comparison msstats.xml @ 6:b7034eff0db1 draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msstats commit 86de2cd327423c44d042f98108dc93a8f83982d0

5:28434abe6c5c

<tool id="msstats" name="MSstats" version="@VERSION@.0">
    <description>statistical relative protein significance analysis in DDA, SRM and DIA Mass Spectrometry</description>
    <macros>
        <token name="@VERSION@">4.0.0</token>
        <xml name="useUniquePeptide">
            <param name="useUniquePeptide" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Remove peptides that are assigned for more than one proteins"/>
        </xml>
        <xml name="summaryforMultipleRows">
            <param name="summaryforMultipleRows" type="select" label="Summary for MultipleRows" help="When there are multiple measurements for certain feature and certain run, use highest or sum of all">

            <param name="removeProtein_with1Peptide" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="false" label="Remove the proteins which have only 1 peptide and charge"/>
        </xml>
        
    </macros>
    <requirements>
        <requirement type="package" version="@VERSION@">bioconductor-msstats</requirement>
    </requirements>
    <command detect_errors="exit_code"><![CDATA[
        cat '$msstats_script' > '$r_script' &&
        Rscript '$msstats_script'
    ]]></command>

    </tests>
    <help><![CDATA[
MSstats is an open-source R package for statistical relative quantification of proteins and peptides in global, targeted and data-independent proteomics. `More information on MSstats <http://msstats.org/>`_

The MSstats Galaxy tool (version @VERSION@) allows the detection of differentially abundant proteins for label-free MS experiments with complex designs on data derived from open-source proteomics software available in Galaxy (e.g. MaxQuant, OpenMS, OpenSWATH). Processing functionalities such as log transformation, normalization, feature selection, missing value imputation and quantification are available as well. 

-----

**Input data**

    ]]></help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btu305</citation>
    </citations>
</tool>

6:b7034eff0db1

<tool id="msstats" name="MSstats" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@">
    <description>statistical relative protein significance analysis in DDA, SRM and DIA Mass Spectrometry</description>
    <macros>
        <token name="@TOOL_VERSION@">4.0.0</token>
        <token name="@VERSION_SUFFIX@">1</token>
        <xml name="useUniquePeptide">
            <param name="useUniquePeptide" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Remove peptides that are assigned for more than one proteins"/>
        </xml>
        <xml name="summaryforMultipleRows">
            <param name="summaryforMultipleRows" type="select" label="Summary for MultipleRows" help="When there are multiple measurements for certain feature and certain run, use highest or sum of all">

            <param name="removeProtein_with1Peptide" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="false" label="Remove the proteins which have only 1 peptide and charge"/>
        </xml>
        
    </macros>
    <requirements>
        <requirement type="package" version="@TOOL_VERSION@">bioconductor-msstats</requirement>
    </requirements>
    <command detect_errors="exit_code"><![CDATA[
        cat '$msstats_script' > '$r_script' &&
        Rscript '$msstats_script'
    ]]></command>

    </tests>
    <help><![CDATA[
MSstats is an open-source R package for statistical relative quantification of proteins and peptides in global, targeted and data-independent proteomics. `More information on MSstats <http://msstats.org/>`_

The MSstats Galaxy tool (version @TOOL_VERSION@) allows the detection of differentially abundant proteins for label-free MS experiments with complex designs on data derived from open-source proteomics software available in Galaxy (e.g. MaxQuant, OpenMS, OpenSWATH). Processing functionalities such as log transformation, normalization, feature selection, missing value imputation and quantification are available as well.

-----

**Input data**

    ]]></help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btu305</citation>
        <citation type="doi">10.1021/acs.jproteome.2c00051</citation>
    </citations>
</tool>