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1 <tool id="openms_consensus_id" version="0.1.0" name="Consensus ID">
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2 <description>
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3 Computes a consensus identification from peptide identification engines.
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4 </description>
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5 <macros>
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6 <import>macros.xml</import>
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7 </macros>
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8 <expand macro="stdio" />
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9 <expand macro="requires" />
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10 <command interpreter="python">
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11 openms_wrapper.py --executable 'ConsensusID' --config $config
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12 </command>
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13 <configfiles>
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14 <configfile name="config">[simple_options]
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15 in=${input1}
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16 out=${out}
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17 </configfile>
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18 </configfiles>
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19 <inputs>
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20 <param format="idxml,consensusxml,featurexml" name="input1" type="data" label="idXML Input" />
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21 <!-- Add options -->
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22 </inputs>
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23 <outputs>
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24 <data format="input" name="out" metadata_source="input1" />
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25 </outputs>
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26 <help>
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27 **What it does**
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28
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29 This implementation (for PEPMatrix and PEPIons) is described in
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30
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31 Nahnsen S, Bertsch A, Rahnenfuehrer J, Nordheim A, Kohlbacher O
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32 Probabilistic Consensus Scoring Improves Tandem Mass Spectrometry Peptide Identification
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33 Journal of Proteome Research (2011), DOI: 10.1021/pr2002879
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34 The input file can contain several searches, e.g., from several identification engines. In order to use the PEPMatrix or the PEPIons algorithm, posterior error probabilities (PEPs) need to be calculated using the IDPosteriorErrorProbability tool for all individual search engines. After PEP calculation, the different search engine results have to be combined using IDMerger. Identification runs can be mapped to featureXML and consensusXML with the IDMapper tool. The merged file can now be fed into into the ConsensusID tool. For the statistical assessment of the results it is recommended to use target-decoy databases for peptide identifications. The false discovery rates (FDRs) can be calculated using the FalseDiscoveryRate tool.
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35
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36 **Citation**
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37
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38 For the underlying tool, please cite ``Marc Sturm, Andreas Bertsch, Clemens Gröpl, Andreas Hildebrandt, Rene Hussong, Eva Lange, Nico Pfeifer, Ole Schulz-Trieglaff, Alexandra Zerck, Knut Reinert, and Oliver Kohlbacher, 2008. OpenMS – an Open-Source Software Framework for Mass Spectrometry. BMC Bioinformatics 9: 163. doi:10.1186/1471-2105-9-163.``
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39
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40 If you use this tool in Galaxy, please cite Chilton J, et al. https://bitbucket.org/galaxyp/galaxyp-toolshed-openms
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41 </help>
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42 </tool>
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