annotate feature_linker_unlabeled.xml @ 2:cf0d72c7b482 draft

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<tool id="openms_feature_linker_unlabeled" version="0.1.0" name="Feature Linker (Labeled)">
  <description>
    Groups corresponding features from multiple maps.
  </description>
  <macros>
    <import>macros.xml</import>
  </macros>
  <expand macro="stdio" />
  <expand macro="requires" />
  <command interpreter="python">
    openms_wrapper.py --executable 'FeatureLinkedUnlabeled' --config $config
  </command>
  <configfiles>
    <configfile name="config">[simple_options]
in=$input1
out=$output
</configfile>
  </configfiles>
  <inputs>
    <conditional name="type">
      <param name="input_type" type="select" label="Input Type">
        <option value="featurexml">Features (FeatureXML)</option>
        <option value="consensusxml">Consensus (ConsensusXML)</option>
      </param>
      <when value="featurexml">
        <param format="featurexml" name="input1" type="data" label="Input Features" />
      </when>
      <when value="consensusxml">
        <param format="consensusxml" name="input1" type="data" label="Input Consensus" />
      </when>
    </conditional>
  </inputs>
  <outputs>
    <data format="consensusxml" name="output" />
  </outputs>
  <help>
**What it does**

This tool provides an algorithm for grouping corresponding features in multiple runs of label-free experiments. For more details and algorithm-specific parameters (set in the INI file) see "Detailed Description" in the algorithm documentation or the INI file table below.

FeatureLinkerUnlabeled takes several feature maps (featureXML files) and stores the corresponding features in a consensus map (consensusXML file). Feature maps can be created from MS experiments (peak data) using one of the FeatureFinder TOPP tools.

Advanced users can convert the consensusXML generated by this tool to EDTA using FileConverter and plot the distribution of distances in RT (or m/z) between different input files (can be done in Excel). The distribution should be Gaussian-like with very few points beyond the tails. Points far away from the Gaussian indicate a too wide tolerance. A Gaussian with its left/right tail trimmed indicates a too narrow tolerance.

**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>