comparison tools/protein_analysis/tmhmm2.xml @ 16:7de64c8b258d draft

Uploaded v0.2.5, MIT licence, RST for README, citation information, development moved to GitHub

15:6abd809cefdd

<tool id="tmhmm2" name="TMHMM 2.0" version="0.0.10">
    <description>Find transmembrane domains in protein sequences</description>
    <!-- If job splitting is enabled, break up the query file into parts -->
    <!-- Using 2000 chunks meaning 4 threads doing 500 each is ideal -->
    <parallelism method="basic" split_inputs="fasta_file" split_mode="to_size" split_size="2000" merge_outputs="tabular_file"></parallelism>
    <command interpreter="python">
      tmhmm2.py "\$NSLOTS" $fasta_file $tabular_file
      ##Set the number of threads in the runner entry in universe_wsgi.ini
      ##which (on SGE at least) will set the $NSLOTS environment variable.
      ##If the environment variable isn't set, get "", and defaults to one.
    </command>
    <stdio>
        <!-- Anything other than zero is an error -->
        <exit_code range="1:" />
        <exit_code range=":-1" />

-----

**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.
http://dx.doi.org/10.1006/jmbi.2000.4315

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.
http://www.ncbi.nlm.nih.gov/pubmed/9783223

http://www.cbs.dtu.dk/services/TMHMM/

    </help>
</tool>

16:7de64c8b258d

<tool id="tmhmm2" name="TMHMM 2.0" version="0.0.11">
    <description>Find transmembrane domains in protein sequences</description>
    <!-- If job splitting is enabled, break up the query file into parts -->
    <!-- Using 2000 chunks meaning 4 threads doing 500 each is ideal -->
    <parallelism method="basic" split_inputs="fasta_file" split_mode="to_size" split_size="2000" merge_outputs="tabular_file"></parallelism>
    <command interpreter="python">
      tmhmm2.py "\$NSLOTS" $fasta_file $tabular_file
      ##I want the number of threads to be a Galaxy config option...
      ##Set the number of threads in the runner entry in universe_wsgi.ini
      ##which (on SGE at least) will set the $NSLOTS environment variable.
      ##If the environment variable isn't set, get "", and the python wrapper
      ##defaults to four threads.
    </command>
    <stdio>
        <!-- Anything other than zero is an error -->
        <exit_code range="1:" />
        <exit_code range=":-1" />

-----

**References**

If you use this Galaxy tool in work leading to a scientific publication please
cite the following papers:

Peter J.A. Cock, Björn A. Grüning, Konrad Paszkiewicz and Leighton Pritchard (2013).
Galaxy tools and workflows for sequence analysis with applications
in molecular plant pathology. PeerJ 1:e167
http://dx.doi.org/10.7717/peerj.167

Krogh, Larsson, von Heijne, and Sonnhammer (2001).
Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes.
J. Mol. Biol. 305:567-580.
http://dx.doi.org/10.1006/jmbi.2000.4315

Sonnhammer, von Heijne, and Krogh (1998).
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.
http://www.ncbi.nlm.nih.gov/pubmed/9783223

See also http://www.cbs.dtu.dk/services/TMHMM/

This wrapper is available to install into other Galaxy Instances via the Galaxy
Tool Shed at http://toolshed.g2.bx.psu.edu/view/peterjc/tmhmm_and_signalp
    </help>
</tool>