<macros>
    <token name="@TOOL_VERSION@">0.5.4</token>
    <xml name="requirements">
        <requirements>
            <container type="docker">recetox/retip:@TOOL_VERSION@-recetox4</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>