Difference between revisions of "Fabry:Sequence alignments (sequence searches and multiple alignments)/Journal"

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The histograms were generated with the [https://www.dropbox.com/sh/wsuttl48gkqkuyh/FVMQmn3Cv9/generate_histograms.sh.txt generate_histograms.sh] script:
 
The histograms were generated with the [https://www.dropbox.com/sh/wsuttl48gkqkuyh/FVMQmn3Cv9/generate_histograms.sh.txt generate_histograms.sh] script:
 
$ bash <span class="plainlinks">[https://www.dropbox.com/sh/wsuttl48gkqkuyh/FVMQmn3Cv9/generate_histograms.sh.txt generate_histograms.sh]</span> *.stats
 
$ bash <span class="plainlinks">[https://www.dropbox.com/sh/wsuttl48gkqkuyh/FVMQmn3Cv9/generate_histograms.sh.txt generate_histograms.sh]</span> *.stats
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GO term comparison
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perl ../download-annotation.pl ids_psiblast_10th_round.txt
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perl ../compare_GO_terms.pl P06280 ids_psiblast_10th_round_GOterms.tsv
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R CMD BATCH hist_psiblast.R
   
 
=== HHblits / HHsearch ===
 
=== HHblits / HHsearch ===

Revision as of 16:25, 7 May 2012

Fabry Disease » Sequence alignments » Journal

Please see Task 2 Scripts for the used scripts.

Sequence searches

Blast

We searched the "big80" database with Blast with the following command:

blastall -p blastp -d /mnt/project/pracstrucfunc12/data/big/big_80 -i P06280.fasta -m 0 -o blastsearch_default.out -v 700 -b 700
perl extract_ids_blast.pl blastsearch_default.out
perl ../download-annotation.pl blastsearch_default_ids.txt
perl ../compare_GO_terms.pl P06280 blastsearch_default_ids_GOterms.tsv
perl parse_blast.pl blastsearch_default.out

R CMD BATCH hist_blast.R

Psi-Blast

The following command was used to run Psi-Blast with AGAL as query sequence against big80. It was run with two and ten iterations configured and an e-value cut-off of 2e-3 and 1e-9, respectively.

$ bash run_psi_blast.sh &> run_psi_blast.log

The log file contains the runtimes of the different psi-blast runs:

Iterations E-value cut-off Runtime
2 2e-3 3m0.814s
2 1e-9 3m9.422s
10 2e-3 14m29.179s
10 1e-9 15m39.251s

Afterwards, the psi-blast output was parsed to collect the all the information about all the hits of the last iteration, which include the e-value, the sequence identity, the coverage in the longer sequence of the pairwise alignment and the length of the alignment. When there were more than one alignment per hit, we used the first one which was also listed in the short result output.

$ for i in psi_results_*.txt; do
    perl parse_psiblast.pl "$i" > "${i%.*}.stats"
  done

The histograms were generated with the generate_histograms.sh script:

$ bash generate_histograms.sh *.stats

GO term comparison

perl ../download-annotation.pl ids_psiblast_10th_round.txt
perl ../compare_GO_terms.pl P06280 ids_psiblast_10th_round_GOterms.tsv
R CMD BATCH hist_psiblast.R

HHblits / HHsearch

We searched the "big80" database with HHblits using the default settings and also with the maximum number of possible iterations (8) with the following commands:

time hhblits -i ../P06280.fasta -d /mnt/project/pracstrucfunc12/data/hhblits/uniprot20_current -e 0.003 -o hhblits_default.out -E 0.003  -z 700
./extract_ids_hhblits.sh hhblits_default.out
perl parse_hhblits.pl hhblits_default.out
perl ../download-annotation.pl hhblits_default.out_cluster_ids_only.tsv
perl ../compare_GO_terms.pl P06280 hhblits_default.out_cluster_ids_only_GOterms.tsv

time hhblits -i ../P06280.fasta -d /mnt/project/pracstrucfunc12/data/hhblits/uniprot20_current -e 0.003 -o hhblits_n8_neu.out -E 0.003 -n 8 -z 800 -b 800
./extract_ids_hhblits.sh hhblits_n8_neu.out
perl parse_hhblits.pl hhblits_n8_neu.out
perl ../download-annotation.pl hhblits_n8_neu.out_cluster_ids_only.tsv
perl ../compare_GO_terms.pl P06280 hhblits_n8_neu.out_cluster_ids_only_GOterms.tsv

R CMD BATCH hist_hhblits.R

Comparison

Venn diagrams created with Oliveros, J.C. (2007) VENNY. An interactive tool for comparing lists with Venn Diagrams.

  >R CMD BATCH all_Evalues.R

Multiple sequence alignments

Results

The following commands were used in our bash script calculate_msas.sh to generate the multiple sequence alignments. The pictures were obtained by using jalview.

$ clustalw -infile="<filename>.fasta" -outfile="msa/clustalw_<filename>.msa" &

$ muscle -in "<filename>.fasta" -out "msa/muscle_<filename>.msa" &

$ /mnt/opt/T-Coffee/bin/t_coffee -seq "<filename>.fasta" -outfile "msa/tcoffe_<filename>.msa" &

$ /mnt/opt/T-Coffee/bin/t_coffee -seq "<filename>.fasta" -method sap_pair -template_file "<filename>.pdb" \
    -outfile "msa/3Dcoffee_<filename>.msa" &

We counted the number of gaps and conserved columns with the perl script countGaps.pl. There is also a small wrapper script - countAllGaps.sh which runs countGaps.pl on all .msa files in a specific folder:

#!/bin/bash

for file in msa/*.msa; do
	perl countGaps.pl "$file" > "${file%.*}.counts"
done