view condel_web/condel_web.xml @ 7:67212f958e5a draft default tip

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<tool id="condel_web" name="condel">
    <description>Condel 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">
        condel_web.py --input $input --output $output
    </command>
    <inputs>
        <param name="input" format="text" type="data" label="Input Variants" />
    </inputs>
    <outputs>
        <data name="output" format="tabular"/>
    </outputs>
    <tests>
        <test>
            <param name="input" value="condel_input.tsv"/>
            <output name="output" file="condel_output.csv"/>
        </test>
    </tests>
    <help>
        **What it does**

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

        Condel stands for CONsensus DELeteriousness score of non-synonymous single nucleotide variants (SNVs).
        The idea behind it is to integrate the output of computational tools aimed at assessing the impact of non synonymous SNVs on protein function.
        To do this, it computes a weighted average of the scores (WAS) of these tools : SIFT, Polyphen2, MAPP, LogR Pfam E-value
        (implemented ad hoc following the instructions at Clifford RJ, Edmonson MN, Nguyen C, and Buetow KH (2004)
        Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms. Bioinformatics 20, 1006-1014) and MutationAssessor

        **How does it work**

        The scores of different methods are weighted using the complementary cumulative distributions produced by the five methods on a
        dataset of approximately 20000 missense SNPs, both deleterious and neutral. The probability that a predicted deleterious mutation is not a
        false positive of the method and the probability that a predicted neutral mutation is not a false negative are employed as weights.

        **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

        Ex:

        EGFR_HUMAN R521K

        EGFR_HUMAN R98Q

        .. class:: warningmark

        Note

        Please, note that the Variant Effect Predictor assumes that the allele submitted is coded in the forward strand.

        If you are assessing the effect of variants coded in the reverse strand, please introduce the complementary nucleotide instead.

        **Citation**


        If you use this tool please cite:


        Improving the Assessment of the Outcome of Nonsynonymous SNVs with a Consensus Deleteriousness Score, Condel (2011) Abel González-Pérez and Nuria López-Bigas, American Journal of Human Genetics 10.1016/j.ajhg.2011.03.004


    </help>
</tool>