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+<macros>
+    <token name="@TOOL_VERSION@">0.5.4</token>
+    <xml name="requirements">
+        <requirements>
+            <container type="docker">recetox/retip:@TOOL_VERSION@-recetox0</container>
+        </requirements>
+    </xml>
+    <xml name="citations">
+        <citations>
+            <citation type="doi">https://doi.org/10.1021/acs.analchem.9b05765</citation>
+        </citations>
+    </xml>
+    <token name="@HELP@"><![CDATA[
+Retip is an R package for predicting Retention Time (RT) for small molecules in a high pressure liquid
+chromatography (HPLC) Mass Spectrometry analysis. Retention time calculation can be useful in identifying
+unknowns and removing false positive annotations. It uses five different machine learning algorithms to built a
+stable, accurate and fast RT prediction model:
+
+- Random Forest: a decision tree algorithms
+- BRNN: Bayesian Regularized Neural Network
+- XGBoost: an extreme Gradient Boosting for tree algorithms
+- lightGBM: a gradient boosting framework that uses tree based learning algorithms.
+- Keras: a high-level neural networks API for Tensorflow
+
+Retip also includes useful biochemical databases like: BMDB, ChEBI, DrugBank, ECMDB, FooDB, HMDB, KNApSAcK,
+PlantCyc, SMPDB, T3DB, UNPD, YMDB and STOFF.
+
+**Get started**
+
+To use Retip, a user needs to prepare a compound retention time library. The input file
+needs compound Name, InChiKey, SMILES code and experimental retention time information for each compound.
+The input must be a CSV file. Retip will use this input file to build a the model and will predict
+retention times for other biochemical databases or an input query list of compounds. It is suggested that
+the file has at least 300 compounds to build a good retention time prediction model.
+    ]]>
+    </token>
+</macros>