annotate id_posterior_error_probability.xml @ 6:42b843627623 draft default tip

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<tool id="openms_id_posterior_error_probability" version="0.1.0" name="ID Posterior Error Probability">
  <description>
    Tool to estimate the probability of peptide hits to be incorrectly assigned.
  </description>
  <macros>
    <import>macros.xml</import>
  </macros>
  <expand macro="stdio" />
  <expand macro="requires" />
  <command interpreter="python">
    openms_wrapper.py --executable 'IDPosteriorErrorProbability' --config $config
  </command>
  <configfiles>
    <configfile name="config">[simple_options]
in=${input1}
out=${out}
split_charge=${split_charge}
top_hits_only=${top_hits_only}
</configfile>
  </configfiles>
  <inputs>
    <param format="idxml" name="input1" type="data" label="idXML Input" />
    <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" />
    <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" />
    <!-- TODO: Advanced Options -->
  </inputs>
  <outputs>
    <data format="idxml" name="out" />
  </outputs>
  <help>
**What it does**

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.

No target/decoy information needs to be provided, since the model fits are done on the mixed distribution.

In order to validate the computed probabilities one can adjust the fit_algorithm subsection.

**Citation**

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.``

If you use this tool in Galaxy, please cite Chilton J, et al. https://bitbucket.org/galaxyp/galaxyp-toolshed-openms
  </help>
</tool>