annotate tools/protein_analysis/tmhmm2.xml @ 0:bca9bc7fdaef

Migrated tool version 0.0.1 from old tool shed archive to new tool shed repository

<tool id="tmhmm2" name="TMHMM 2.0" version="0.0.1">
    <description>Find transmembrane domains in protein sequences</description>
    <command interpreter="python">
      tmhmm2.py 8 $fasta_file $tabular_file
      ##I want the number of threads to be a Galaxy config option...
    </command>
    <inputs>
        <param name="fasta_file" type="data" format="fasta" label="FASTA file of protein sequences"/> 
        <!--
        <param name="version" type="select" display="radio" label="Model version">
            <option value="">Version 1 (old)</option>
            <option value="" selected="True">Version 2 (default)</option>
        </param>
        -->
    </inputs>
    <outputs>
        <data name="tabular_file" format="tabular" label="TMHMM results" />
    </outputs>
    <requirements>
        <requirement type="binary">tmhmm</requirement>
    </requirements>
    <tests>
        <test>
            <param name="fasta_file" value="four_human_proteins.fasta" ftype="fasta"/>
            <output name="tabular_file" file="four_human_proteins.tmhmm2.tsv" ftype="tabular"/>
        </test>
    </tests>
    <help>
    
**What it does**

This calls the TMHMM v2.0 tool for prediction of transmembrane (TM)  helices in proteins using a hidden Markov model (HMM).

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

 1. Sequence identifier
 2. Sequence length
 3. Expected number of amino acids in TM helices (ExpAA). If this number is larger than 18 it is very likely to be a transmembrane protein (OR have a signal peptide).
 4. Expected number of amino acids in TM helices in the first 60 amino acids of the protein (Exp60). If this number more than a few, be aware that a predicted transmembrane helix in the N-term could be a signal peptide.
 5. Number of transmembrane helices predicted by N-best.
 6. Topology predicted by N-best (encoded as a strip using o for output and i for inside)

Predicted TM segments in the n-terminal region sometime turn out to be signal peptides.

One of the most common mistakes by the program is to reverse the direction of proteins with one TM segment.

Do not use the program to predict whether a non-membrane protein is cytoplasmic or not. 

**Notes**

The raw output from TMHMM v2.0 looks like this (six columns tab separated):

=================================== ======= =========== ============= ========= =============================
gi|2781234|pdb|1JLY|B               len=304 ExpAA=0.01  First60=0.00  PredHel=0 Topology=o
gi|4959044|gb|AAD34209.1|AF069992_1 len=600 ExpAA=0.00  First60=0.00  PredHel=0 Topology=o
gi|671626|emb|CAA85685.1|           len=473 ExpAA=0.19  First60=0.00  PredHel=0 Topology=o
gi|3298468|dbj|BAA31520.1|          len=107 ExpAA=59.37 First60=31.17 PredHel=3 Topology=o23-45i52-74o89-106i
=================================== ======= =========== ============= ========= =============================

In order to make it easier to use in Galaxy, the wrapper script simplifies this to remove the redundant tags, and instead adds a comment line at the top with the column names:

=================================== === ===== ======= ======= ====================
#ID                                 len	ExpAA First60 PredHel Topology 
gi|2781234|pdb|1JLY|B               304  0.01    0.00       0 o
gi|4959044|gb|AAD34209.1|AF069992_1 600  0.00    0.00       0 o
gi|671626|emb|CAA85685.1|           473  0.19    0.00       0 o
gi|3298468|dbj|BAA31520.1|          107 59.37   31.17       3 o23-45i52-74o89-106i
=================================== === ===== ======= ======= ====================

**References**

Krogh, Larsson, von Heijne, and Sonnhammer.
Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes.
J. Mol. Biol. 305:567-580, 2001.

Sonnhammer, von Heijne, and Krogh.
A hidden Markov model for predicting transmembrane helices in protein sequences.
In J. Glasgow et al., eds.: Proc. Sixth Int. Conf. on Intelligent Systems for Molecular Biology, pages 175-182. AAAI Press, 1998.

    </help>
</tool>