Initially forked from here. Thank you to the awesome binder team!
Part of the Bioinformatics Virtual Coordination Network :)
Enter the MagicCave
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
go 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 ../../
mv 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 new 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
Initially forked from here. Thank you to the awesome binder team!
Part of the Bioinformatics Virtual Coordination Network :)
Run phmmer on the Cyc1 fasta file
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