Mercurial > repos > peterjc > tmhmm_and_signalp
diff tools/protein_analysis/signalp3.xml @ 1:3ff1dcbb9440
Migrated tool version 0.0.3 from old tool shed archive to new tool shed repository
author | peterjc |
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date | Tue, 07 Jun 2011 18:04:05 -0400 |
parents | bca9bc7fdaef |
children | f3b373a41f81 |
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--- a/tools/protein_analysis/signalp3.xml Tue Jun 07 18:03:34 2011 -0400 +++ b/tools/protein_analysis/signalp3.xml Tue Jun 07 18:04:05 2011 -0400 @@ -1,4 +1,4 @@ -<tool id="signalp3" name="SignalP 3.0" version="0.0.1"> +<tool id="signalp3" name="SignalP 3.0" version="0.0.3"> <description>Find signal peptides in protein sequences</description> <command interpreter="python"> signalp3.py $organism $truncate 8 $fasta_file $tabular_file @@ -26,14 +26,32 @@ <param name="fasta_file" value="four_human_proteins.fasta" ftype="fasta"/> <param name="organism" value="euk"/> <param name="truncate" value="0"/> - <output name="tabular_file" file="four_human_proteins.signalp3.tsv" ftype="tabular"/> + <output name="tabular_file" file="four_human_proteins.signalp3.tabular" ftype="tabular"/> + </test> + <test> + <param name="fasta_file" value="empty.fasta" ftype="fasta"/> + <param name="organism" value="euk"/> + <param name="truncate" value="60"/> + <output name="tabular_file" file="empty_signalp3.tabular" ftype="tabular"/> + </test> + <test> + <param name="fasta_file" value="empty.fasta" ftype="fasta"/> + <param name="organism" value="gram+"/> + <param name="truncate" value="80"/> + <output name="tabular_file" file="empty_signalp3.tabular" ftype="tabular"/> + </test> + <test> + <param name="fasta_file" value="empty.fasta" ftype="fasta"/> + <param name="organism" value="gram-"/> + <param name="truncate" value="0"/> + <output name="tabular_file" file="empty_signalp3.tabular" ftype="tabular"/> </test> </tests> <help> **What it does** -This calls the SignalP v3.0 tool for prediction of signal peptides, which uses both a neural network (NN) and Hidden Markmov Model (HMM) to produce two sets of scores. +This calls the SignalP v3.0 tool for prediction of signal peptides, which uses both a Neural Network (NN) and Hidden Markov Model (HMM) to produce two sets of scores. The input is a FASTA file of protein sequences, and the output is tabular with twenty columns (one row per protein): @@ -57,7 +75,7 @@ The S-mean is the average of the S-score, ranging from the N-terminal amino acid to the amino acid assigned with the highest Y-max score, thus the S-mean score is calculated for the length of the predicted signal peptide. The S-mean score was in SignalP version 2.0 used as the criteria for discrimination of secretory and non-secretory proteins. -The D-score is introduced in SignalP version 3.0 and is a simple average of the S-mean and Y-max score. The score shows superior discrimination performance of secretory and non-secretory proteins to that of the S-mean score which was used in SignalP version 1 and 2. +The D-score was introduced in SignalP version 3.0 and is a simple average of the S-mean and Y-max score. The score shows superior discrimination performance of secretory and non-secretory proteins to that of the S-mean score which was used in SignalP version 1 and 2. For non-secretory proteins all the scores represented in the SignalP3-NN output should ideally be very low.