comparison tools/protein_analysis/signalp3.xml @ 1:9a8a7f680dd6

Migrated tool version 0.0.3 from old tool shed archive to new tool shed repository

0:a2eeeaa6f75e

<tool id="signalp3" name="SignalP 3.0" version="0.0.1">
    <description>Find signal peptides in protein sequences</description>
    <command interpreter="python">
      signalp3.py $organism $truncate 8 $fasta_file $tabular_file
      ##I want the number of threads to be a Galaxy config option...
    </command>

    <tests>
        <test>
            <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"/>
        </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.

The input is a FASTA file of protein sequences, and the output is tabular with twenty columns (one row per protein):

 * Sequence identifier
 * Neural Network (NN) predictions (13 columns)

Y-max is a derivative of the C-score combined with the S-score resulting in a better cleavage site prediction than the raw C-score alone. This is due to the fact that multiple high-peaking C-scores can be found in one sequence, where only one is the true cleavage site. The cleavage site is assigned from the Y-score where the slope of the S-score is steep and a significant C-score is found.

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.

For non-secretory proteins all the scores represented in the SignalP3-NN output should ideally be very low.

**Hidden Markov Model Scores**

1:9a8a7f680dd6

<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
      ##I want the number of threads to be a Galaxy config option...
    </command>

    <tests>
        <test>
            <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.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 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):

 * Sequence identifier
 * Neural Network (NN) predictions (13 columns)

Y-max is a derivative of the C-score combined with the S-score resulting in a better cleavage site prediction than the raw C-score alone. This is due to the fact that multiple high-peaking C-scores can be found in one sequence, where only one is the true cleavage site. The cleavage site is assigned from the Y-score where the slope of the S-score is steep and a significant C-score is found.

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 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.

**Hidden Markov Model Scores**