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1 <tool id="openms_protein_quantifier" version="0.1.0" name="Protein Quantifier">
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2 <description>
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3 Compute peptide and protein abundances from annotated feature/consensus maps.
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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 'ProteinQuantifier' --config $config
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12 </command>
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13 <configfiles>
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14 <configfile name="config">[simple_options]
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15 in=$map_input
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16 #if $protein_prophet.use
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17 protxml=$protxml
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18 #end if
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19 out=$protein_output
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20 peptide_out=$peptide_output
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21 top=$top
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22 format!quoting=none
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23 </configfile>
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24 </configfiles>
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25 <inputs>
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26 <expand macro="map_input" />
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27 <conditional name="protein_prophet">
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28 <param name="use" label="Use ProteinProphet Results (converted to idXML)" help="For information about indistinguishable proteins." type="boolean" />
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29 <when value="false">
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30 </when>
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31 <when value="true">
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32 <param name="protxml" label="Protein/Peptide Identifications Input" type="data" format="idxml" />
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33 </when>
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34 </conditional>
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35 <param name="top" label="Top" value="0" type="integer" />
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36 </inputs>
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37 <outputs>
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38 <data format="tabular" name="protein_output" />
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39 <data format="tabular" name="peptide_output" />
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40 </outputs>
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41 <help>
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42 **What it does**
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43
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44 Quantification is based on the intensity values of the features in the input. Feature intensities are first accumulated to peptide abundances, according to the peptide identifications annotated to the features/feature groups. Then, abundances of the peptides of a protein are averaged to compute the protein abundance.
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45
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46 The peptide-to-protein step uses the (e.g. 3) most abundant proteotypic peptides per protein to compute the protein abundances. This is a general version of the "top 3 approach" (but only for relative quantification) described in:
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47 Silva et al.: Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition (Mol. Cell. Proteomics, 2006, PMID: 16219938).
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48
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49 Only features/feature groups with unambiguous peptide annotation are used for peptide quantification, and generally only proteotypic peptides (i.e. those matching to exactly one protein) are used for protein quantification. As an exception to this rule, if ProteinProphet results for the whole sample set are provided with the protxml option, or are already included in a featureXML input, also groups of indistinguishable proteins will be quantified. The reported quantity then refers to the total for the whole group.
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50
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51 Peptide/protein IDs from multiple identification runs can be handled, but will not be differentiated (i.e. protein accessions for a peptide will be accumulated over all identification runs).
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52
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53 Peptides with the same sequence, but with different modifications are quantified separately on the peptide level, but treated as one peptide for the protein quantification (i.e. the contributions of differently-modified variants of the same peptide are accumulated).
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54
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55 **Citation**
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56
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57 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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58 and ``Weisser et al.: An automated pipeline for high-throughput label-free quantitative proteomics (J. Proteome Res., 2013, PMID: 23391308). doi:10.1021/pr300992u``
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59
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60 If you use this tool in Galaxy, please cite Chilton J, et al. https://bitbucket.org/galaxyp/galaxyp-toolshed-openms
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61 </help>
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62 </tool>
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