view fathmm/fathmm.xml @ 0:fd66648ce5f9 draft default tip

author saket-choudhary
date Tue, 07 Oct 2014 19:25:07 -0400
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<tool id="fathmm_web" name="FATHMM">
    <description>fathmm web service</description>
        <requirement type="package" version="2.2.1">requests</requirement>
        <requirement type="python-module">requests</requirement>
    <command interpreter="python"> --input $input --output $output --threshold $threshold
        <param name="input" format="txt" type="data" label="Input variants" />
        <param name="threshold" type="float" label="Threshold cutoff" value="-0.75" help="Predictions with scores less than this indicate that the mutation is potentially associated with cancer" />
        <data name="output" format="tabular"/>
            <param name="input" value="fathmm_input.txt"/>
            <param name="threshold" value="-0.75" />
            <output name="output" file="" lines_diff="2"/>

    **What it does**

        This script calls FATHMM( Web API to fetch
        predict functional impact of mutations.

        Input is a plain text file:

        1.  &lt;protein&gt;  &lt;substitution&gt;

        2.  dbSNP rs identifiers

        Where &lt;protein&gt;  is the protein identifier and
        &lt;substitution&gt; is the amino acid substitution in the conventional one letter format.
        Multiple substitutions can be entered on a single line and should be separated by a comma.
        SwissProt/TrEMBL, RefSeq and Ensembl protein identifiers are accepted:

        P43026 L441P
        ENSP00000325527 N548I,E1073K,C2307S


        If you use this tool in Galaxy, please cite :

        Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GLA, Edwards KJ, Day INM, Gaunt, TR. (2013).
        Predicting the Functional, Molecular and PhenotypicConsequences of Amino Acid Substitutions using
        Hidden Markov Models. Hum. Mutat., 34:57-65

        Shihab HA, Gough J, Cooper DN, Day INM, Gaunt, TR. (2013). Predicting the Functional Consequences
        of Cancer-Associated Amino Acid Substitutions. Bioinformatics 29:1504-1510.

        Shihab HA, Gough J, Mort M, Cooper DN, Day INM, Gaunt, TR. (2014).
        Ranking Non-Synonymous Single Nucleotide Polymorphisms based on Disease Concepts. In Press