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Arkadiy-Garber edited this page Jan 22, 2022 · 1 revision

Using pre-existing HMM sets included with MagicLamp (e.g. lithotrophy, respiration, iron, ROS, etc.)

Binder <-- click here to begin

(Initially forked from here. Thank you to the awesome binder team!)

You can also follow along in this linked video: Video presentation 1

Enter the MagicLamp repository

cd MagicCave/

print the MagicLamp help menu

MagicLamp.py help

print WspGenie help menu

MagicLamp.py WspGenie -h

run WspGenie on test dataset

MagicLamp.py WspGenie -bin_dir test_dataset/ -bin_ext fna -out wspgenie_out

move into the wspgenie output directory and check out the output file

cd wspgenie_out/
less -S wspgenie-summary.csv

check out the gene predictions

cd ORF_calls/
cd ../../

move ORF calls to the main directory

mv wspgenie_out/ORF_calls/ ./

print LithoGenie help menu

MagicLamp.py LithoGenie -h

run LithoGenie on ORF calls

MagicLamp.py LithoGenie -bin_dir ORF_calls/ -bin_ext faa --orfs -out lithogenie_out

check out the output

cd lithogenie_out/
less -S lithogenie-summary.csv
less lithogenie.ALL.heatmap.csv
cd ../

re-run LithoGenie to create a .heatmap.csv for an element-of-interest

MagicLamp.py LithoGenie -bin_dir ORF_calls/ -bin_ext faa --orfs -out lithogenie_out --skip -cat sulfur
# answer 'y' to the question
MagicLamp.py LithoGenie -bin_dir ORF_calls/ -bin_ext faa --orfs -out lithogenie_out --skip -cat iron

check out the updated results

cd lithogenie_out/
less lithogenie.sulfur.heatmap.csv
less lithogenie.iron.heatmap.csv

print the HmmGenie help menu

MagicLamp.py HmmGenie -h

run HmmGenie with a set of HMMs for gas vesicle formation

MagicLamp.py HmmGenie -hmm_dir MagicCave/hmms/gas/ -hmm_ext hmm -bin_dir test_dataset/ -bin_ext fna -out gas_out

check out the results and re-run HmmGenie with more stringent parameters

MagicLamp.py HmmGenie -hmm_dir MagicCave/hmms/gas/ -hmm_ext hmm -bin_dir test_dataset/ -bin_ext fna -out gas_out -clu 5

check out the results

cd gas_out/
less -S genie-summary.csv

Making your own HMMs to use with HmmGenie

Binder <-- click here to begin

You can also follow along in this linked video: Video presentation 2

Run phmmer on a fasta file containing representative sequences of a cytochrome proteins (Cyc1)

phmmer -A Cyc1.refseq.msa --tblout Cyc1.refseq.tblout -E 1E-20 Cyc1.faa ../refseq_db/refseq_nr.sample.faa

Build HMM file from MSA (multiple sequence alignment) file, using hmmbuild

hmmbuild Cyc1.hmm Cyc1.refseq.msa

Query the Cyc1 HMM file against refseq database sample

hmmsearch --tblout Cyc1.hmm.refseq.tblout Cyc1.hmm ../refseq_db/refseq_nr.sample.faa

Examine the output file. What do the bit scores look like for likely false positives

less Cyc1.hmm.refseq.tblout

Move into directory containing MtrA FASTA file, and create an alignment using Muscle.

muscle -in MtrA.faa -out MtrA.fa

Build HMM file from MSA (multiple sequence alignment) file, using hmmbuild

hmmbuild MtrA.hmm MtrA.fa

Query the MtrA HMM file against refseq database sample

hmmsearch --tblout MtrA.hmm.nr.tblout MtrA.hmm ../refseq_db/refseq_nr.sample.faa

Examine the output file. What do the bit scores look like for likely false positives

less MtrA.hmm.nr.tblout

Move the HMM files into a single directory

mv MtrA.hmm ../HMMs/
mv Cyc1.hmm ../HMMs/

Check out the Pfam-derived HMM and bitscores.txt file

less Catalase.hmm

Run HmmGenie (MagicLamp) on test dataset using the new HMM collection

MagicLamp.py HmmGenie -hmm_dir HMMs/ -hmm_ext hmm -bin_dir test_data/ -bin_ext txt -out hmmgenie_out -eval 1E-1
MagicLamp.py HmmGenie -hmm_dir HMMs/ -hmm_ext hmm -bin_dir test_data/ -bin_ext txt -out hmmgenie_out -bit HMMs/bitscores.txt
MagicLamp.py HmmGenie -hmm_dir HMMs/ -hmm_ext hmm -bin_dir test_data/ -bin_ext txt -out hmmgenie_out -bit HMMs/bitscores.txt -clu 2
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