<tool id="transFIC_web" name="TransFIC">
    <description>TransFIC web service</description>
    <requirements>
        <requirement type="package" version="2.2.1">requests</requirement>
        <requirement type="package" version="7.19.3.1">pycurl</requirement>
        <requirement type="package" version="4.1.0">beautifulsoup4</requirement>
        <requirement type="python-module">requests</requirement>
        <requirement type="python-package">pycurl</requirement>
        <requirement type="python-package">bs4</requirement>
    </requirements>
    <command interpreter="python">
        transFIC_web.py --input $input --output $output
    </command>
    <inputs>
        <param name="input" format="text" type="data" label="Inout Variants"/>
    </inputs>
    <outputs>
        <data name="output" format="tabular"/>
    </outputs>
    <tests>
        <test>
            <param name="input" value="condel_input.tsv"/>
            <output name="output" file="transfic_output.csv"/>
        </test>
    </tests>
    <help>
        **What it does**

        This script calls TransFIC web api at  http://bg.upf.edu/transfic/

        TransFIC stands for TRANsformed Functional Impact for Cancer.
        It is a method to transform Functional Impact Scores taking into account the differences in basal tolerance to germline SNVs of genes that belong to
        different functional classes. This transformation allows to use the scores provided by well-known tools (e.g. SIFT, Polyphen2, MutationAssessor)
        to rank the functional impact of cancer somatic mutations. Mutations with greater transFIC are more likely to be cancer drivers.


        **How does it work**


        TransFIC takes as input the Functional Impact Score of a somatic mutation provided by one of the aforementioned tools.
        It then compares that score to the distribution of scores of germline SNVs observed in genes with similar functional
        annotations (for instance genes with the same molecular function as provided by the Gene Ontologies).
        The score is thus transformed using the Zscore formula.
        The result is that mutations in genes that are less tolerant to germline SNVs are amplified,
        while the scores of mutations on relatively tolerant genes are decreased.

        **Input**

        There are two main formats allowed:


        SNVs may be submitted for analysis both in chromosome and protein coordinates.


        The chromosome coordinates (hg19) input must follow this format:


        [CHROMOSOME] [START] [END] [MUTANT_NUCLEOTIDE]



        The END column is the same as the START for SNVs.
        Those four columns must be separated by tabs. Also a fifth column can optionally be added with the Variant name


        Ex:

        9   32473058    32473058    A

        7   43918688    43918688    C

        Additionally, the input could be composed by two columns the strand of the SNV and an identifier:

        [PROTEIN_ID][variant]

        Also tab separated. Currently only Uniprot, RefSeq_Peptide and Ensembl identifiers are recognized by the webserver.

        The variant column must contain the following information (in this order ): change_position, reference_aminoacid and changed_aminoacid

        **Citation**

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

        Gonzalez-Perez A, Deu-Pons J and Lopez-Bigas N. Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation
        Genome Medicine 2012. 4:89 doi:10.1186/gm390s
    </help>
</tool>