<tool id="pyprophet_subsample" name="PyProphet subsample" version="@VERSION@.0">
    <description>
    Subsample OpenSWATH file
    </description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements"/>
    <command detect_errors="aggressive">
    <![CDATA[
        pyprophet subsample
        --in='$input'
        --subsample_ratio=$subsample_ratio
        $test_mode
        --out='./output.osw'

    ]]>
    </command>
    <inputs>
        <param name="input" type="data" format="osw" label="Input file" help="This file needs to be in OSW format." />
        <param argument="--subsample_ratio" type="float" value="1" label="Subsample ratio used per input file (between 0 (0 %) and 1 (100 %))" />
        <param name="test_mode" type="boolean" truevalue="--test" falsevalue="--no-test" label="Run in test mode with fixed seed" />
    </inputs>
    <outputs>
        <data name="output" format="osw" label="${tool.name} on ${on_string}: subsample.osw" from_work_dir="output.osw" />
    </outputs>
    <tests>
        <test>
            <param name="input" value="merged.osw" ftype="osw" />
            <param name="subsample_ratio" value="0.5" />
            <param name="test_mode" value="True" />
            <output name="output" file="subsample.tabular" />
        </test>
    </tests>
    <help>
<![CDATA[
**What it does**

PyProphet: Semi-supervised learning and scoring of OpenSWATH results.

Subsample OpenSWATH file to minimum for integrated scoring.

PyProphet is a Python re-implementation of the mProphet algorithm (Reiter 2010 Nature Methods) optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in (Telemann 2014 Bioinformatics) and has since been extended to support new data types and analysis modes (Rosenberger 2017, Nature biotechnology and Nature methods).

For more information, visit @link@

]]>
    </help>
    <expand macro="citations"/>
</tool>