annotate pyprophet_merge.xml @ 2:a67b508b1dc5 draft default tip

"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/pyprophet commit d1c34e31a93761cf7cfd4068bcdb70495d4d90bb"

<tool id="pyprophet_merge" name="PyProphet merge" version="@VERSION@.0">
    <description>
    Merge multiple osw files
    </description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements"/>
    <command detect_errors="aggressive">
    <![CDATA[
        pyprophet merge
        --template='$template'
        $same_run
        --out='./output.osw'

        #echo ' '.join(["'%s'" % $input for $input in $inputs])#
    ]]>
    </command>
    <inputs>
        <param name="inputs" type="data" format="osw" multiple="true" label="Input file" help="This file needs to be in OSW format, an SQLite variant." />
        <param argument="--template" type="data" format="sqlite" label="Template osw file" />
        <param name="same_run" type="boolean" truevalue="--same_run" falsevalue="--no-same_run"
            label="Input files are from same run (deletes run information)" help="(--same_run/--no-same_run)"/>
    </inputs>
    <outputs>
        <data name="output" format="osw" label="${tool.name} on ${on_string}: merged.osw" from_work_dir="output.osw" />
    </outputs>
    <tests>
        <test>
            <param name="inputs" value="open_swath_output1.osw,open_swath_output2.osw" ftype="osw"/>
            <param name="template_file" ftype="pqp" value="patient_specific_OSW_optimized_decoys.pqp"/>
            <param name="same_run" value="False"/>
            <output name="output" file="merged.osw" compare="sim_size" />
        </test>
    </tests>
    <help>
<![CDATA[
**What it does**

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

Merge function of PyProphet: Takes several osw input files and merges them. For large experiments, it is recommended to subsample first.

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>