--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/feature_linker_unlabeled.xml	Fri May 10 17:31:05 2013 -0400
@@ -0,0 +1,51 @@
+<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>