<tool id="biotransformer" name="BioTransformer" version="1.1.5+galaxy0">
  <requirements>
     <requirement type="package" version="1.1.5">biotransformer</requirement>
     <requirement type="package" version="3.1.1">openbabel</requirement>
     <requirement type="package" version="1.1.1">pandas</requirement>
     <requirement type="package" version="3.7">python</requirement>
  </requirements>
  <command><![CDATA[ python3 '${__tool_directory__}/wrapper_biotransformer.py'
 -k $k
 -b $b
 -s $steps
 -t $tolerance
 -a
 -icsv $input_file
 -ocsv $output_file
 -ocsvDup $output_file2
 -ocsvDup2 $output_file3

  ]]>
  </command>
  <inputs>
    <param name="k" type="select" value="pred" label="The task to be permed [pred=prediction, cid=compound identification].">
       <option value="pred" selected="true" >pred</option>
       <option value="cid">cid</option>
    </param>
    <param name="b" type="select" value="ecbased" label="The type of description.">
       <option value="ecbased" selected="true" >EC-based</option>
       <option value="cyp450">CYP450</option>
       <option value="phaseII">Phase II</option>
       <option value="hgut">Human gut microbial</option>
       <option value="superbio">Human super transformer 1 (superbio)</option>
       <option value="allHuman">Human super transformer 2 (allHuman)</option>
       <option value="envimicro">Environmental microbial</option>
    </param>
    <param name="steps" type="integer" value="1" label=" The number of steps for the prediction."/>
    <param name="tolerance" type="float" value="0.01" label="Mass tolerance for metabolite identification."/>
    <param name="input_file" type="data" format="csv" label="Input CSV file."/>
  </inputs>

  <outputs>
    <data format="csv" name="output_file" label="BioTransformer - output."/>
    <data format="csv" name="output_file2" label="BioTransformer - filtered CSV output."/>
    <data format="csv" name="output_file3" label="BioTransformer - super-filtered CSV output."/>
  </outputs>

  <tests>
    <test>
      <param name="k" value="pred"/>
      <param name="b" value="allHuman"/>
      <param name="steps" value="1"/>
      <param name="tolerance" value="0.01"/>
      <param name="input_file" value="smiles.csv"/>
      <output name="output_file" value="output1.csv"/>
      <output name="output_file2" value="output2.csv"/>
      <output name="output_file3" value="output3.csv"/>
    </test>
  </tests>

  <help>
BioTransformer is a software tool that predicts small molecule metabolism in mammals, their gut micr obiota,
as well as the soil/aquatic microbiota. BioTransformer also assists scientists in metabolite identification,
based on the metabolism prediction.

BioTransformer is offered to the public as a freely acessible software package under the GNU License GPL v2.1.

Users are free to copy and redistribute the material in any medium or format. Moreover, they could modify, and
build upon the material under the condition that they must give appropriate credit, provide links to the license,
and indicate if changes were made. Furthermore, the above copyright notice and this permission notice must be
included. Use and re-distribution of the these resources, in whole or in part, for commercial purposes requires
explicit permission of the authors. We ask that all users of the BioTransformer software tool, the BioTransformer
web server, or BioTransformerDB to cite the BioTransformer reference in any resulting publications, and to
acknowledge the authors.

Parameters explanation:

Input. Currently, only a CSV file with one SMILES per line is accepted.

The type of description: Type of biotransformer - EC-based (ecbased), CYP450 (cyp450), Phase II (phaseII), Human gut
microbial (hgut), human super transformer* (superbio, or allHuman), Environmental microbial (envimicro).

The number of steps for the prediction. This option will be used for the EC-based, CYP450, Phase II, and Environmental
microbial biotransformers. The default value is 1.

Mass tolerance for metabolite identification (default is 0.01).

Output of BioTransformer with CSV as an input are 3 CSV files. One without any filtering, second with filtered
duplicates based on 6 columns (InChI, InChIKey, Synonyms, Molecular formula, Major Isotope Mass, AlogP) and third with
filtered duplicates based on 3 columns (Molecular formula, Major Isotope Mass, AlogP).

(* ) While the 'superbio' option runs a set number of transformation steps in a pre-defined order (e.g. deconjugation
first, then Oxidation/reduction, etc.), the 'allHuman' option predicts all possible metabolites from any applicable
reaction(Oxidation, reduction, (de-)conjugation) at each step.
  </help>
  <citations>
    <citation type="doi">https://doi.org/10.1186/s13321-018-0324-5</citation>
  </citations>
</tool>