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comparison id_posterior_error_probability.xml @ 2:cf0d72c7b482 draft

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author galaxyp
date Fri, 10 May 2013 17:31:05 -0400
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1 <tool id="openms_id_posterior_error_probability" version="0.1.0" name="ID Posterior Error Probability">
2 <description>
3 Tool to estimate the probability of peptide hits to be incorrectly assigned.
4 </description>
5 <macros>
6 <import>macros.xml</import>
7 </macros>
8 <expand macro="stdio" />
9 <expand macro="requires" />
10 <command interpreter="python">
11 openms_wrapper.py --executable 'IDPosteriorErrorProbability' --config $config
12 </command>
13 <configfiles>
14 <configfile name="config">[simple_options]
15 in=${input1}
16 out=${out}
17 split_charge=${split_charge}
18 top_hits_only=${top_hits_only}
19 </configfile>
20 </configfiles>
21 <inputs>
22 <param format="idxml" name="input1" type="data" label="idXML Input" />
23 <param name="split_charge" type="boolean" label="Split Charge" help="The search engine scores are split by charge if this flag is set. Thus, for each charge state a new model will be computed." checked="false" truevalue="true" falsevalue="false" />
24 <param name="top_hits_only" type="boolean" label="Use Only Top Hits" help="If set only the top hits of every PeptideIdentification will be used" checked="false" truevalue="true" falsevalue="false" />
25 <!-- TODO: Advanced Options -->
26 </inputs>
27 <outputs>
28 <data format="idxml" name="out" />
29 </outputs>
30 <help>
31 **What it does**
32
33 By default an estimation is performed using the (inverse) Gumbel distribution for incorrectly assigned sequences and a Gaussian distribution for correctly assigned sequences. The probabilities are calculated by using Bayes' law, similar to PeptideProphet. Alternatively, a second Gaussian distribution can be used for incorrectly assigned sequences. At the moment, IDPosteriorErrorProbability is able to handle X!Tandem, Mascot, MyriMatch and OMSSA scores.
34
35 No target/decoy information needs to be provided, since the model fits are done on the mixed distribution.
36
37 In order to validate the computed probabilities one can adjust the fit_algorithm subsection.
38
39 **Citation**
40
41 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.``
42
43 If you use this tool in Galaxy, please cite Chilton J, et al. https://bitbucket.org/galaxyp/galaxyp-toolshed-openms
44 </help>
45 </tool>