From 956dc53333cd5ceb5cbb05030aa0ff64f7d383ec Mon Sep 17 00:00:00 2001 From: Robert Gifford Date: Wed, 29 May 2024 23:32:44 +0200 Subject: [PATCH] Restore website --- index.html | 2 + md/database-schema.md | 54 + website/assets/images/analysis-phase.jpg | Bin 0 -> 198637 bytes website/assets/images/analysis-phase.png | Bin 0 -> 237802 bytes website/assets/images/exploration-phase.jpg | Bin 0 -> 77108 bytes website/assets/images/overview-phases.jpg | Bin 0 -> 44307 bytes website/user-guide/explore.html | 5 +- website/user-guide/overview.html | 217 ++++ website/user-guide/user-guide.html | 1026 +++++++++++++++++++ 9 files changed, 1303 insertions(+), 1 deletion(-) create mode 100644 md/database-schema.md create mode 100755 website/assets/images/analysis-phase.jpg create mode 100755 website/assets/images/analysis-phase.png create mode 100755 website/assets/images/exploration-phase.jpg create mode 100755 website/assets/images/overview-phases.jpg create mode 100755 website/user-guide/overview.html create mode 100755 website/user-guide/user-guide.html diff --git a/index.html b/index.html index 111c244..b089cd5 100644 --- a/index.html +++ b/index.html @@ -18,6 +18,8 @@

The DIGS Tool

A Software Framework for Database-Integrated Genome Screening (DIGS)

Background + Overview + Manual Download GitHub Twitter diff --git a/md/database-schema.md b/md/database-schema.md new file mode 100644 index 0000000..bffcc7a --- /dev/null +++ b/md/database-schema.md @@ -0,0 +1,54 @@ +# DIGS screening database field definitions + +When a screen is initiated by the DIGS tool, a project-specific, relational database is created. This 'screening database' captures data generated by DIGS. + +### `searches_performed` table + +Records the details of BLAST searches that have been performed in this DIGS project. + +| **Field** | **Type** | **Description** | +|---|---|---| +| record_ID | INT | Automatically incremented primary key | +| probe_ID | VARCHAR | Unique iID of probe sequence | +| probe_name | VARCHAR | Name of probe sequence | +| probe_gene | VARCHAR | Name of probe sequence gene | +| target_id | VARCHAR | Unique identifier the TDb file | +| organism | VARCHAR | Name of the organism (Latin binomial) from which TDb was generated | +| target_datatype | VARCHAR | Data type of the TDb file | +| target_version | VARCHAR | Version details of the TDb file | +| target_name | VARCHAR | Name of TDb file | +| Timestamp | TIMESTAMP | Timestamp of the table entry | + + +### `digs_results` table + +Contains the extracted sequences of the loci specified in BLAST_results table, and results of the second round of paired BLAST, in which extracted sequences are 'genotyped' by BLAST comparison to the reference library. + +| **Field** | **Type** | **Description** | +|---|---|---| +| record_ID | INT | Automatically incremented primary key | +| organism | VARCHAR | Organism name (Latin binomial) | +| target_datatype | VARCHAR | Genome data type | +| target_version | VARCHAR | Genome build version details | +| target_name | VARCHAR | Name of genome data file containing the BLAST hit | +| probe_type | VARCHAR | Type of probe sequence (amino acid or nucleotide) | +| extract_start | INT | 5’ (start) position of reverse BLAST alignment in the RSL sequence | +| extract_end | INT | 3’ (end) position of reverse BLAST alignment in the RSL sequence | +| scaffold | VARCHAR | Name of scaffold/contig/chromosome containing the BLAST hit | +| orientation | ENUM | Orientation of the BLAST hit relative to the probe | +| assigned_name | VARCHAR | Name of closest matching sequence in RSL | +| assigned_gene | VARCHAR | Name of gene of closest matching sequence in RSL | +| bit_score | FLOAT | Bit score of the best match from reverse BLAST | +| identity | FLOAT | Percentage identity of the best match from reverse BLAST | +| e_value_num | FLOAT | Coefficient of the expect (e) value for the best match from reverse BLAST | +| e_value_exp | INT | Exponent (base e) of the expect (e) value for the best match from reverse BLAST | +| subject_start | INT | 5’ (start) position of reverse BLAST alignment in the RSL sequence | +| subject_end | INT | 3’ (end) position of reverse BLAST alignment in the RSL sequence | +| query_start | INT | 5’ (start) position of reverse BLAST alignment in the probe sequence | +| query_end | INT | 3’ (end) position of reverse BLAST alignment in the probe sequence | +| mismatches | INT | Number of mismatches in alignment from reverse BLAST | +| gap_openings | INT | Number of gap openings in alignment from reverse BLAST | +| sequence_length | INT | Length of the extracted sequence | +| 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diff --git a/website/user-guide/explore.html b/website/user-guide/explore.html index d3119b6..60a45cc 100644 --- a/website/user-guide/explore.html +++ b/website/user-guide/explore.html @@ -25,7 +25,10 @@

Database-Integrated Genome Screening

Home - Download + Overview + Manual + + Download GitHub Twitter diff --git a/website/user-guide/overview.html b/website/user-guide/overview.html new file mode 100755 index 0000000..851704a --- /dev/null +++ b/website/user-guide/overview.html @@ -0,0 +1,217 @@ + + + + + + + + DIGS: Overview + + + + + + + + + + + + +
+ +

+ Structure of a DIGS-based investigation +

+
+ + +

+ Comparative studies using database-integrated genome screening (DIGS) entail + separate 'exploration' and 'analysis' phases, + with each of these phases being split into two component parts as follows: + + +

    + +
  • Exploration: (1) Setting up and (2) running a similarity search-based screen.
  • +
  • Analysis: (3) Inspecting screening output via a relational database, and (4) + performing comparative analysis of exported sequence data.
  • +
+ + + +

+ + +

Overview - phases

+ +

+ + As shown above, this process is usually iterative - at least to some degree - since + analysis of screening results often reveals new information that can be used to + design more informative or comprehensive screens. + +

+ + +
+ + + +

+ Exploration phase: Setting up and running an in silico screen +

+
+ +

+ + DIGS is a project-based framework in which investigations are centred around + a genome feature of interest. Any genome feature can be investigated in principle, + so long as it contains sufficient sequence conservation to be reliably detected in a similarity + search. + +

+ + +

+ The 'reference sequence library' + is a curated set of sequences relevant to the genome feature under investigation). + Usually this will consist of: + + +

    +
  • A set of conserved DNA or polypeptide sequences derived from the genome feature of interest.
  • +
+ +

+ + +

+ However, depending on the kind of investigation being performed, it may also contain : + + +

    +
  • Sequences that do not derive from the genome feature under investigation, + but can provide useful information about the locus in which it occurs.
  • +
  • Sequences representing genome features that are not relevant to the investigation, + but are sufficiently similar to them to generate 'false positive' matches.
  • +
+ + + Screening entails selecting particular sequences from the reference library for use as + 'probes' in a BLAST search of a specific 'target database'. +

+ + +

+ Sequences that match to the query ('hits') can then be extracted and classified. + A convenient way of rapidly classifying or 'genotyping' hits is via BLAST-based comparison to the + reference library, as indicated in the illustration below. +

+ + + + +

Exploration phase

+ + + +
+ + Schematic representation of the exploration phase of a DIGS-based investigation. +
+ Here, the genome features being investigated are a set of related genes + In step (1) a sequence from the reference library is selected and used + as a 'probe' or 'query' in a BLAST-based search of a chosen target database. + In step (2), sequences identified in this search are extracted + and classified via BLAST-based comparison to the reference library. + These searches provides a way to effectively 'delve in' to + genomic databanks and recover related sequences and as such, they provide a means + to survey unmapped regions of the genomic 'landscape'. + +
+ + +
+

+ Analysis phase: Inspecting results and exporting data for comparative analysis +

+
+ + +

+ In DIGS, a similarity search-based screening pipeline is linked to a relational + database management system (RDBMS), and the outputs of screening are captured + in a project-specific relational database. +

+ +

+ This approach not only provides a convenient and robust + basis for implementing systematic, automated screens that proceed in an efficient, + non-redundant way, it also allows screening data to be interrogated using + structured query language (SQL) - a well-established, powerful approach for + querying relational databases. +

+ +

+ +

    + +
  1. Investigation of output via the relational database.
  2. +
  3. Comparative genomic analysis of exported sequence data
  4. +
    +
+ + +
+ + + +

Analysis phase

+ + + +
+ Analysing screening output: A schematic representation of the two component + parts of the 'analysis' phase of DIGS-based screen + (some comparative analysis do not require an alignment, but most do). +
+ + + + +
+ + +
+ + + + + +
+ + + + + diff --git a/website/user-guide/user-guide.html b/website/user-guide/user-guide.html new file mode 100755 index 0000000..24fd889 --- /dev/null +++ b/website/user-guide/user-guide.html @@ -0,0 +1,1026 @@ + + + + + DIGS tool: User Guide + + + + + + + + + + + +
+ + User guide + +

+ Contents +

+
+ + +

+ + +

    +
  1. Part 1. Setting up for DIGS +
    +
    +
      +
    1. Software requirements
    2. +
    3. Program-specific environment variables
    4. +
    5. Setting up target libraries
    6. +
    7. Creating project-specific sequence libraries
    8. +
    9. Creating a project-specific 'control file'
    10. +
    +
  2. +
    +
  3. Part 2. Performing DIGS and working with project databases
  4. +
    +
      +
    1. Running an in silico screen
    2. +
    3. Investigating screening results via a GUI SQL client
    4. +
    5. Updating reference sequences and reclassifying results
    6. +
    7. Incorporating linked data into a DIGS project
    8. +
    9. 'Consolidate': Merging hits in the DIGS results table into larger sequences.
    10. +
    11. Extracting hit sequences and their flanks using the DIGS tool
    12. +
    + +
+ + +

+ + +
+ + + + +

+ Part 1. Setting up for DIGS +

+
+ + + + + +

+ 1.1. Software requirements +

+
+ + +

+ DIGS tool:
+ The latest version can be obtained by clicking the 'Download' button at the top of this page. + All software used within the DIGS framework can be installed on LINUX or UNIX systems. +

+ + + +

+ + * Since Macintosh (OSX and above) and Windows (10 and above) + both have LINUX/UNIX subsystems, + implementation on these platforms is - in theory - also possible. + In practice, however, this may require some troubleshooting, and should probably + only be attempted by users comfortable with tackling issues related to use of + PERL's DBI (database interface) + on these platforms. + +

+ + +

+ Scripting language:
+ The DIGS tool requires PERL. + The DBI (database interface) + module should be installed via CPAN if it is not part of your native PERL distribution. +

+ + + +

+ Similarity searches:
+ The DIGS tool uses the freely available + BLAST+ + package to perform sequence similarity searches. +

+ + +

+ + Relational database management system (RDBMS):
+ The DIGS tool uses the MySQL relational database management system. + MySQL Community Server + is a free-to-use, open source version of this program. +

+ + + +

+ GUI client for relational database (optional):
+ To interface with DIGS project databases via a graphical user interface (GUI), + please install one of the many SQL client programs that are compatible with MySQL, + such as SequelPro. +

+ + + + + +
+

+ 1.2. Setting up your local environment for DIGS +

+
+ + + +

+ The DIGS tool requires users to set four environment variables in their home environment: + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameDefinition
$DIGS_GENOMESPath to the top-level of the 'target-databases' directory (see below)
$DIGS_HOMEPath to the DIGS-Tool directory
$DIGS_MYSQL_USERYour mysql user name
$DIGS_MYSQL_PASSWORDYour mysql password
+
+ + +

+ + +

+ Note that one of the environment variables is the path to the 'target-databases' directory. + DIGS requires that all project-relevant sequence databases + are contained within this directory, which has a simple, pre-defined subdirectory + structure (see section 1.3 below). +

+ + +

+ Once environment variables have been set, you should be able to run the DIGS tool + script with the -h (--help) option as shown here: + +

+  giff01r@Alpha:~/DIGS/DIGS-tool$ ./digs_tool.pl -h
+ + + +

+ + +

+ This should print the DIGS input help page to the console: + +

+  ### DIGS version 1.13.2
+  
+	 ### usage: ./digs_tool.pl m=[option] -i=[control file] -h=[help]
+
+	 ### Main functions
+
+	 -m=1  Prepare target files (index files for BLAST)
+	 -m=2  Do DIGS
+	 -m=3  Reassign loci
+	 -m=4  Defragment loci
+	 -m=5  Consolidate loci
+
+	 ### Summarising target databases
+
+	 -g=1  Summarise targets (brief summary, by species)
+	 -g=2  Summarise targets (long, by individual target file)
+
+	 ### Managing DIGS screening DBs
+
+	 -d=1  Import tab-delimited data
+	 -d=2  Flush core tables
+	 -d=3  Drop tables
+	 -d=4  Drop a screening DB
+	 -d=5  Append data to 'digs_results' table
+	 -d=6  Extract sequences using tabular file
+
+	 Target path variable '$DIGS_GENOMES' is set to '/home2/db/digs_genomes'
+ +

+ + +
+

+ 1.3. Setting up target libraries +

+
+ + +

+ The DIGS tool is designed to perform systematic, similarity-search based screening + of local DNA sequence 'databases' - in other words, collections of FASTA-formatted + DNA sequence data. +

+ + +

+ Genome data should be stored in a directory tree with five subdirectory levels, as shown below. +

+ + +

DIGS target directory structure

+ + + +

+ To illustrate this another way - the figure below shows how a very simple target directory + (in this example, it is only the Y chromosome of the human genome) + might look on the (macintosh) desktop. +

+ +
+ + +

DIGS target directory structure

+ + +
+ +

+ Table: Directory levels in the 'target databases' directory +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
LevelInformation
1The top level directory - should be the path specified under the DIGS environment + variable $DIGS_GENOMES. +
2Group - this level allows for categorisation of species or samples from + which sequence data are derived, so as to facilitate data management. + For example, if screening in a wide range of endothermic vertebrate + species, we might have separate bird and mammal directories at this level. +
3Species or sample name - within the group directory, data are + logically separated based on the provenance of sequence data. + For example, genome data from distinct humans should be under + a subdirectory named 'Homo_sapiens' or something equivalent. +
4Data type - name appropriately and (ideally) consistently (e.g. wgs, transcriptome)
5Assembly version - FASTA data files should be in this directory
+
+ + + +

+ + Before we can use BLAST to screen target sequence databases, it is neccessary to + 'index' these files for BLAST searching. +

+ + +

+ This can be performed efficiently by running the digs_tool.pl script as follows: + + +

+  ./digs_tool.pl –m=1
+ +

+ + +

+ This will initiate a console-based interactive process in which the target + database folder is scanned, and genomes that require indexing for BLAST are identified. +

+ + +
+

+ 1.4. Creating project-specific sequence libraries +

+
+ + +

+ To perform DIGS we require a library of FASTA-formatted sequences, called the + 'reference sequence library'. +

+ + +

+ The reference sequence library is used a source of 'probes' for screening, + and also provides a means of classifying hits (i.e. similar sequences) identified in these screens. +

+ +

+ The DIGS tool uses a simple rule to capture data from the headers of + FASTA-formatted reference (and probe) sequences, in which + everything to the left of the last underscore in the FASTA header + is taken as the 'species name' and everything to the right is taken + as the 'genome feature name'. +

+ + + +

DIGS target directory structure

+ + + +

+ A subset of reference sequences should be selected as probes. + The entire reference library can be used - in which case there is no need to + create a separate file – but it is often sufficient to use only a subset of + sequences from the reference sequence library, in which case, a separate file + containing this subset should be created. + The path to this file is specified in the DIGS control file. +

+ +
+

+ 1.5. Creating a project-specific control file +

+
+ +

+ The DIGS control file is a text file that specifies parameters and paths for DIGS. +

+ +

+ Control files are structured as NEXUS-style + blocks bounded by BEGIN and ENDBLOCK statements as shown below. + +

+  Begin SCREENDB;
+	 db_name=erv_lenti;
+	 mysql_server=localhost;
+  ENDBLOCK;
+
+  BEGIN SCREENSETS;
+	  query_aa_fasta=/home/rob/DIGS/projects/lenti-probes.DIGS.faa;
+	  reference_aa_fasta=/home/rob/DIGS/projects/ERV-reference.DIGS.faa;
+	  bitscore_min_tblastn=60;
+	  consolidated_reference_aa_fasta=/home/rob/DIGS/projects/;
+	  output_path=./tmp/;
+	  seq_length_minimum=50;
+	  defragment_range=10;
+
+	  #query_na_fasta=/home/rob/DIGS/projects/lenti-probes.fna
+	  #reference_na_fasta=/home/rob/DIGS/projects/lenti-probes.fna
+	  #bitscore_min_blastn=30;
+  ENDBLOCK;
+
+  BEGIN TARGETS;
+	  Mammalia/
+  ENDBLOCK;
+  
+ +

+ + +

+ + Note that not all parameters will need to be defined for every screen. + +

+ + +

+ Table: Parameters defined in the DIGS control file +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDefinition
db_nameName of the project-specific screening database
mysql_servername of the mysql_server to use (set to 'localhost' to use local database)
query_aa_fastaPath to file with amino acid probe sequences
reference_aa_fastaPath to file with amino acid reference sequences
query_na_fastaPath to file with nucleic acid probe sequences
reference_na_fastaPath to file with nucleic acid reference sequences
bitscore_min_tblastnMinimum bit-score of tBLASTn hit to extract
bitscore_min_blastnPMinimum bit-score of BLASTn hit to extract
seq_length_minimumMinimum length of sequence to extract
defragment_rangeRange within which two BLAST hits in the target sequence will be merged
+ + + + +
+
+ + + +
+

+ Part 2. Performing DIGS and working with project databases +

+
+ + + +

+ 2.1. Running an in silico screen +

+
+ + +

+ Run the DIGS tool using the -m=2 option to start screening. + + +

+   ./digs_tool.pl -m=2 -i [path to your project control file]
+ + +

+

+ e.g. + + +

+   ./digs_tool.pl -m=2 -i myControlFile.ctl
+ + +

+ + +

+ This will initiate the screening process. + A progress log is reported to the screen: +

+ + + +

+ +

+   
+
+	  Connecting to DB:  eve_1_parvoviridae
+	  Created report directory
+	  Path: './tmp/result_set_38612_1618435290'
+	  Probe sequences:   22 amino acid FASTA sequences
+	  Reference library: 39902 amino acid sequences
+	  Targets:           1866 target files
+	  Previous queries:  41052 previous queries
+	  Skipped in set:    41008 (of 41052)
+	  Searches to run    44
+	  
+	  ### Starting database-integrated genome screening
+
+	  tblastn: 1: 'Eurynorhynchus_pygmeus' (GCA_003697955, low_coverage)
+	  target: 'GCA_003697955.1_ASM369795v1_genomic.fna'
+	  probe:  'NC_001401-AAV2_NS'
+	  
+		 # 2 matches to probe: NC_001401-AAV2, NS
+		 # 0 matches above threshold (excluded: 0 < length; 2 < bitscore)
+		 # done 1 of 44 queries (%2.27)
+
+	  tblastn: 2: 'Hypophthalmichthys_nobilis' (HypNob1.0, low_coverage)
+	  target: 'GCA_004193235.1_HypNob1.0_genomic.fna'
+	  probe:  'NC_001401-AAV2_NS'
+
+ 
+ +

+ + + +

+ The first few lines of output report the properties of the probe, reference + and target sequence libraries that compose the screen. +

+ +

+ In addition, the status of the screen is reported - of all the individual BLAST + searches that comprise the screen, how many have been performed, and how many are + still outstanding. +

+ +

+ Run with the -v (--verbose) option to see more detailed log output. +

+ + +

+ If screening is interrupted for any reason, simply restart the process. + Screening will continue from the point at which the interruption occurred. +

+ + + + +
+

+ 2.2. Investigating screening results using an SQL client +

+
+ + + +

+ + The relational database component allows efficient + monitoring and summarising the output of screening. + Particularly when the screening project database is enriched with additional + data (e.g. taxonomic data, see below) this greatly + enhances users capacity to interrogate the data generated by screening. + + +

+ +

+ A MySQL client with a graphical user interface (e.g. SequelPro) + can be used to connect to the screening database and select view + the results of screening. For example, as shown below. +

+ + + +

DIGS screenshot

+ + +
+ Visualising DIGS results: In the example shown above, an SQL statment is + used to select sequences that matched to Miniopterus endogenous retrovirus (MinERVa) + with a BLAST bitscore of at least 100. The 'ORDER BY' part of the SQL statement is + used to sort the matching rows in order of the time they were entered into the results + table (the TIMESTAMP field on each database table captures this information). +
+ +
+

+ As well as selecting rows, it is often useful to count rows based using + an SQL statement with a 'GROUP BY' clause, as shown in the example below. +

+ + + +

DIGS screenshot (counts)

+ + +
+ Use of a 'GROUP BY' statement to count by category: In the example shown above, an SQL statment is + used to select sequences that matched to Miniopterus endogenous retrovirus (MinERVa) + with a BLAST bitscore of at least 100. The 'ORDER BY' part of the SQL statement is + used to sort the matching rows in order of the time they were entered into the results + table (the TIMESTAMP field on each database table captures this information). +
+ + +
+

+ 2.3. Updating reference sequences and reclassifying results +

+
+ + +

+ Inspecting screening results often reveals the deficiencies of the reference + sequence library that was used to classify screening results. +

+ + + +

+ In particular, screening can make it clear that there are certain variants of + the genome feature being investigated that are not represented in the reference + sequence library. +

+ + +

+ When this happens it is useful to reclassify sequences identified via + screening using an updated reference sequence library that includes representatives of + the missing variant(s). +

+ +

+ + To do this, simply add the new sequences to your project-specific reference library + and run the digs_tool.pl script using your project control file and the -m=3 option, + as shown here: + +

+  ./digs_tool.pl -m=3 -i ../projects/eve/erv_1_lenti.ctl 
+     
+
+	  Connecting to DB:  erv_lenti
+	  Created report directory
+	  Path: './tmp/result_set_33636_1618435124'
+	  Reference library: 59 amino acid sequences
+
+
+      Enter a WHERE statement to limit reaasign (Optional) : WHERE assigned_name = 'RELIK'
+      
+ + +

+ +

+ The option to limit the reassign via a WHERE statement is presented. + This can save time when the aim is to reassign only a subset of hits + in a digs_results table with a large number of entries. + If using a WHERE statement enter it as you would in a SQL client (i.e. using + appropriate syntax), as shown above. +

+ + + +
+ + + + +

+ 2.4. Incorporating linked data into a DIGS project +

+
+ + +

+ Incorporating additional, linked data tables into DIGS project databases allows + users to reference these data in SQL queries. +

+ + +

+ For example, adding a table that contains taxonomic information about the species + screened will allow SQL queries to reference higher taxonomic ranks than species. +

+ + + + +

+ The digs_tool.pl script provides functions for console-based management of + additional tables in DIGS project databases. + For example, to add a table with virus taxonomy, first run DIGS as follows: + +

+  giff01r@Alpha:~/DIGS/DIGS-tool$ ./digs_tool.pl -d=1 -i ../projects/eve/eve_1_parvo.ctl
+  
+ +

+ + + + +

+ The console will prompt for input, beginning with the path to a file containing tabular data + +

+  Connecting to DB:  erv_lenti
+
+	 #### WARNING: This function expects a tab-delimited data table with column headers!
+
+	 Please enter the path to the file with the table data and column headings
+
+	 : ../projects/eve/tabular/ncbi_virus_taxonomy.txt
+ + +

+ +

+ If valid tabular input is received, a breakdown of the column headers will be shown. + If it looks correct enter 'y' (yes), and select appropriate options. For example: +

+ + +

+ +

+  The following cleaned column headers (i.e. table fields) were obtained
+
+		 Column 1: 'Target_species'
+		 Column 2: 'Target_class'
+		 Column 3: 'Target_superorder'
+		 Column 4: 'Target_order'
+		 Column 5: 'Target_family'
+		 Column 6: 'Target_genus'
+
+	 Is this correct? (y/n): y
+
+		 1. Create new ancillary table
+		 2. Append data to existing ancillary table
+		 3. Flush existing ancillary table and import fresh data
+
+	 Choose an option (1/2/3): 1
+
+	 What is the name of the new table? : host_taxonomy
+
+	 Creating ancillary table 'host_taxonomy' in eve_1_parvoviridae screening database
+
+	 #### IMPORTING to table 'host_taxonomy'
+
+	 # Exit
+
+ +

+ + +

+ NOTE - in the example above I have deliberately prefixed the + column names - which are taxonomic ranks - with 'Target_'. + This avoid conflicting with any of + MySQLs reserved words + - one of which is 'ORDER'. +

+ + + +

+ Now that I have added the host_taxonomy table, I can select + database entries based on any of the taxonomic ranks included in my file, + through reference to the 'host_taxonomy' table, as shown below. +

+ + + +

DIGS screenshot

+ +
+ +

+ 2.5. Merging hits in the DIGS results table into larger sequences +

+
+ +

+ When relying on sequence similarity as a means of recovering the sequences of + related genome features, a limitation is that the sequences of many interesting + genome features are only partially conserved, and large regions of sequence + within these features may be rearranged or divergent. +

+ +

+ However, when two or more conserved features occur contiguously, their relationship + can be used to determine the coordinates of a more complete sequence for the + genome feature of interest. +

+ + + +

+ For example, integrated retroviruses ('proviruses') are comprised of internal coding domains + (gag, pol, env - in that order), flanked by terminal LTRs + that are usually (though not always entirely) non-coding. + However, + endogenous retroviruses (ERVs) frequently have much complex genome arrangements, + with many being fragmentary or mosaic in structure, and large regions of + the integrated provirus often being highly divergent from anything seen previously. +

+ + + +

+ Accordingly, it makes semse to screen first using individual features + (i.e. Gag, Pol, Env polypeptides, plus LTR nucleotide sequences), + as probes and references, then to 'consolidate' the hits to these + probes into a larger sequences comprised of the hits, plus the intervening sequences. + At the same time, we can record the relationship between the component parts of + the merged sequence, where merging occurs. +

+ +

+ The DIGS tool can be used to implement a ‘consolidation’ of this kind. + Contiguous hits in the ‘digs_results’ table are merged based on whether they + are within a user-defined distance of one another. +

+ + +

Consolidate

+ + +

+ Running the consolidation process produces a set of merged sequences, and + also classifies these sequences using the same approach applied when + generating the digs_results table. + The results - i.e. a non-overlapping set of sequences, merged as determined by + user-specified rules - are entered into the 'loci' table + (see the database schema page for details). + A separate reference sequence library that is appropriate for classifying + the longer sequences should be used for classifying the consolidated results, and is + specified by a distinct parameter (see section 4 in the set-up stages above). +

+ + +

+ The loci table contains most of the same fields as the digs_results table, + but also includes a 'locus_structure' field that records the relationship + between merged hits, including their orientation relative to one another. +

+ + +

+ The locus table includes a field 'locus-structure' that shows the order + and orientation of the individual hits from the digs-results table that + were combined to create the merged hit, as shown below. +

+ + +
+ + + +

Consolidation - loci table query

+ + + + +
+ + + +

+ 2.6. Extracting hit sequences and their flanks using the DIGS tool +

+
+ + +

+ Working within the framework of the DIGS tool (i.e. using SQL to query DIGS results, + and reclassifying sequences through merging and updates reference libraries) + can provide many useful insights into the distribution and diversity of a given + genome feature. +

+ +

+ For further investigations, however, it will often be necessary to export sequences + from the DIGS screening database so that they can be analysed using other kinds + of bioinformatic and comparative approaches. +

+ +

+ As well as extracting the sequence matches themselves, it is often helpful to extract + the upstream and downstream flanking sequences. +

+ +

+ + To do this, run the digs_tool.pl script using the -d=6 option, + and providing a tabular file containing locus data using the -i option + as illustrated here: + +

+    giff01r@Alpha:~/DIGS/DIGS-tool$ ./digs_tool.pl -d=6 -i loci.tsv
+ + +

+ + + +
+ +
+ +
+ + + + + +
+ + + + +