("bash %s" % job_name, logger)
     elif cluster == "cluster":
         #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
+        keep_logging('Generating Core Step command: \n%s\n' % core_pipeline, 'Generating Core Step command: \n%s\n' % core_pipeline, logger, 'info')
+        #qid = subprocess.check_output("qsub %s" % job_name, shell=True)
+        #print qid.split('.')[0]
     elif cluster == "parallel-cluster":
         #call("qsub %s" % job_name, logger)
+        with open(job_name, 'w') as out:
+            job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
+            out.write(job_title + '\n')
+            out.write(Pbs_model_lines + '\n')
+            out.write(
+                "#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+            out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
+            out.write("cd %s" % core_temp_dir + '\n')
+            out.write(core_pipeline + '\n')
         keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
         qid = subprocess.check_output("qsub %s" % job_name, shell=True)
         print qid.split('.')[0]
     #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
+    return core_pipeline
 
 def run_report_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, core_results_dir, config_file):
     file_exists(reference)
@@ -414,15 +453,15 @@ def run_report_analysis(core_temp_dir, reference, analysis_name, log_unique_time
     job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
     Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l nodes=1:ppn=4,pmem=4000mb,walltime=92:00:00\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
                       % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_pipeline+'\n')
-    out.close()
+    # with open(job_name, 'w') as out:
+    #     job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
+    #     out.write(job_title+'\n')
+    #     out.write(Pbs_model_lines+'\n')
+    #     out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+    #     out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
+    #     out.write("cd %s" % core_temp_dir + '\n')
+    #     out.write(core_pipeline+'\n')
+    # out.close()
 
     if cluster == "local":
         keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
@@ -431,15 +470,26 @@ def run_report_analysis(core_temp_dir, reference, analysis_name, log_unique_time
         call("bash %s" % job_name, logger)
     elif cluster == "cluster":
         #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("bash %s" % job_name, shell=True)
-        print qid.split('.')[0]
+        keep_logging('Generating Report Step command: \n%s\n' % core_pipeline, 'Generating Report Step command: \n%s\n' % core_pipeline, logger, 'info')
+        #qid = subprocess.check_output("bash %s" % job_name, shell=True)
+        #print qid.split('.')[0]
     elif cluster == "parallel-cluster":
         #call("qsub %s" % job_name, logger)
+        with open(job_name, 'w') as out:
+            job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
+            out.write(job_title + '\n')
+            out.write(Pbs_model_lines + '\n')
+            out.write(
+                "#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+            out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
+            out.write("cd %s" % core_temp_dir + '\n')
+            out.write(core_pipeline + '\n')
+        out.close()
         keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
         qid = subprocess.check_output("qsub %s" % job_name, shell=True)
         print qid.split('.')[0]
     #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
+    return core_pipeline
 
 def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, core_results_dir, config_file):
     if args.debug_mode == "yes":
@@ -457,15 +507,15 @@ def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
     job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
     Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
                       % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core_tree" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_pipeline+'\n')
-    out.close()
+    # with open(job_name, 'w') as out:
+    #     job_title = "#PBS -N %s_%s_core_tree" % (log_unique_time, analysis_name)
+    #     out.write(job_title+'\n')
+    #     out.write(Pbs_model_lines+'\n')
+    #     out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+    #     out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
+    #     out.write("cd %s" % core_temp_dir + '\n')
+    #     out.write(core_pipeline+'\n')
+    # out.close()
 
     if cluster == "local":
         keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
@@ -474,15 +524,26 @@ def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
         call("bash %s" % job_name, logger)
     elif cluster == "cluster":
         #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
+        keep_logging('Generating Tree Step command: \n%s\n' % core_pipeline, 'Generating Tree Step command: \n%s\n' % core_pipeline, logger, 'info')
+        #qid = subprocess.check_output("qsub %s" % job_name, shell=True)
+        #print qid.split('.')[0]
     elif cluster == "parallel-cluster":
         #call("qsub %s" % job_name, logger)
+        with open(job_name, 'w') as out:
+            job_title = "#PBS -N %s_%s_core_tree" % (log_unique_time, analysis_name)
+            out.write(job_title + '\n')
+            out.write(Pbs_model_lines + '\n')
+            out.write(
+                "#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+            out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
+            out.write("cd %s" % core_temp_dir + '\n')
+            out.write(core_pipeline + '\n')
+        out.close()
         keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
         qid = subprocess.check_output("qsub %s" % job_name, shell=True)
         print qid.split('.')[0]
     #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
+    return core_pipeline
 
 """ Start of Main Method/Pipeline """
 if __name__ == '__main__':
@@ -491,31 +552,38 @@ def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
     start_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
     start_time_2 = datetime.now()
 
+    # Pass arguments to args object
     args = parser().parse_args()
 
     global config_file
     global log_unique_time
+    global Config
+    global files_to_delete
+    global logger
+
     if args.output_folder != '':
         args.output_folder += '/'
     make_sure_path_exists(args.output_folder)
+
     log_unique_time = datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
+
     if args.config:
         config_file = args.config
     else:
         config_file = os.path.dirname(os.path.abspath(__file__)) + "/config"
-    global logger
+
+
     logs_folder = args.output_folder + "/Logs"
     make_sure_path_exists(logs_folder)
     # logger = generate_logger(logs_folder, args.analysis_name, log_unique_time)
-    global Config
-    global files_to_delete
+
     files_to_delete = []
     Config = ConfigParser.ConfigParser()
     Config.read(config_file)
 
-
     # Run pipeline steps
-    if "core" not in args.steps and "core_prep" not in args.steps and "report" not in args.steps and "tree" not in args.steps:
+    if "core_All" not in args.steps and "core" not in args.steps and "core_prep" not in args.steps and "report" not in args.steps and "tree" not in args.steps and "2" not in args.steps:
+        """ Set Up variant calling logs folder/logger object, cluster mode and copy config files to it"""
         vc_logs_folder = logs_folder + "/variant_calling"
         make_sure_path_exists(vc_logs_folder)
         logger = generate_logger(vc_logs_folder, args.analysis_name, log_unique_time)
@@ -527,6 +595,7 @@ def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
 
         keep_logging('START: Variant Calling Pipeline', 'START: Variant Calling Pipeline', logger, 'info')
 
+        """ Main Variant calling Methods: Generate and Run the jobs"""
         list_of_files = get_filenames(args.dir, args.type, args.filenames, args.analysis_name, args.suffix)
         list_of_jobs = create_varcall_jobs(list_of_files, args.type, args.output_folder, args.index, args.steps, config_file, logger)
         run_varcall_jobs(list_of_jobs, cluster_mode, log_unique_time, args.analysis_name, args.output_folder, logger)
@@ -535,34 +604,300 @@ def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
         keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
         keep_logging('End: Variant calling Pipeline', 'End: Variant calling Pipeline', logger, 'info')
 
+    elif "core_All" in args.steps or "2" in args.steps:
+        core_All_cmds = []
+        """ Set Up Core Prep logs folder/logger object, cluster mode and copy config files to it"""
+        core_prep_logs_folder = logs_folder + "/core_prep"
+        make_sure_path_exists(core_prep_logs_folder)
+        core_temp_dir = args.output_folder + "/core_temp_dir/"
+        logger = generate_logger(core_prep_logs_folder, args.analysis_name, log_unique_time)
+        #keep_logging('START: Copying variant calling results to %s and running core_prep step.' % core_temp_dir, 'START: Copying variant calling results to %s and running core_prep step.' % core_temp_dir, logger, 'info')
+        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, core_prep_logs_folder, log_unique_time, args.analysis_name), logger)
+        make_sure_path_exists(core_temp_dir)
+        keep_logging('\nCopying vcf files to %s\n' % core_temp_dir, '\nCopying vcf files to %s\n' % core_temp_dir, logger, 'info')
+        cp_command = "cp %s/*/*_vcf_results/*_filter2_indel_final.vcf %s/*/*_vcf_results/*_aln_mpileup_raw.vcf %s/*/*_vcf_results/*_raw.vcf_5bp_indel_removed.vcf* %s/*/*_vcf_results/*filter2_final.vcf* %s/*/*_vcf_results/*vcf_no_proximate_snp.vcf* %s/*/*_vcf_results/*array %s/*/*unmapped.bed_positions %s/*/*_vcf_results/*_indel_gatk.vcf %s" % (args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, core_temp_dir)
+        # cp_command = "cp %s/*/*_filter2_indel_final.vcf %s/*/*_aln_mpileup_raw.vcf %s/*/*_raw.vcf_5bp_indel_removed.vcf* %s/*/*filter2_final.vcf* %s/*/*vcf_no_proximate_snp.vcf* %s/*/*array %s/*/*unmapped.bed_positions %s" % (
+        # args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder,
+        # args.output_folder, args.output_folder, core_temp_dir)
+        call(cp_command, logger)
+
+        """ Decompress zipped files in core temp folder"""
+        list_of_gzipped_files = glob.glob("%s/*.gz" % core_temp_dir)
+        keep_logging('Decompressing gzipped files in %s\n' % core_temp_dir, 'Decompressing gzipped files in %s\n' % core_temp_dir, logger, 'info')
+        gzipped_command_list = []
+        for i in list_of_gzipped_files:
+            gzipped_command_list.append("gzip -df %s" % i)
+
+        """ Set the number of cores variable for Parallel-local mode """
+        num_cores = multiprocessing.cpu_count()
+        results = Parallel(n_jobs=num_cores)(delayed(run_command_list)(i) for i in gzipped_command_list)
+
+        """ Generate a custom vcf file list to process for core prep step. If file is not provided, it will consider all the samples in output folder"""
+        """ Perform Sanity check to confirm that variant calling results are present in core temp folder """
+        if args.filenames:
+            list_of_files = get_filenames(args.dir, args.type, args.filenames, args.analysis_name, args.suffix)
+            list_of_vcf_files = generate_custom_vcf_file_list(sorted(list_of_files), logger)
+            keep_logging('\nNumber of final variant call vcf files: %s\n' % len(list_of_vcf_files), '\nNumber of final variant call vcf files: %s\n' % len(list_of_vcf_files), logger, 'info')
+            #keep_logging('Make sure the number of final variant call vcf files looks correct', 'Make sure the number of final variant call vcf files looks correct', logger, 'info')
+            #keep_logging('Running core_prep on these files...', 'Running core_prep on these files...', logger, 'info')
+            with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
+                for file in list_of_vcf_files:
+                    out_fp.write(os.path.basename(file)+'\n')
+            out_fp.close()
+        else:
+            keep_logging('\nChecking if all the variant calling results exists in %s\n' % core_temp_dir, '\nChecking if all the variant calling results exists in %s\n' % core_temp_dir, logger, 'info')
+            #call("ls -1a %s/*.vcf_no_proximate_snp.vcf > %s/vcf_filenames" % (core_temp_dir, core_temp_dir), logger)
+            try:
+                list_cmd = "ls -1a %s/*.vcf_no_proximate_snp.vcf" % core_temp_dir
+                list_of_files = subprocess.check_output(list_cmd, shell=True)
+
+                keep_logging('Number of final variant call vcf files: %s\n' % len(list_of_files.splitlines()), 'Number of final variant call vcf files: %s\n' % len(list_of_files.splitlines()), logger, 'info')
+                #keep_logging('Make sure the number of final variant call vcf files looks correct', 'Make sure the number of final variant call vcf files looks correct', logger, 'info')
+                #keep_logging('Running core_prep on these files...', 'Running core_prep on these files...', logger, 'info')
+                with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
+                    for file in list_of_files.splitlines():
+                        out_fp.write(os.path.basename(file)+'\n')
+                out_fp.close()
+            except:
+                keep_logging('Error: The variant calling results were not found in %s. Please check if variant calling step finished properly without any errors. '
+                             'This can be done by checking if all the variant call results folder contains final variant call vcf file: *.vcf_no_proximate_snp.vcf file' % core_temp_dir, 'Error: The variant calling results were not found in %s. Please check if variant calling step finished properly without any errors. '
+                                                                                                                                                                                              'This can be done by checking if all the variant call results folder contains final variant call vcf file: *.vcf_no_proximate_snp.vcf file' % core_temp_dir, logger, 'exception')
+                exit()
+
+
+
+            # list_cmd = "ls -1a %s/*.vcf_no_proximate_snp.vcf" % core_temp_dir
+            # list_of_files = subprocess.check_output(list_cmd, shell=True)
+            # print list_of_files
+            # with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
+            #     for file in list_of_files.splitlines():
+            #         out_fp.write(os.path.basename(file)+'\n')
+            # out_fp.close()
+        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
+        core_prep_pipeline_cmd = run_core_prep_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, config_file)
+        core_All_cmds.append(core_prep_pipeline_cmd)
+        time_taken = datetime.now() - start_time_2
+        keep_logging('Core Prep Logs will be recorded in file with extension log.txt in %s\n' % core_prep_logs_folder, 'Core Prep Logs will be recorded in file with extension log.txt in %s\n' % core_prep_logs_folder, logger, 'info')
+        #keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
+
+        """ Generate Core Variants from core_prep intermediate files """
+        core_logs_folder = logs_folder + "/core"
+        make_sure_path_exists(core_logs_folder)
+        logger = generate_logger(core_logs_folder, args.analysis_name, log_unique_time)
+        #keep_logging('START: Extract core snps and generate diagnostic plots','START: Extract core snps and generate diagnostic plots', logger, 'info')
+        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, core_logs_folder, log_unique_time, args.analysis_name),
+             logger)
+        core_temp_dir = args.output_folder + "/core_temp_dir/"
+        core_results_dir = args.output_folder + "/%s_core_results/" % log_unique_time
+        make_sure_path_exists(core_results_dir)
+
+        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)[
+            'ref_name']
+
+        """ If Phaster Summary file doesn't exist in reference genome folder """
+        if not os.path.isfile("%s/summary.txt" % os.path.dirname(reference)):
+            if ConfigSectionMap("functional_filters", Config)['apply_functional_filters'] == "yes":
+                keep_logging('Preparing Functional class filters\n', 'Preparing Functional class filters\n', logger,
+                             'info')
+                if ConfigSectionMap("functional_filters", Config)['find_phage_region'] == "yes":
+                    # Submit Phaster jobs to find ProPhage region in reference genome.
+                    run_phaster(reference, os.path.dirname(reference), logger, Config)
+
+
+
+        # Parse Phaster results file to extract phage region.
+        if ConfigSectionMap("functional_filters", Config)['apply_functional_filters'] == "yes":
+            keep_logging('Parsing Functional class filters\n', 'Parsing Functional class filters\n', logger,
+                         'info')
+            functional_class_filter_positions = "%s/Functional_class_filter_positions.txt" % core_temp_dir
+            f1 = open(functional_class_filter_positions, 'w+')
+            if ConfigSectionMap("functional_filters", Config)['find_phage_region'] == "yes":
+                phage_region_positions = parse_phaster(reference, core_temp_dir, logger, Config)
+                with open(phage_region_positions, 'rU') as fp:
+                    for line in fp:
+                        f1.write(line)
+                fp.close()
+            if ConfigSectionMap("functional_filters", Config)['find_repetitive_region'] == "yes":
+                # Find repeat regions in reference genome
+                repeat_region_positions = nucmer_repeat(reference, core_temp_dir, logger, Config)
+                with open(repeat_region_positions, 'rU') as fp:
+                    for line in fp:
+                        f1.write(line)
+                fp.close()
+            if ConfigSectionMap("functional_filters", Config)['mask_region'] == "yes":
+                # Mask custom region/Positions
+                if ConfigSectionMap("functional_filters", Config)['mask_file']:
+                    mask_file = ConfigSectionMap("functional_filters", Config)['mask_file']
+                    mask_extension = os.path.splitext(mask_file)[1]
+                    if mask_extension == ".bed":
+                        mask_positions_file = mask_regions(mask_file, core_temp_dir, logger, Config)
+                        keep_logging(
+                            'Mask positions in this file %s will be filtered out' % mask_positions_file,
+                            'Mask positions in this file %s will be filtered out' % mask_positions_file,
+                            logger, 'info')
+                    else:
+                        # mask_positions_file = mask_file
+                        os.system("cp %s %s/mask_positions.txt" % (mask_file, core_temp_dir))
+                        mask_positions_file = "%s/mask_positions.txt" % core_temp_dir
+                        keep_logging(
+                            'Mask positions in this file %s will be filtered out' % mask_positions_file,
+                            'Mask positions in this file %s will be filtered out' % mask_positions_file,
+                            logger, 'info')
+                    with open(mask_positions_file, 'rU') as fp:
+                        for line in fp:
+                            f1.write(line)
+                    fp.close()
+            f1.close()
+
+        copy_phage_results = "cp %s/summary.txt %s/phage_region_positions.txt %s/detail.txt" % (os.path.dirname(reference), os.path.dirname(reference), os.path.dirname(reference))
+        call(copy_phage_results, logger)
+
+        core_pipeline_cmd = run_core_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger,
+                          core_results_dir, config_file)
+        core_All_cmds.append(core_pipeline_cmd)
+        time_taken = datetime.now() - start_time_2
+        keep_logging('Core Step Logs will be recorded in file with extension log.txt in %s\n' % core_logs_folder,
+                     'Core Step Logs will be recorded in file with extension log.txt in %s\n' % core_logs_folder, logger, 'info')
+        #keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger,'info')
+
+        """ Generate Reports and organize core results folder """
+        report_logs_folder = logs_folder + "/report"
+        make_sure_path_exists(report_logs_folder)
+        logger = generate_logger(report_logs_folder, args.analysis_name, log_unique_time)
+        #keep_logging('START: Generating alignment and variant calling report','START: Generating alignment and variant calling report', logger, 'info')
+        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, report_logs_folder, log_unique_time, args.analysis_name),
+             logger)
+        core_temp_dir = args.output_folder + "/core_temp_dir/"
+        # core_results_dir = args.output_folder + "/%s_core_results/" % log_unique_time
+        # make_sure_path_exists(core_results_dir)
+        proc = subprocess.Popen(["ls -1ad %s/*_core_results | tail -n1" % args.output_folder], stdout=subprocess.PIPE,
+                                shell=True)
+        (out2, err2) = proc.communicate()
+        core_results_dir = out2.strip()
+        #keep_logging('Moving final results and reports to %s' % core_results_dir,'Moving final results and reports to %s' % core_results_dir, logger, 'info')
+
+        list_of_label_files = glob.glob("%s/*_label" % core_temp_dir)
+        list_of_vcf_files = []
+        with open("%s/vcf_filenames" % core_temp_dir, 'r') as out_fp:
+            for line in out_fp:
+                list_of_vcf_files.append(line)
+        for i in list_of_label_files:
+            if os.stat(i).st_size == 0:
+                keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i),
+                             'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger,
+                             'exception')
+                exit()
+        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)[
+            'ref_name']
+        run_report_analysis_cmd = run_report_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger,
+                            core_results_dir, config_file)
+        time_taken = datetime.now() - start_time_2
+        keep_logging('Report Step Logs will be recorded in file with extension log.txt in %s\n' % report_logs_folder,
+                     'Report Step Logs will be recorded in file with extension log.txt in %s\n' % report_logs_folder, logger, 'info')
+        #keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger,'info')
+
+        core_All_cmds.append(run_report_analysis_cmd)
+
+        """ Generate Trees """
+        tree_logs_folder = logs_folder + "/tree"
+        make_sure_path_exists(tree_logs_folder)
+        logger = generate_logger(tree_logs_folder, args.analysis_name, log_unique_time)
+        #keep_logging('START: Generating FastTree and RAxML trees', 'START: Generating FastTree and RAxML trees', logger,'info')
+        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, tree_logs_folder, log_unique_time, args.analysis_name),
+             logger)
+        core_temp_dir = args.output_folder + "/core_temp_dir/"
+        # core_results_dir = args.output_folder + "/%s_core_results/" % log_unique_time
+        proc = subprocess.Popen(["ls -1ad %s/*_core_results | tail -n1" % args.output_folder], stdout=subprocess.PIPE,
+                                shell=True)
+        (out2, err2) = proc.communicate()
+        core_results_dir = out2.strip()
+        #keep_logging('Generating RaxML and fasttree trees in %s/trees' % core_results_dir,'Generating RaxML and fasttree trees in %s/trees' % core_results_dir, logger, 'info')
+        print core_results_dir
+        list_of_label_files = glob.glob("%s/*_label" % core_temp_dir)
+        list_of_vcf_files = []
+        with open("%s/vcf_filenames" % core_temp_dir, 'r') as out_fp:
+            for line in out_fp:
+                list_of_vcf_files.append(line)
+        for i in list_of_label_files:
+            if os.stat(i).st_size == 0:
+                keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i),
+                             'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger,
+                             'exception')
+                exit()
+        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)[
+            'ref_name']
+        run_tree_analysis_cmd = run_tree_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time,
+                                                  args.cluster, logger, core_results_dir, config_file)
+        time_taken = datetime.now() - start_time_2
+        keep_logging('Tree Step Logs will be recorded in file with extension log.txt in %s\n' % tree_logs_folder,
+                     'Tree Step Logs will be recorded in file with extension log.txt in %s\n' % tree_logs_folder, logger, 'info')
+        #keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger,'info')
+
+        core_All_cmds.append(run_tree_analysis_cmd)
+
+        combine_job_name = core_temp_dir + "/" + log_unique_time + "_" + args.analysis_name + "_core_All.pbs"
+
+        Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n" \
+                          % (ConfigSectionMap("scheduler", Config)['email'],
+                             ConfigSectionMap("scheduler", Config)['notification'],
+                             ConfigSectionMap("scheduler", Config)['large_resources'],
+                             ConfigSectionMap("scheduler", Config)['queue'],
+                             ConfigSectionMap("scheduler", Config)['flux_account'])
+
+        with open(combine_job_name, 'w') as out:
+            job_title = "#PBS -N %s_%s_core_All" % (log_unique_time, args.analysis_name)
+            out.write(job_title + '\n')
+            out.write(Pbs_model_lines + '\n')
+            out.write(
+                "#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
+            out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
+            out.write("cd %s" % core_temp_dir + '\n')
+            for cmds in core_All_cmds:
+                out.write(cmds + '\n')
+        out.close()
+
+        keep_logging('Running: %s\n' % combine_job_name,
+                     'Running: %s\n' % combine_job_name,
+                     logger, 'info')
+
+        call("qsub %s" % combine_job_name, logger)
+
+
     elif "core_prep" in args.steps:
+        """ Set Up Core Prep logs folder/logger object, cluster mode and copy config files to it"""
         core_prep_logs_folder = logs_folder + "/core_prep"
         make_sure_path_exists(core_prep_logs_folder)
         core_temp_dir = args.output_folder + "/core_temp_dir/"
         logger = generate_logger(core_prep_logs_folder, args.analysis_name, log_unique_time)
         keep_logging('START: Copying variant calling results to %s and running core_prep step.' % core_temp_dir, 'START: Copying variant calling results to %s and running core_prep step.' % core_temp_dir, logger, 'info')
         call("cp %s %s/%s_%s_config_copy.txt" % (config_file, core_prep_logs_folder, log_unique_time, args.analysis_name), logger)
-
         make_sure_path_exists(core_temp_dir)
         keep_logging('Copying vcf files to %s' % core_temp_dir, 'Copying vcf files to %s' % core_temp_dir, logger, 'info')
-        cp_command = "cp %s/*/*_filter2_indel_final.vcf %s/*/*_aln_mpileup_raw.vcf %s/*/*_raw.vcf_5bp_indel_removed.vcf* %s/*/*filter2_final.vcf.gz %s/*/*vcf_no_proximate_snp.vcf* %s/*/*array %s/*/*unmapped.bed_positions %s" % (args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, core_temp_dir)
+        cp_command = "cp %s/*/*_vcf_results/*_filter2_indel_final.vcf %s/*/*_vcf_results/*_aln_mpileup_raw.vcf %s/*/*_vcf_results/*_raw.vcf_5bp_indel_removed.vcf* %s/*/*_vcf_results/*filter2_final.vcf* %s/*/*_vcf_results/*vcf_no_proximate_snp.vcf* %s/*/*_vcf_results/*array %s/*/*unmapped.bed_positions %s" % (args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, core_temp_dir)
+        # cp_command = "cp %s/*/*_filter2_indel_final.vcf %s/*/*_aln_mpileup_raw.vcf %s/*/*_raw.vcf_5bp_indel_removed.vcf* %s/*/*filter2_final.vcf* %s/*/*vcf_no_proximate_snp.vcf* %s/*/*array %s/*/*unmapped.bed_positions %s" % (
+        # args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder,
+        # args.output_folder, args.output_folder, core_temp_dir)
         call(cp_command, logger)
+
+        """ Decompress zipped files in core temp folder"""
         list_of_gzipped_files = glob.glob("%s/*.gz" % core_temp_dir)
         keep_logging('Decompressing gzipped files in %s' % core_temp_dir, 'Decompressing gzipped files in %s' % core_temp_dir, logger, 'info')
         gzipped_command_list = []
         for i in list_of_gzipped_files:
             gzipped_command_list.append("gzip -df %s" % i)
+
+        """ Set the number of cores variable for Parallel-local mode """
         num_cores = multiprocessing.cpu_count()
         results = Parallel(n_jobs=num_cores)(delayed(run_command_list)(i) for i in gzipped_command_list)
+
+        """ Generate a custom vcf file list to process for core prep step. If file is not provided, it will consider all the samples in output folder"""
+        """ Perform Sanity check to confirm that variant calling results are present in core temp folder """
         if args.filenames:
             list_of_files = get_filenames(args.dir, args.type, args.filenames, args.analysis_name, args.suffix)
             list_of_vcf_files = generate_custom_vcf_file_list(sorted(list_of_files), logger)
-
-
             keep_logging('Number of final variant call vcf files: %s' % len(list_of_vcf_files), 'Number of final variant call vcf files: %s' % len(list_of_vcf_files), logger, 'info')
             keep_logging('Make sure the number of final variant call vcf files looks correct', 'Make sure the number of final variant call vcf files looks correct', logger, 'info')
             keep_logging('Running core_prep on these files...', 'Running core_prep on these files...', logger, 'info')
-
             with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
                 for file in list_of_vcf_files:
                     out_fp.write(os.path.basename(file)+'\n')
@@ -597,12 +932,13 @@ def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
             #         out_fp.write(os.path.basename(file)+'\n')
             # out_fp.close()
         reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_core_prep_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, config_file)
+        core_prep_pipeline_cmd = run_core_prep_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, config_file)
         time_taken = datetime.now() - start_time_2
         keep_logging('Logs were recorded in file with extension log.txt in %s' % core_prep_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % core_prep_logs_folder, logger, 'info')
         keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
 
     elif "core" in args.steps:
+        """ Generate Core Variants from core_prep intermediate files """
         core_logs_folder = logs_folder + "/core"
         make_sure_path_exists(core_logs_folder)
         logger = generate_logger(core_logs_folder, args.analysis_name, log_unique_time)
@@ -693,9 +1029,7 @@ def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
                     fp.close()
             f1.close()
 
-
-
-        run_core_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
+        core_pipeline_cmd = run_core_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
         time_taken = datetime.now() - start_time_2
         keep_logging('Logs were recorded in file with extension log.txt in %s' % core_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % core_logs_folder, logger, 'info')
         keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
@@ -726,7 +1060,7 @@ def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
                 keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
                 exit()
         reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_report_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
+        run_report_analysis_cmd = run_report_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
         time_taken = datetime.now() - start_time_2
         keep_logging('Logs were recorded in file with extension log.txt in %s' % report_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % report_logs_folder, logger, 'info')
         keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
@@ -756,7 +1090,7 @@ def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time,
                 keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
                 exit()
         reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_tree_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
+        run_tree_analysis_cmd = run_tree_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
         time_taken = datetime.now() - start_time_2
         keep_logging('Logs were recorded in file with extension log.txt in %s' % tree_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % tree_logs_folder, logger, 'info')
         keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
diff --git a/variant_call_backup.py b/variant_call_backup.py
deleted file mode 100644
index 775e9ae..0000000
--- a/variant_call_backup.py
+++ /dev/null
@@ -1,643 +0,0 @@
-__author__ = 'alipirani'
-
-import sys
-import os
-import argparse
-import errno
-import ConfigParser
-import glob
-import re
-import multiprocessing
-import subprocess as subprocess
-from datetime import datetime
-from joblib import Parallel, delayed
-from config_settings import ConfigSectionMap
-from modules.logging_subprocess import *
-from modules.log_modules import *
-from argparse import RawTextHelpFormatter
-
-""" Command Line Argument Parsing """
-def parser():
-    parser = argparse.ArgumentParser(description='\nVariant Calling pipeline for Illumina PE/SE data.\n', formatter_class=RawTextHelpFormatter)
-    required = parser.add_argument_group('Required arguments')
-    optional = parser.add_argument_group('Optional arguments')
-    required.add_argument('-type', action='store', dest="type", help='Type of reads: SE or PE', required=True)
-    required.add_argument('-readsdir', action='store', dest="dir", help='Path to Sequencing Reads Data directory. NOTE: Provide full/absolute path.', required=True)
-    required.add_argument('-outdir', action='store', dest="output_folder", help='Output Folder Path ending with output directory name to save the results. Creates a new output directory path if it doesn\'t exist. NOTE: Provide full/absolute path.', required=True)
-    required.add_argument('-index', action='store', dest="index", help='Reference Index Name. Most Frequently used reference genomes index options: KPNIH1 | MRSA_USA_300 | MRSA_USA_100 | CDIFF_630 | paris Make sure the paths are properly set in config file', required=True)
-    required.add_argument('-steps', action='store', dest="steps", help='Variant Calling Steps in sequential order.\n'
-                                                                     '1.   All: This will run all the steps starting from cleaning the reads to variant calling;\n'
-                                                                     '2.   clean,align,post-align,varcall,filter,stats : This will also run all steps starting from cleaning to variant calling. \nYou can also run part of the pipeline by giving "align,post-align,varcall,filter,stats" which will skip the cleaning part.\nThe order is required to be sequential while using this option. Also, while skipping any of the step make sure you have results already present in your output folder.\n'
-                                                                     '3.   coverage_depth_stats: Run Only Depth of Coverage Stats module after cleaning and read mapping steps\n'
-                                                                     '4.   core_prep: Run this step before running the core steps. This will prepare the data required for generating core SNPs\n'
-                                                                     '5.   core: extract core snps and generate diagnostics plot data matrices to explore filtered snps.')
-    required.add_argument('-analysis', action='store', dest="analysis_name", help='Unique analysis name that will be used as prefix to saving results and log files.', required=True)
-    optional.add_argument('-config', action='store', dest="config", help='Path to Config file, Make sure to check config settings before running pipeline', required=False)
-    optional.add_argument('-suffix', action='store', dest="suffix", help='Fastq reads suffix such as fastq, fastq.gz, fq.gz, fq; Default: fastq.gz', required=False)
-    optional.add_argument('-filenames', action='store', dest="filenames", help='fastq filenames with one single-end filename per line. \nIf the type is set to PE, it will detect the second paired-end filename with the suffix from first filename. \nUseful for running variant calling pipeline on selected files in a reads directory or extracting core snps for selected samples in input reads directory. \nOtherwise the pipeline will consider all the samples available in reads directory.', required=False)
-    optional.add_argument('-cluster', action='store', dest='cluster', help='Run variant calling pipeline in one of the four modes. Default: local. Suggested mode for core snp is cluster that will run all the steps in parallel with the available cores. Make sure to provide a large memory node for this option\nThe possible modes are: cluster/parallel-cluster/parallel-local/local\ncluster: Runs all the jobs on a single large cluster. This will mimic the local run but rather on a large compute node.\nparallel-cluster: Submit variant call jobs for each sample in parallel on compute nodes. This mode is no available for core snp extraction step.\nparallel-local: Run variant call jobs for each sample in parallel locally.\nlocal: Run variant call jobs locally.\nMake Sure to check if the [scheduler] section in config file is set up correctly for your cluster.')
-    optional.add_argument('-clean', action='store', dest="clean", help='clean up intermediate files. Default: OFF')
-    optional.add_argument('-extract_unmapped', action='store', dest="extract_unmapped", help='Extract unmapped reads, assemble it and detect AMR genes using ariba')
-    optional.add_argument('-datadir', action='store', dest="datadir", help='Path to snpEff data directory')
-    optional.add_argument('-snpeff_db', action='store', dest="snpeff_db", help='Name of pre-build snpEff database to use for Annotation')
-    optional.add_argument('-debug_mode', action='store', dest="debug_mode", help='yes/no for debug mode')
-    return parser
-
-""" Sanity checks and directory structure maintenance methods """
-def file_exists(path1):
-    if not os.path.isfile(path1):
-        file_basename = os.path.basename(path1)
-        keep_logging('The file {} does not exists. Please provide another file with full path or check the files path.\n'.format(file_basename), 'The input file {} does not exists. Please provide another file or check the files path.\n'.format(file_basename), logger, 'exception')
-        exit()
-
-def make_sure_path_exists(out_path):
-    try:
-        os.makedirs(out_path)
-    except OSError as exception:
-        if exception.errno != errno.EEXIST:
-            keep_logging('Errors in output folder path! please change the output path or analysis name.', 'Errors in output folder path! please change the output path or analysis name', logger, 'exception')
-            exit()
-
-def get_filenames(dir, type, filenames, analysis, suffix):
-    if not filenames:
-        if not suffix:
-            suffix = ".fastq.gz"
-        try:
-            list_of_files = glob.glob("%s/*%s" % (dir, suffix))
-            if len(list_of_files) < 1:
-                print "No fastq files with suffix %s found in reads directory %s" % (suffix, dir)
-        except OSError as exception:
-            if exception.errno != errno.EEXIST:
-                keep_logging('Error while listing files in reads directory.', 'Error while listing files in reads directory.', logger, 'exception')
-                exit()
-    else:
-        list_of_files = []
-        with open(filenames) as fp:
-            for line in fp:
-                line = line.strip()
-                line = dir + "/" + line
-                list_of_files.append(line)
-    return list_of_files
-
-""" Methods to generate jobs for various pipeline tasks """
-def create_varcall_jobs(filenames_array, type, output_folder, reference, steps, config_file, logger):
-    jobs_temp_dir = "%s/temp_jobs" % output_folder
-    make_sure_path_exists(jobs_temp_dir)
-    keep_logging('Generating cluster jobs in temporary directory %s' % jobs_temp_dir, 'Generating cluster jobs in temporary directory %s' % jobs_temp_dir, logger, 'exception')
-    ##Change the email address; nodes and processor requirements accordingly
-
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-
-    for file in filenames_array:
-        filename_base = os.path.basename(file)
-        if "R1_001_final.fastq.gz" in filename_base or "R1.fastq.gz" in filename_base or "1_combine.fastq.gz" in filename_base or "1_sequence.fastq.gz" in filename_base or "_forward.fastq.gz" in filename_base or "R1_001.fastq.gz" in filename_base or "_1.fastq.gz" in filename_base or ".1.fastq.gz" in filename_base or "_R1.fastq.gz" in filename_base:
-            # Forward reads file name and get analysis name from its name
-            first_file = file
-            # Get the name of reverse reads files
-            if "R1_001_final.fastq.gz" in filename_base:
-                second_part = filename_base.replace("R1_001_final.fastq.gz", "R2_001_final.fastq.gz")
-                first_part_split = filename_base.split('R1_001_final.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "_R1.fastq.gz" in filename_base:
-                second_part = filename_base.replace("_R1.fastq.gz", "_R2.fastq.gz")
-                first_part_split = filename_base.split('_R1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "R1.fastq.gz" in filename_base:
-                second_part = filename_base.replace("R1.fastq.gz", "R2.fastq.gz")
-                first_part_split = filename_base.split('R1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "1_combine.fastq.gz" in filename_base:
-                second_part = filename_base.replace("1_combine.fastq.gz", "2_combine.fastq.gz")
-                first_part_split = filename_base.split('1_combine.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "1_sequence.fastq.gz" in filename_base:
-                second_part = filename_base.replace("1_sequence.fastq.gz", "2_sequence.fastq.gz")
-                first_part_split = filename_base.split('1_sequence.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "_forward.fastq.gz" in filename_base:
-                second_part = filename_base.replace("_forward.fastq.gz", "_reverse.fastq.gz")
-                first_part_split = filename_base.split('_forward.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "R1_001.fastq.gz" in filename_base:
-                second_part = filename_base.replace("R1_001.fastq.gz", "R2_001.fastq.gz")
-                first_part_split = filename_base.split('R1_001.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "_1.fastq.gz" in filename_base:
-                second_part = filename_base.replace("_1.fastq.gz", "_2.fastq.gz")
-                first_part_split = filename_base.split('_1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif ".1.fastq.gz" in filename_base:
-                second_part = filename_base.replace(".1.fastq.gz", ".2.fastq.gz")
-                first_part_split = filename_base.split('.1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-
-            """ Have a standard filename preparation step"""
-            # else:
-            #     print "Using Standard second file naming convention"
-            #     second_part = filename_base.replace("_R1_", "_R2_")
-            #     first_part_split = filename_base.split('_R1.fastq.gz')
-            #     first_part = first_part_split[0].replace('_L001', '')
-            #     first_part = re.sub("_S.*_", "", first_part)
-            second_file = args.dir + "/" + second_part
-            job_name = jobs_temp_dir + "/" + first_part + ".pbs"
-            if not steps:
-                steps == "All"
-            if type == "SE":
-                command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -o %s/%s -analysis %s -index %s -type SE -config %s -steps %s" % (os.path.dirname(os.path.abspath(__file__)), first_file, output_folder, first_part, first_part, reference, config_file, steps)
-            else:
-                command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -PE2 %s -o %s/%s -analysis %s -index %s -type PE -config %s -steps %s" % (os.path.dirname(os.path.abspath(__file__)), first_file, second_file, output_folder, first_part, first_part, reference, config_file, steps)
-
-            with open(job_name, 'w') as out:
-                job_title = "#PBS -N %s" % first_part
-                out.write(job_title+'\n')
-                out.write(Pbs_model_lines+'\n')
-                #out.write(cd_command+'\n') ## changed it to automatically change to PBS working directory
-                out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-                out.write("echo $PBS_O_WORKDIR" + '\n')
-                out.write("cd %s/temp_jobs" % output_folder + '\n')
-                out.write(command+'\n')
-        elif "R2_001_final.fastq.gz" in filename_base or "R2.fastq.gz" in filename_base or "2_combine.fastq.gz" in filename_base or "2_sequence.fastq.gz" in filename_base or "_reverse.fastq.gz" in filename_base or "R2_001.fastq.gz" in filename_base or "_2.fastq.gz" in filename_base or ".2.fastq.gz" in filename_base or "_R2.fastq.gz" in filename_base:
-            continue
-        else:
-            keep_logging('Error while generating cluster jobs. Make sure the fastq filenames ends with one of these suffix: R1_001_final.fastq.gz, R1.fastq.gz, 1_combine.fastq.gz, 1_sequence.fastq.gz, _forward.fastq.gz, R1_001.fastq.gz, _1.fastq.gz, .1.fastq.gz, _R1.fastq.gz', 'Error while generating cluster jobs. Make sure the fastq filenames ends with one of these suffix: R1_001_final.fastq.gz, R1.fastq.gz, 1_combine.fastq.gz, 1_sequence.fastq.gz, _forward.fastq.gz, R1_001.fastq.gz, _1.fastq.gz, .1.fastq.gz, _R1.fastq.gz', logger, 'exception')
-            exit()
-    list_of_jobs = glob.glob("%s/*.pbs" % jobs_temp_dir)
-    return list_of_jobs
-
-def generate_custom_vcf_file_list(filenames_array, logger):
-    keep_logging('Generating custom vcf file list for core pipeline steps', 'Generating custom vcf files list for core pipeline steps', logger, 'exception')
-    list_of_vcf_files = []
-    for file in filenames_array:
-        filename_base = os.path.basename(file)
-        if "R1_001_final.fastq.gz" in filename_base or "R1.fastq.gz" in filename_base or "1_combine.fastq.gz" in filename_base or "1_sequence.fastq.gz" in filename_base or "_forward.fastq.gz" in filename_base or "R1_001.fastq.gz" in filename_base or "_1.fastq.gz" in filename_base or ".1.fastq.gz" in filename_base or "_R1.fastq.gz" in filename_base:
-            # Forward reads file name and get analysis name from its name
-            first_file = file
-            if "R1_001_final.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('R1_001_final.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "_R1.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('_R1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "R1.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('R1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "1_combine.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('1_combine.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "1_sequence.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('1_sequence.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "_forward.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('_forward.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "R1_001.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('R1_001.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "_1.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('_1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif ".1.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('.1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            list_of_vcf_files.append(first_part)
-    return list_of_vcf_files
-
-def run_command(job):
-    keep_logging('Running Job: bash %s' % job, 'Running Job: bash %s' % job, logger, 'info')
-    call("bash %s" % job, logger)
-    done = "Job Run completed: %s" % job
-    return done
-
-def run_command_list(command):
-    keep_logging('Running command: %s' % command, 'Running command: %s' % command, logger, 'info')
-    call("%s" % command, logger)
-    done = "Command Run completed: %s" % command
-    return done
-
-def run_varcall_jobs(list_of_jobs, cluster, log_unique_time, analysis_name, output_folder, logger):
-    #Generate command list to run on single cluster or in parallel
-    command_list = ""
-    command_list_qsub = []
-    for job in list_of_jobs:
-        command_list = command_list + "bash %s\n" % job
-        command_list_qsub.append(job)
-
-    #cluster mode
-    if cluster == "cluster":
-        keep_logging('Running Job in cluster mode', 'Running Job in cluster mode', logger, 'info')
-        cluster_job_dir = output_folder + "/temp_jobs/cluster/"
-        make_sure_path_exists(cluster_job_dir)
-        Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-        job_name = cluster_job_dir + log_unique_time + "_" + analysis_name + ".pbs"
-        with open(job_name, 'w') as out:
-            job_title = "#PBS -N %s_%s" % (log_unique_time, analysis_name)
-            out.write(job_title+'\n')
-            out.write(Pbs_model_lines+'\n')
-            out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-            out.write("echo $PBS_O_WORKDIR" + '\n')
-            out.write("cd %s/temp_jobs" % output_folder + '\n')
-            out.write(command_list+'\n')
-        out.close()
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        #call("qsub %s" % job_name, logger)
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-        keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-    elif cluster == "parallel-cluster":
-        keep_logging('Running Jobs in parallel-cluster mode', 'Running Jobs in parallel-cluster mode', logger, 'info')
-        for job in command_list_qsub:
-            keep_logging('Submitting Job: qsub %s' % job, 'Submitting Job: qsub %s' % job, logger, 'info')
-            call("qsub %s" % job, logger)
-        keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-    elif cluster == "parallel-local":
-        keep_logging('Running Jobs in parallel-local mode', 'Running Jobs in parallel-local mode', logger, 'info')
-        cluster_job_dir = output_folder + "/temp_jobs/parallel-local/"
-        make_sure_path_exists(cluster_job_dir)
-        job_name = cluster_job_dir + log_unique_time + "_" + analysis_name + ".sh"
-        with open(job_name, 'w') as out:
-            out.write(command_list+'\n')
-        num_cores = multiprocessing.cpu_count()
-        keep_logging('Number of cores available: %s' % num_cores, 'Number of cores available: %s' % num_cores, logger, 'info')
-        results = Parallel(n_jobs=num_cores)(delayed(run_command)(i) for i in command_list_qsub)
-    elif cluster == "local":
-        keep_logging('Running Jobs in local mode', 'Running Jobs in local mode', logger, 'info')
-        for job in command_list_qsub:
-            keep_logging('Running Job: bash %s' % job, 'Running Job: bash %s' % job, logger, 'info')
-            call("bash %s" % job, logger)
-
-""" Pipeline individual task methods """
-def run_core_prep_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, config_file):
-    file_exists(reference)
-    if args.debug_mode == "yes":
-        core_prep_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline_debug.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 1 -jobrun %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, config_file)
-    else:
-        core_prep_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 1 -jobrun %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, config_file)
-
-    job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
-
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l nodes=1:ppn=4,mem=47000mb,walltime=92:00:00\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_prep_pipeline+'\n')
-    out.close()
-    if cluster == "local":
-        keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "parallel-local":
-        keep_logging('Running parallel-local mode: bash %s' % job_name, 'Running parallel-local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "cluster":
-        #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    elif cluster == "parallel-cluster":
-        #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-
-def run_core_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, core_results_dir, config_file):
-    file_exists(reference)
-    if args.debug_mode == "yes":
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline_debug.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 2 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    else:
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 2 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
-
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l nodes=1:ppn=4,mem=47000mb,walltime=92:00:00\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_pipeline+'\n')
-    out.close()
-    if cluster == "local":
-        keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "parallel-local":
-        call("bash %s" % job_name, logger)
-    elif cluster == "cluster":
-        #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    elif cluster == "parallel-cluster":
-        #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-
-def run_report_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, core_results_dir, config_file):
-    file_exists(reference)
-    if args.debug_mode == "yes":
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline_debug.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 3 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    else:
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 3 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l nodes=1:ppn=4,mem=47000mb,walltime=92:00:00\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_pipeline+'\n')
-    out.close()
-
-    if cluster == "local":
-        keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "parallel-local":
-        call("bash %s" % job_name, logger)
-    elif cluster == "cluster":
-        call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    elif cluster == "parallel-cluster":
-        call("qsub %s" % job_name, logger)
-        keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-
-def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, core_results_dir, config_file):
-    if args.debug_mode == "yes":
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline_debug.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 4 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    else:
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 4 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l nodes=1:ppn=4,mem=47000mb,walltime=92:00:00\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core_tree" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_pipeline+'\n')
-    out.close()
-
-    if cluster == "local":
-        keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "parallel-local":
-        call("bash %s" % job_name, logger)
-    elif cluster == "cluster":
-        call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    elif cluster == "parallel-cluster":
-        call("qsub %s" % job_name, logger)
-        keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-
-""" Start of Main Method/Pipeline """
-if __name__ == '__main__':
-    # Set up logging modules and config file
-    start_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-    start_time_2 = datetime.now()
-    args = parser().parse_args()
-    global config_file
-    global log_unique_time
-    if args.output_folder != '':
-        args.output_folder += '/'
-    make_sure_path_exists(args.output_folder)
-    log_unique_time = datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
-    if args.config:
-        config_file = args.config
-    else:
-        config_file = os.path.dirname(os.path.abspath(__file__)) + "/config"
-    global logger
-    logs_folder = args.output_folder + "/Logs"
-    make_sure_path_exists(logs_folder)
-    # logger = generate_logger(logs_folder, args.analysis_name, log_unique_time)
-    global Config
-    global files_to_delete
-    files_to_delete = []
-    Config = ConfigParser.ConfigParser()
-    Config.read(config_file)
-
-
-    # Run pipeline steps
-    if "core" not in args.steps and "core_prep" not in args.steps and "report" not in args.steps and "tree" not in args.steps:
-        vc_logs_folder = logs_folder + "/variant_calling"
-        make_sure_path_exists(vc_logs_folder)
-        logger = generate_logger(vc_logs_folder, args.analysis_name, log_unique_time)
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, vc_logs_folder, log_unique_time, args.analysis_name), logger)
-        if args.cluster:
-            cluster_mode = args.cluster
-        else:
-            cluster_mode = "local"
-
-        keep_logging('START: Variant Calling Pipeline', 'START: Variant Calling Pipeline', logger, 'info')
-
-        list_of_files = get_filenames(args.dir, args.type, args.filenames, args.analysis_name, args.suffix)
-        list_of_jobs = create_varcall_jobs(list_of_files, args.type, args.output_folder, args.index, args.steps, config_file, logger)
-        run_varcall_jobs(list_of_jobs, cluster_mode, log_unique_time, args.analysis_name, args.output_folder, logger)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % vc_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % vc_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-        keep_logging('End: Variant calling Pipeline', 'End: Variant calling Pipeline', logger, 'info')
-
-    elif "core_prep" in args.steps:
-        core_prep_logs_folder = logs_folder + "/core_prep"
-        make_sure_path_exists(core_prep_logs_folder)
-        logger = generate_logger(core_prep_logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('START: Extract core snps and generate diagnostic plots', 'START: Extract core snps and generate diagnostic plots', logger, 'info')
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, core_prep_logs_folder, log_unique_time, args.analysis_name), logger)
-        core_temp_dir = args.output_folder + "/core_temp_dir/"
-        make_sure_path_exists(core_temp_dir)
-        keep_logging('Copying vcf files to %s' % core_temp_dir, 'Copying vcf files to %s' % core_temp_dir, logger, 'info')
-        cp_command = "cp %s/*/*_filter2_indel_final.vcf %s/*/*_aln_mpileup_raw.vcf %s/*/*_raw.vcf_5bp_indel_removed.vcf.gz %s/*/*filter2_final.vcf.gz %s/*/*vcf_no_proximate_snp.vcf.gz %s/*/*array %s/*/*unmapped.bed_positions %s" % (args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, core_temp_dir)
-        call(cp_command, logger)
-        list_of_gzipped_files = glob.glob("%s/*.gz" % core_temp_dir)
-        keep_logging('Decompressing gzipped files in %s' % core_temp_dir, 'Decompressing gzipped files in %s' % core_temp_dir, logger, 'info')
-        gzipped_command_list = []
-        for i in list_of_gzipped_files:
-            gzipped_command_list.append("gzip -df %s" % i)
-        num_cores = multiprocessing.cpu_count()
-        results = Parallel(n_jobs=num_cores)(delayed(run_command_list)(i) for i in gzipped_command_list)
-        if args.filenames:
-            list_of_files = get_filenames(args.dir, args.type, args.filenames, args.analysis_name, args.suffix)
-            list_of_vcf_files = generate_custom_vcf_file_list(list_of_files, logger)
-            print len(list_of_vcf_files)
-            with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
-                for file in list_of_vcf_files:
-                    out_fp.write(os.path.basename(file)+'\n')
-            out_fp.close()
-        else:
-            call("ls -1a %s/*.vcf_no_proximate_snp.vcf > %s/vcf_filenames" % (core_temp_dir,core_temp_dir), logger)
-            list_cmd = "ls -1a %s/*.vcf_no_proximate_snp.vcf" % core_temp_dir
-            list_of_files = subprocess.check_output(list_cmd, shell=True)
-            with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
-                for file in list_of_files.splitlines():
-                    out_fp.write(os.path.basename(file)+'\n')
-            out_fp.close()
-        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_core_prep_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, config_file)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % core_prep_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % core_prep_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-
-    elif "core" in args.steps:
-        core_logs_folder = logs_folder + "/core"
-        make_sure_path_exists(core_logs_folder)
-        logger = generate_logger(core_logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('START: Extract core snps and generate diagnostic plots', 'START: Extract core snps and generate diagnostic plots', logger, 'info')
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, core_logs_folder, log_unique_time, args.analysis_name), logger)
-        core_temp_dir = args.output_folder + "/core_temp_dir/"
-        core_results_dir = args.output_folder + "/%s_core_results/" % log_unique_time
-        #make_sure_path_exists(core_results_dir)
-        if args.filenames:
-            list_of_files = get_filenames(args.dir, args.type, args.filenames, args.analysis_name, args.suffix)
-            list_of_vcf_files = generate_custom_vcf_file_list(list_of_files, logger)
-            list_of_label_files = []
-            for i in list_of_vcf_files:
-                list_of_label_files.append(i + '_positions_label')
-            if len(list_of_label_files) == len(list_of_vcf_files):
-                for i in list_of_label_files:
-                    if os.stat("%s/%s" % (core_temp_dir, i)).st_size == 0:
-                        keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
-                        exit()
-            else:
-                keep_logging('Problem in core_prep results. Rerun the core_prep step\n', 'Problem in core_prep results. Rerun the core_prep step\n', logger, 'exception')
-                exit()
-            with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
-                for file in list_of_vcf_files:
-                    out_fp.write(os.path.basename(file)+'\n')
-            out_fp.close()
-        else:
-            list_of_label_files = glob.glob("%s/*_no_proximate_snp.vcf_positions_label" % core_temp_dir)
-            list_of_vcf_files = glob.glob("%s/*_filter2_final.vcf_no_proximate_snp.vcf" % core_temp_dir)
-            if len(list_of_label_files) == len(list_of_vcf_files):
-                for i in list_of_label_files:
-                    if os.stat(i).st_size == 0:
-                        keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
-                        exit()
-            else:
-                keep_logging('Problem in core_prep results. Rerun the core_prep step\n', 'Problem in core_prep results. Rerun the core_prep step\n', logger, 'exception')
-                exit()
-            with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
-                for file in list_of_vcf_files:
-                    out_fp.write(os.path.basename(file)+'\n')
-            out_fp.close()
-        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_core_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % core_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % core_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-
-    elif "report" in args.steps:
-        report_logs_folder = logs_folder + "/report"
-        make_sure_path_exists(report_logs_folder)
-        logger = generate_logger(report_logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('START: Generating alignment and variant calling report', 'START: Generating alignment and variant calling report', logger, 'info')
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, report_logs_folder, log_unique_time, args.analysis_name), logger)
-        core_temp_dir = args.output_folder + "/core_temp_dir/"
-        core_results_dir = args.output_folder + "/%s_core_results/" % log_unique_time
-        make_sure_path_exists(core_results_dir)
-        list_of_label_files = glob.glob("%s/*_label" % core_temp_dir)
-        list_of_vcf_files = []
-        with open("%s/vcf_filenames" % core_temp_dir, 'r') as out_fp:
-            for line in out_fp:
-                list_of_vcf_files.append(line)
-        for i in list_of_label_files:
-            if os.stat(i).st_size == 0:
-                keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
-                exit()
-        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_report_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % report_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % report_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-
-    elif "tree" in args.steps:
-        tree_logs_folder = logs_folder + "/tree"
-        make_sure_path_exists(tree_logs_folder)
-        logger = generate_logger(tree_logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('START: Generating FastTree and RAxML trees', 'START: Generating FastTree and RAxML trees', logger, 'info')
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, tree_logs_folder, log_unique_time, args.analysis_name), logger)
-        core_temp_dir = args.output_folder + "/core_temp_dir/"
-        #core_results_dir = args.output_folder + "/%s_core_results/" % log_unique_time
-        proc = subprocess.Popen(["ls -1ad *_core_results | tail -n1"], stdout=subprocess.PIPE, shell=True)
-        (out2, err2) = proc.communicate()
-        core_results_dir = args.output_folder + "/" + out2.strip()
-        print core_results_dir
-        list_of_label_files = glob.glob("%s/*_label" % core_temp_dir)
-        list_of_vcf_files = []
-        with open("%s/vcf_filenames" % core_temp_dir, 'r') as out_fp:
-            for line in out_fp:
-                list_of_vcf_files.append(line)
-        for i in list_of_label_files:
-            if os.stat(i).st_size == 0:
-                keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
-                exit()
-        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_tree_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % tree_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % tree_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-    else:
-        logger = generate_logger(logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('Please provide argument -steps to run pipeline', 'Please provide argument -steps to run pipeline', logger, 'info')
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % logs_folder, 'Logs were recorded in file with extension log.txt in %s' % logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-
-
-    ## Changed this to individual steps log folder in Logs folder
-    #logger = generate_logger(logs_folder, args.analysis_name, log_unique_time)
-    # time_taken = datetime.now() - start_time_2
-    # keep_logging('Logs were recorded in file with extension log.txt in Logs folder placed in output folder', 'Logs were recorded in file with extension log.txt in Logs folder placed in output folder', logger, 'info')
-    # keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
diff --git a/variant_call_debug.py b/variant_call_debug.py
deleted file mode 100644
index d765bd9..0000000
--- a/variant_call_debug.py
+++ /dev/null
@@ -1,651 +0,0 @@
-__author__ = 'alipirani'
-
-import sys
-import os
-import argparse
-import errno
-import ConfigParser
-import glob
-import re
-import multiprocessing
-import subprocess as subprocess
-from datetime import datetime
-from joblib import Parallel, delayed
-from config_settings import ConfigSectionMap
-from modules.logging_subprocess import *
-from modules.log_modules import *
-from argparse import RawTextHelpFormatter
-
-""" Command Line Argument Parsing """
-def parser():
-    parser = argparse.ArgumentParser(description='\nVariant Calling pipeline for Illumina PE/SE data.\n', formatter_class=RawTextHelpFormatter)
-    required = parser.add_argument_group('Required arguments')
-    optional = parser.add_argument_group('Optional arguments')
-    required.add_argument('-type', action='store', dest="type", help='Type of reads: SE or PE', required=True)
-    required.add_argument('-readsdir', action='store', dest="dir", help='Path to Sequencing Reads Data directory. NOTE: Provide full/absolute path.', required=True)
-    required.add_argument('-outdir', action='store', dest="output_folder", help='Output Folder Path ending with output directory name to save the results. Creates a new output directory path if it doesn\'t exist. NOTE: Provide full/absolute path.', required=True)
-    required.add_argument('-index', action='store', dest="index", help='Reference Index Name. Most Frequently used reference genomes index options: KPNIH1 | MRSA_USA_300 | MRSA_USA_100 | CDIFF_630 | paris Make sure the paths are properly set in config file', required=True)
-    required.add_argument('-steps', action='store', dest="steps", help='Variant Calling Steps in sequential order.\n'
-                                                                     '1.   All: This will run all the steps starting from cleaning the reads to variant calling;\n'
-                                                                     '2.   clean,align,post-align,varcall,filter,stats : This will also run all steps starting from cleaning to variant calling. \nYou can also run part of the pipeline by giving "align,post-align,varcall,filter,stats" which will skip the cleaning part.\nThe order is required to be sequential while using this option. Also, while skipping any of the step make sure you have results already present in your output folder.\n'
-                                                                     '3.   coverage_depth_stats: Run Only Depth of Coverage Stats module after cleaning and read mapping steps\n'
-                                                                     '4.   core_prep: Run this step before running the core steps. This will prepare the data required for generating core SNPs\n'
-                                                                     '5.   core: extract core snps and generate diagnostics plot data matrices to explore filtered snps.')
-    required.add_argument('-analysis', action='store', dest="analysis_name", help='Unique analysis name that will be used as prefix to saving results and log files.', required=True)
-    optional.add_argument('-config', action='store', dest="config", help='Path to Config file, Make sure to check config settings before running pipeline', required=False)
-    optional.add_argument('-suffix', action='store', dest="suffix", help='Fastq reads suffix such as fastq, fastq.gz, fq.gz, fq; Default: fastq.gz', required=False)
-    optional.add_argument('-filenames', action='store', dest="filenames", help='fastq filenames with one single-end filename per line. \nIf the type is set to PE, it will detect the second paired-end filename with the suffix from first filename. \nUseful for running variant calling pipeline on selected files in a reads directory or extracting core snps for selected samples in input reads directory. \nOtherwise the pipeline will consider all the samples available in reads directory.', required=False)
-    optional.add_argument('-cluster', action='store', dest='cluster', help='Run variant calling pipeline in one of the four modes. Default: local. Suggested mode for core snp is cluster that will run all the steps in parallel with the available cores. Make sure to provide a large memory node for this option\nThe possible modes are: cluster/parallel-cluster/parallel-local/local\ncluster: Runs all the jobs on a single large cluster. This will mimic the local run but rather on a large compute node.\nparallel-cluster: Submit variant call jobs for each sample in parallel on compute nodes. This mode is no available for core snp extraction step.\nparallel-local: Run variant call jobs for each sample in parallel locally.\nlocal: Run variant call jobs locally.\nMake Sure to check if the [scheduler] section in config file is set up correctly for your cluster.')
-    optional.add_argument('-clean', action='store', dest="clean", help='clean up intermediate files. Default: OFF')
-    optional.add_argument('-extract_unmapped', action='store', dest="extract_unmapped", help='Extract unmapped reads, assemble it and detect AMR genes using ariba')
-    optional.add_argument('-datadir', action='store', dest="datadir", help='Path to snpEff data directory')
-    optional.add_argument('-snpeff_db', action='store', dest="snpeff_db", help='Name of pre-build snpEff database to use for Annotation')
-    optional.add_argument('-debug_mode', action='store', dest="debug_mode", help='yes/no for debug mode')
-    return parser
-
-
-class set_global_variable():
-    # Set up logging modules and config file
-    args = parser().parse_args()
-    start_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-    start_time_2 = datetime.now()
-    log_unique_time = datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
-    if args.config:
-        config_file = args.config
-    else:
-        config_file = os.path.dirname(os.path.abspath(__file__)) + "/config"
-    logs_folder = args.output_folder + "/Logs"
-    # logger = generate_logger(logs_folder, args.analysis_name, log_unique_time)
-    files_to_delete = []
-    Config = ConfigParser.ConfigParser()
-    Config.read(config_file)
-
-
-
-
-
-
-
-
-""" Sanity checks and directory structure maintenance methods """
-def file_exists(path1):
-    if not os.path.isfile(path1):
-        file_basename = os.path.basename(path1)
-        keep_logging('The file {} does not exists. Please provide another file with full path or check the files path.\n'.format(file_basename), 'The input file {} does not exists. Please provide another file or check the files path.\n'.format(file_basename), logger, 'exception')
-        exit()
-
-def make_sure_path_exists(out_path):
-    try:
-        os.makedirs(out_path)
-    except OSError as exception:
-        if exception.errno != errno.EEXIST:
-            keep_logging('Errors in output folder path! please change the output path or analysis name.', 'Errors in output folder path! please change the output path or analysis name', logger, 'exception')
-            exit()
-
-def get_filenames(dir, type, filenames, analysis, suffix):
-    if not filenames:
-        if not suffix:
-            suffix = ".fastq.gz"
-        try:
-            list_of_files = glob.glob("%s/*%s" % (dir, suffix))
-            if len(list_of_files) < 1:
-                print "No fastq files with suffix %s found in reads directory %s" % (suffix, dir)
-        except OSError as exception:
-            if exception.errno != errno.EEXIST:
-                keep_logging('Error while listing files in reads directory.', 'Error while listing files in reads directory.', logger, 'exception')
-                exit()
-    else:
-        list_of_files = []
-        with open(filenames) as fp:
-            for line in fp:
-                line = line.strip()
-                line = dir + "/" + line
-                list_of_files.append(line)
-    return list_of_files
-
-def generate_custom_vcf_file_list(filenames_array, logger):
-    keep_logging('Generating custom vcf file list for core pipeline steps', 'Generating custom vcf files list for core pipeline steps', logger, 'exception')
-    list_of_vcf_files = []
-    for file in filenames_array:
-        filename_base = os.path.basename(file)
-        if "R1_001_final.fastq.gz" in filename_base or "R1.fastq.gz" in filename_base or "1_combine.fastq.gz" in filename_base or "1_sequence.fastq.gz" in filename_base or "_forward.fastq.gz" in filename_base or "R1_001.fastq.gz" in filename_base or "_1.fastq.gz" in filename_base or ".1.fastq.gz" in filename_base or "_R1.fastq.gz" in filename_base:
-            # Forward reads file name and get analysis name from its name
-            first_file = file
-            if "R1_001_final.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('R1_001_final.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "_R1.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('_R1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "R1.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('R1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "1_combine.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('1_combine.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "1_sequence.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('1_sequence.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "_forward.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('_forward.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "R1_001.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('R1_001.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif "_1.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('_1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            elif ".1.fastq.gz" in filename_base:
-                first_part_split = filename_base.split('.1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-                first_part = first_part + "_filter2_final.vcf_no_proximate_snp.vcf"
-            list_of_vcf_files.append(first_part)
-    return list_of_vcf_files
-
-""" Methods to generate jobs for various pipeline tasks """
-def create_varcall_jobs(filenames_array, type, output_folder, reference, steps, config_file, logger):
-    jobs_temp_dir = "%s/temp_jobs" % output_folder
-    make_sure_path_exists(jobs_temp_dir)
-    keep_logging('Generating cluster jobs in temporary directory %s' % jobs_temp_dir, 'Generating cluster jobs in temporary directory %s' % jobs_temp_dir, logger, 'exception')
-    ##Change the email address; nodes and processor requirements accordingly
-
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-
-    for file in filenames_array:
-        filename_base = os.path.basename(file)
-        if "R1_001_final.fastq.gz" in filename_base or "R1.fastq.gz" in filename_base or "1_combine.fastq.gz" in filename_base or "1_sequence.fastq.gz" in filename_base or "_forward.fastq.gz" in filename_base or "R1_001.fastq.gz" in filename_base or "_1.fastq.gz" in filename_base or ".1.fastq.gz" in filename_base or "_R1.fastq.gz" in filename_base:
-            # Forward reads file name and get analysis name from its name
-            first_file = file
-            # Get the name of reverse reads files
-            if "R1_001_final.fastq.gz" in filename_base:
-                second_part = filename_base.replace("R1_001_final.fastq.gz", "R2_001_final.fastq.gz")
-                first_part_split = filename_base.split('R1_001_final.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "_R1.fastq.gz" in filename_base:
-                second_part = filename_base.replace("_R1.fastq.gz", "_R2.fastq.gz")
-                first_part_split = filename_base.split('_R1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "R1.fastq.gz" in filename_base:
-                second_part = filename_base.replace("R1.fastq.gz", "R2.fastq.gz")
-                first_part_split = filename_base.split('R1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "1_combine.fastq.gz" in filename_base:
-                second_part = filename_base.replace("1_combine.fastq.gz", "2_combine.fastq.gz")
-                first_part_split = filename_base.split('1_combine.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "1_sequence.fastq.gz" in filename_base:
-                second_part = filename_base.replace("1_sequence.fastq.gz", "2_sequence.fastq.gz")
-                first_part_split = filename_base.split('1_sequence.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "_forward.fastq.gz" in filename_base:
-                second_part = filename_base.replace("_forward.fastq.gz", "_reverse.fastq.gz")
-                first_part_split = filename_base.split('_forward.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "R1_001.fastq.gz" in filename_base:
-                second_part = filename_base.replace("R1_001.fastq.gz", "R2_001.fastq.gz")
-                first_part_split = filename_base.split('R1_001.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif "_1.fastq.gz" in filename_base:
-                second_part = filename_base.replace("_1.fastq.gz", "_2.fastq.gz")
-                first_part_split = filename_base.split('_1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-            elif ".1.fastq.gz" in filename_base:
-                second_part = filename_base.replace(".1.fastq.gz", ".2.fastq.gz")
-                first_part_split = filename_base.split('.1.fastq.gz')
-                first_part = first_part_split[0].replace('_L001', '')
-                first_part = re.sub("_S.*_", "", first_part)
-
-            """ Have a standard filename preparation step"""
-            # else:
-            #     print "Using Standard second file naming convention"
-            #     second_part = filename_base.replace("_R1_", "_R2_")
-            #     first_part_split = filename_base.split('_R1.fastq.gz')
-            #     first_part = first_part_split[0].replace('_L001', '')
-            #     first_part = re.sub("_S.*_", "", first_part)
-            second_file = args.dir + "/" + second_part
-            job_name = jobs_temp_dir + "/" + first_part + ".pbs"
-            if not steps:
-                steps == "All"
-            if type == "SE":
-                command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -o %s/%s -analysis %s -index %s -type SE -config %s -steps %s" % (os.path.dirname(os.path.abspath(__file__)), first_file, output_folder, first_part, first_part, reference, config_file, steps)
-            else:
-                command = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/pipeline.py -PE1 %s -PE2 %s -o %s/%s -analysis %s -index %s -type PE -config %s -steps %s" % (os.path.dirname(os.path.abspath(__file__)), first_file, second_file, output_folder, first_part, first_part, reference, config_file, steps)
-
-            with open(job_name, 'w') as out:
-                job_title = "#PBS -N %s" % first_part
-                out.write(job_title+'\n')
-                out.write(Pbs_model_lines+'\n')
-                #out.write(cd_command+'\n') ## changed it to automatically change to PBS working directory
-                out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-                out.write("echo $PBS_O_WORKDIR" + '\n')
-                out.write("cd %s/temp_jobs" % output_folder + '\n')
-                out.write(command+'\n')
-        elif "R2_001_final.fastq.gz" in filename_base or "R2.fastq.gz" in filename_base or "2_combine.fastq.gz" in filename_base or "2_sequence.fastq.gz" in filename_base or "_reverse.fastq.gz" in filename_base or "R2_001.fastq.gz" in filename_base or "_2.fastq.gz" in filename_base or ".2.fastq.gz" in filename_base or "_R2.fastq.gz" in filename_base:
-            continue
-        else:
-            keep_logging('Error while generating cluster jobs. Make sure the fastq filenames ends with one of these suffix: R1_001_final.fastq.gz, R1.fastq.gz, 1_combine.fastq.gz, 1_sequence.fastq.gz, _forward.fastq.gz, R1_001.fastq.gz, _1.fastq.gz, .1.fastq.gz, _R1.fastq.gz', 'Error while generating cluster jobs. Make sure the fastq filenames ends with one of these suffix: R1_001_final.fastq.gz, R1.fastq.gz, 1_combine.fastq.gz, 1_sequence.fastq.gz, _forward.fastq.gz, R1_001.fastq.gz, _1.fastq.gz, .1.fastq.gz, _R1.fastq.gz', logger, 'exception')
-            exit()
-    list_of_jobs = glob.glob("%s/*.pbs" % jobs_temp_dir)
-    return list_of_jobs
-
-def run_command(job):
-    keep_logging('Running Job: bash %s' % job, 'Running Job: bash %s' % job, logger, 'info')
-    call("bash %s" % job, logger)
-    done = "Job Run completed: %s" % job
-    return done
-
-def run_command_list(command):
-    keep_logging('Running command: %s' % command, 'Running command: %s' % command, logger, 'info')
-    call("%s" % command, logger)
-    done = "Command Run completed: %s" % command
-    return done
-
-def run_varcall_jobs(list_of_jobs, cluster, log_unique_time, analysis_name, output_folder, logger):
-    #Generate command list to run on single cluster or in parallel
-    command_list = ""
-    command_list_qsub = []
-    for job in list_of_jobs:
-        command_list = command_list + "bash %s\n" % job
-        command_list_qsub.append(job)
-
-    #cluster mode
-    if cluster == "cluster":
-        keep_logging('Running Job in cluster mode', 'Running Job in cluster mode', logger, 'info')
-        cluster_job_dir = output_folder + "/temp_jobs/cluster/"
-        make_sure_path_exists(cluster_job_dir)
-        Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-        job_name = cluster_job_dir + log_unique_time + "_" + analysis_name + ".pbs"
-        with open(job_name, 'w') as out:
-            job_title = "#PBS -N %s_%s" % (log_unique_time, analysis_name)
-            out.write(job_title+'\n')
-            out.write(Pbs_model_lines+'\n')
-            out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-            out.write("echo $PBS_O_WORKDIR" + '\n')
-            out.write("cd %s/temp_jobs" % output_folder + '\n')
-            out.write(command_list+'\n')
-        out.close()
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        #call("qsub %s" % job_name, logger)
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-        keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-    elif cluster == "parallel-cluster":
-        keep_logging('Running Jobs in parallel-cluster mode', 'Running Jobs in parallel-cluster mode', logger, 'info')
-        for job in command_list_qsub:
-            keep_logging('Submitting Job: qsub %s' % job, 'Submitting Job: qsub %s' % job, logger, 'info')
-            call("qsub %s" % job, logger)
-        keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-    elif cluster == "parallel-local":
-        keep_logging('Running Jobs in parallel-local mode', 'Running Jobs in parallel-local mode', logger, 'info')
-        cluster_job_dir = output_folder + "/temp_jobs/parallel-local/"
-        make_sure_path_exists(cluster_job_dir)
-        job_name = cluster_job_dir + log_unique_time + "_" + analysis_name + ".sh"
-        with open(job_name, 'w') as out:
-            out.write(command_list+'\n')
-        num_cores = multiprocessing.cpu_count()
-        keep_logging('Number of cores available: %s' % num_cores, 'Number of cores available: %s' % num_cores, logger, 'info')
-        results = Parallel(n_jobs=num_cores)(delayed(run_command)(i) for i in command_list_qsub)
-    elif cluster == "local":
-        keep_logging('Running Jobs in local mode', 'Running Jobs in local mode', logger, 'info')
-        for job in command_list_qsub:
-            keep_logging('Running Job: bash %s' % job, 'Running Job: bash %s' % job, logger, 'info')
-            call("bash %s" % job, logger)
-
-""" Pipeline individual task methods """
-def run_core_prep_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, config_file):
-    file_exists(reference)
-    if args.debug_mode == "yes":
-        core_prep_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline_debug.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 1 -jobrun %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, config_file)
-    else:
-        core_prep_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 1 -jobrun %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, config_file)
-
-    job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
-
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_prep_pipeline+'\n')
-    out.close()
-    if cluster == "local":
-        keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "parallel-local":
-        keep_logging('Running parallel-local mode: bash %s' % job_name, 'Running parallel-local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "cluster":
-        #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    elif cluster == "parallel-cluster":
-        #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-
-def run_core_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, core_results_dir, config_file):
-    file_exists(reference)
-    if args.debug_mode == "yes":
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline_debug.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 2 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    else:
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 2 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
-
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_pipeline+'\n')
-    out.close()
-    if cluster == "local":
-        keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "parallel-local":
-        call("bash %s" % job_name, logger)
-    elif cluster == "cluster":
-        #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    elif cluster == "parallel-cluster":
-        #call("qsub %s" % job_name, logger)
-        keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-
-def run_report_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, core_results_dir, config_file):
-    file_exists(reference)
-    if args.debug_mode == "yes":
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline_debug.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 3 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    else:
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 3 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_pipeline+'\n')
-    out.close()
-
-    if cluster == "local":
-        keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "parallel-local":
-        call("bash %s" % job_name, logger)
-    elif cluster == "cluster":
-        call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    elif cluster == "parallel-cluster":
-        call("qsub %s" % job_name, logger)
-        keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-
-def run_tree_analysis(core_temp_dir, reference, analysis_name, log_unique_time, cluster, logger, core_results_dir, config_file):
-    if args.debug_mode == "yes":
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline_debug.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 4 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    else:
-        core_pipeline = "/nfs/esnitkin/bin_group/anaconda2/bin/python %s/modules/variant_diagnostics/core_pipeline.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_filenames %s/vcf_filenames -reference %s -steps 4 -jobrun %s -results_dir %s -config %s" % (os.path.dirname(os.path.abspath(__file__)), core_temp_dir, core_temp_dir, reference, cluster, core_results_dir, config_file)
-    job_name = core_temp_dir + "/" + log_unique_time + "_" + analysis_name + ".pbs"
-    Pbs_model_lines = "#PBS -M %s\n#PBS -m %s\n#PBS -V\n#PBS -l %s\n#PBS -q %s\n#PBS -A %s\n#PBS -l qos=flux\n"\
-                      % (ConfigSectionMap("scheduler", Config)['email'], ConfigSectionMap("scheduler", Config)['notification'], ConfigSectionMap("scheduler", Config)['resources'], ConfigSectionMap("scheduler", Config)['queue'], ConfigSectionMap("scheduler", Config)['flux_account'])
-    with open(job_name, 'w') as out:
-        job_title = "#PBS -N %s_%s_core_tree" % (log_unique_time, analysis_name)
-        out.write(job_title+'\n')
-        out.write(Pbs_model_lines+'\n')
-        out.write("#  Change to the directory you submitted from\nif [ -n \"$PBS_O_WORKDIR\" ]; then cd $PBS_O_WORKDIR; fi" + '\n')
-        out.write("echo \"PBS working directory: $PBS_O_WORKDIR\"" + '\n')
-        out.write("cd %s" % core_temp_dir + '\n')
-        out.write(core_pipeline+'\n')
-    out.close()
-
-    if cluster == "local":
-        keep_logging('Running local mode: bash %s' % job_name, 'Running local mode: bash %s' % job_name, logger, 'info')
-        call("bash %s" % job_name, logger)
-    elif cluster == "parallel-local":
-        call("bash %s" % job_name, logger)
-    elif cluster == "cluster":
-        call("qsub %s" % job_name, logger)
-        keep_logging('Submitting single cluster Job: qsub %s' % job_name, 'Submitting single cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    elif cluster == "parallel-cluster":
-        call("qsub %s" % job_name, logger)
-        keep_logging('Submitting parallel-cluster Job: qsub %s' % job_name, 'Submitting parallel-cluster Job: qsub %s' % job_name, logger, 'info')
-        qid = subprocess.check_output("qsub %s" % job_name, shell=True)
-        print qid.split('.')[0]
-    #keep_logging('You can check the job status with: qstat -u USERNAME', 'You can check the job status with: qstat -u USERNAME', logger, 'info')
-
-
-""" Start of Main Method/Pipeline """
-if __name__ == '__main__':
-    print set_global_variable.log_unique_time
-    args = parser().parse_args()
-    if args.output_folder != '':
-        args.output_folder += '/'
-    make_sure_path_exists(set_global_variable.logs_folder)
-    make_sure_path_exists(args.output_folder)
-
-    # Run pipeline steps
-    if "core" not in args.steps and "core_prep" not in args.steps and "report" not in args.steps and "tree" not in args.steps:
-        vc_logs_folder = logs_folder + "/variant_calling"
-        make_sure_path_exists(vc_logs_folder)
-        logger = generate_logger(vc_logs_folder, args.analysis_name, log_unique_time)
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, vc_logs_folder, log_unique_time, args.analysis_name), logger)
-        if args.cluster:
-            cluster_mode = args.cluster
-        else:
-            cluster_mode = "local"
-
-        keep_logging('START: Variant Calling Pipeline', 'START: Variant Calling Pipeline', logger, 'info')
-
-        list_of_files = get_filenames(args.dir, args.type, args.filenames, args.analysis_name, args.suffix)
-        list_of_jobs = create_varcall_jobs(list_of_files, args.type, args.output_folder, args.index, args.steps, config_file, logger)
-        run_varcall_jobs(list_of_jobs, cluster_mode, log_unique_time, args.analysis_name, args.output_folder, logger)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % vc_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % vc_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-        keep_logging('End: Variant calling Pipeline', 'End: Variant calling Pipeline', logger, 'info')
-
-    elif "core_prep" in args.steps:
-        core_prep_logs_folder = logs_folder + "/core_prep"
-        make_sure_path_exists(core_prep_logs_folder)
-        logger = generate_logger(core_prep_logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('START: Extract core snps and generate diagnostic plots', 'START: Extract core snps and generate diagnostic plots', logger, 'info')
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, core_prep_logs_folder, log_unique_time, args.analysis_name), logger)
-        core_temp_dir = args.output_folder + "/core_temp_dir/"
-        make_sure_path_exists(core_temp_dir)
-        keep_logging('Copying vcf files to %s' % core_temp_dir, 'Copying vcf files to %s' % core_temp_dir, logger, 'info')
-        cp_command = "cp %s/*/*_filter2_indel_final.vcf %s/*/*_aln_mpileup_raw.vcf %s/*/*_raw.vcf_5bp_indel_removed.vcf.gz %s/*/*filter2_final.vcf.gz %s/*/*vcf_no_proximate_snp.vcf.gz %s/*/*array %s/*/*unmapped.bed_positions %s" % (args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, args.output_folder, core_temp_dir)
-        call(cp_command, logger)
-        list_of_gzipped_files = glob.glob("%s/*.gz" % core_temp_dir)
-        keep_logging('Decompressing gzipped files in %s' % core_temp_dir, 'Decompressing gzipped files in %s' % core_temp_dir, logger, 'info')
-        gzipped_command_list = []
-        for i in list_of_gzipped_files:
-            gzipped_command_list.append("gzip -df %s" % i)
-        num_cores = multiprocessing.cpu_count()
-        results = Parallel(n_jobs=num_cores)(delayed(run_command_list)(i) for i in gzipped_command_list)
-        if args.filenames:
-            list_of_files = get_filenames(args.dir, args.type, args.filenames, args.analysis_name, args.suffix)
-            list_of_vcf_files = generate_custom_vcf_file_list(list_of_files, logger)
-            print len(list_of_vcf_files)
-            with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
-                for file in list_of_vcf_files:
-                    out_fp.write(os.path.basename(file)+'\n')
-            out_fp.close()
-        else:
-            call("ls -1a %s/*.vcf_no_proximate_snp.vcf > %s/vcf_filenames" % (core_temp_dir,core_temp_dir), logger)
-            list_cmd = "ls -1a %s/*.vcf_no_proximate_snp.vcf" % core_temp_dir
-            list_of_files = subprocess.check_output(list_cmd, shell=True)
-            with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
-                for file in list_of_files.splitlines():
-                    out_fp.write(os.path.basename(file)+'\n')
-            out_fp.close()
-        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_core_prep_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, config_file)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % core_prep_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % core_prep_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-
-    elif "core" in args.steps:
-        core_logs_folder = logs_folder + "/core"
-        make_sure_path_exists(core_logs_folder)
-        logger = generate_logger(core_logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('START: Extract core snps and generate diagnostic plots', 'START: Extract core snps and generate diagnostic plots', logger, 'info')
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, core_logs_folder, log_unique_time, args.analysis_name), logger)
-        core_temp_dir = args.output_folder + "/core_temp_dir/"
-        core_results_dir = args.output_folder + "/%s_core_results/" % log_unique_time
-        #make_sure_path_exists(core_results_dir)
-        if args.filenames:
-            list_of_files = get_filenames(args.dir, args.type, args.filenames, args.analysis_name, args.suffix)
-            list_of_vcf_files = generate_custom_vcf_file_list(list_of_files, logger)
-            list_of_label_files = []
-            for i in list_of_vcf_files:
-                list_of_label_files.append(i + '_positions_label')
-            if len(list_of_label_files) == len(list_of_vcf_files):
-                for i in list_of_label_files:
-                    if os.stat("%s/%s" % (core_temp_dir, i)).st_size == 0:
-                        keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
-                        exit()
-            else:
-                keep_logging('Problem in core_prep results. Rerun the core_prep step\n', 'Problem in core_prep results. Rerun the core_prep step\n', logger, 'exception')
-                exit()
-            with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
-                for file in list_of_vcf_files:
-                    out_fp.write(os.path.basename(file)+'\n')
-            out_fp.close()
-        else:
-            list_of_label_files = glob.glob("%s/*_no_proximate_snp.vcf_positions_label" % core_temp_dir)
-            list_of_vcf_files = glob.glob("%s/*_filter2_final.vcf_no_proximate_snp.vcf" % core_temp_dir)
-            if len(list_of_label_files) == len(list_of_vcf_files):
-                for i in list_of_label_files:
-                    if os.stat(i).st_size == 0:
-                        keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
-                        exit()
-            else:
-                keep_logging('Problem in core_prep results. Rerun the core_prep step\n', 'Problem in core_prep results. Rerun the core_prep step\n', logger, 'exception')
-                exit()
-            with open("%s/vcf_filenames" % core_temp_dir, 'w') as out_fp:
-                for file in list_of_vcf_files:
-                    out_fp.write(os.path.basename(file)+'\n')
-            out_fp.close()
-        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_core_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % core_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % core_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-
-    elif "report" in args.steps:
-        report_logs_folder = logs_folder + "/report"
-        make_sure_path_exists(report_logs_folder)
-        logger = generate_logger(report_logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('START: Generating alignment and variant calling report', 'START: Generating alignment and variant calling report', logger, 'info')
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, report_logs_folder, log_unique_time, args.analysis_name), logger)
-        core_temp_dir = args.output_folder + "/core_temp_dir/"
-        core_results_dir = args.output_folder + "/%s_core_results/" % log_unique_time
-        make_sure_path_exists(core_results_dir)
-        list_of_label_files = glob.glob("%s/*_label" % core_temp_dir)
-        list_of_vcf_files = []
-        with open("%s/vcf_filenames" % core_temp_dir, 'r') as out_fp:
-            for line in out_fp:
-                list_of_vcf_files.append(line)
-        for i in list_of_label_files:
-            if os.stat(i).st_size == 0:
-                keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
-                exit()
-        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_report_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % report_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % report_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-
-    elif "tree" in args.steps:
-        tree_logs_folder = logs_folder + "/tree"
-        make_sure_path_exists(tree_logs_folder)
-        logger = generate_logger(tree_logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('START: Generating FastTree and RAxML trees', 'START: Generating FastTree and RAxML trees', logger, 'info')
-        call("cp %s %s/%s_%s_config_copy.txt" % (config_file, tree_logs_folder, log_unique_time, args.analysis_name), logger)
-        core_temp_dir = args.output_folder + "/core_temp_dir/"
-        #core_results_dir = args.output_folder + "/%s_core_results/" % log_unique_time
-        proc = subprocess.Popen(["ls -1ad *_core_results | tail -n1"], stdout=subprocess.PIPE, shell=True)
-        (out2, err2) = proc.communicate()
-        core_results_dir = args.output_folder + "/" + out2.strip()
-        list_of_label_files = glob.glob("%s/*_label" % core_temp_dir)
-        list_of_vcf_files = []
-        with open("%s/vcf_filenames" % core_temp_dir, 'r') as out_fp:
-            for line in out_fp:
-                list_of_vcf_files.append(line)
-        for i in list_of_label_files:
-            if os.stat(i).st_size == 0:
-                keep_logging('The file {} is empty. Please rerun core_prep step again.\n'.format(i), 'The file {} is empty. Please rerun core_prep step again.\n'.format(i), logger, 'exception')
-                exit()
-        reference = ConfigSectionMap(args.index, Config)['ref_path'] + "/" + ConfigSectionMap(args.index, Config)['ref_name']
-        run_tree_analysis(core_temp_dir, reference, args.analysis_name, log_unique_time, args.cluster, logger, core_results_dir, config_file)
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % tree_logs_folder, 'Logs were recorded in file with extension log.txt in %s' % tree_logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-
-
-    else:
-        logger = generate_logger(logs_folder, args.analysis_name, log_unique_time)
-        keep_logging('Please provide argument -steps to run pipeline', 'Please provide argument -steps to run pipeline', logger, 'info')
-        time_taken = datetime.now() - start_time_2
-        keep_logging('Logs were recorded in file with extension log.txt in %s' % logs_folder, 'Logs were recorded in file with extension log.txt in %s' % logs_folder, logger, 'info')
-        keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
-
-
-    ## Changed this to individual steps log folder in Logs folder
-    #logger = generate_logger(logs_folder, args.analysis_name, log_unique_time)
-    # time_taken = datetime.now() - start_time_2
-    # keep_logging('Logs were recorded in file with extension log.txt in Logs folder placed in output folder', 'Logs were recorded in file with extension log.txt in Logs folder placed in output folder', logger, 'info')
-    # keep_logging('Total Time taken: {}'.format(time_taken), 'Total Time taken: {}'.format(time_taken), logger, 'info')
diff --git a/variant_calling_pipeline_dev/other b/variant_calling_pipeline_dev/other
deleted file mode 160000
index 94bdeca..0000000
--- a/variant_calling_pipeline_dev/other
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 94bdecadd29c5e18779717f69dbda8fe3f00f9da