<?xml version="1.0" encoding="UTF-8"?>
<tool id="met4j_ExtractSubNetwork" name="ExtractSubNetwork" version="MET4J_VERSION_TEST">
  <description>Create a subnetwork from a GSMN in SBML format, and two files containing lists of compounds of interests ids, one per row.</description>
  <xrefs>
    <xref type="bio.tools">met4j</xref>
  </xrefs>
  <requirements>
    <container type="singularity">oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:MET4J_VERSION_TEST</container>
  </requirements>
  <command detect_errors="exit_code"><![CDATA[sh /usr/bin/met4j.sh networkAnalysis.ExtractSubNetwork -i "$inputPath"
 -s "$sourcePath"
 -t "$targetPath"
#if str($sideCompoundFile) != 'None':
 -sc "$sideCompoundFile"
#end if
 $degree
#if str($weightFile) != 'None':
 -cw "$weightFile"
#end if
 $chemicalSim
 $undirected
 $asTable
#if str($k):
 -k "$k"
#end if
 $st
 -o "$outputPath"
]]></command>
  <inputs>
    <param argument="-i" format="sbml" label="input SBML file" name="inputPath" optional="false" type="data" value=""/>
    <param argument="-s" format="txt" label="input sources txt file" name="sourcePath" optional="false" type="data" value=""/>
    <param argument="-t" format="txt" label="input targets txt file" name="targetPath" optional="false" type="data" value=""/>
    <param argument="-sc" format="txt" label="an optional file containing list of side compounds to ignore" name="sideCompoundFile" optional="true" type="data" value=""/>
    <param argument="-dw" checked="false" falsevalue="" label="penalize traversal of hubs by using degree square weighting" name="degree" truevalue="-dw" type="boolean" value="false"/>
    <param argument="-cw" format="tsv" label="an optional file containing weights for compound pairs" name="weightFile" optional="true" type="data" value=""/>
    <param argument="-sw" checked="false" falsevalue="" label="penalize traversal of non-relevant edges by using chemical similarity weighting" name="chemicalSim" truevalue="-sw" type="boolean" value="false"/>
    <param argument="-u" checked="false" falsevalue="" label="Ignore reaction direction" name="undirected" truevalue="-u" type="boolean" value="false"/>
    <param argument="-tab" checked="false" falsevalue="" label="Export in tabulated file instead of .GML" name="asTable" truevalue="-tab" type="boolean" value="false"/>
    <param argument="-k" label="Extract k-shortest paths" name="k" optional="true" type="text" value="1">
      <sanitizer invalid_char="_">
        <valid initial="string.printable"/>
      </sanitizer>
    </param>
    <param argument="-st" checked="false" falsevalue="" label="Extract Steiner Tree" name="st" truevalue="-st" type="boolean" value="false"/>
  </inputs>
  <outputs>
    <data format="gml" name="outputPath"/>
  </outputs>
  <tests>
    <test>
      <param name="inputPath" value="toy_model.xml"/>
      <param name="sourcePath" value="seeds.txt"/>
      <param name="targetPath" value="targets.txt"/>
      <output ftype="gml" name="outputPath">
        <assert_contents>
          <has_line_matching expression=".*node.*" n="3"/>
          <has_line_matching expression=".*edge.*" n="2"/>
        </assert_contents>
      </output>
    </test>
  </tests>
  <help><![CDATA[Create a subnetwork from a GSMN in SBML format, and two files containing lists of compounds of interests ids, one per row.
The subnetwork correspond to part of the network that connects compounds from the first list to compounds from the second list.
Sources and targets list can have elements in common. The connecting part can be defined as the union of shortest or k-shortest paths between sources and targets, or the Steiner tree connecting them. The relevance of considered path can be increased by weighting the edges using degree squared, chemical similarity (require InChI or SMILES annotations) or any provided weighting.

See previous works on subnetwork extraction for parameters recommendations:Frainay, C., & Jourdan, F. Computational methods to identify metabolic sub-networks based on metabolomic profiles. Bioinformatics 2016;1–14. https://doi.org/10.1093/bib/bbv115
Faust, K., Croes, D., & van Helden, J. Prediction of metabolic pathways from genome-scale metabolic networks. Bio Systems 2011;105(2), 109–121. https://doi.org/10.1016/j.biosystems.2011.05.004
Croes D, Couche F, Wodak SJ, et al. Metabolic PathFinding: inferring relevant pathways in biochemical networks. Nucleic Acids Res 2005;33:W326–30.
Croes D, Couche F, Wodak SJ, et al. Inferring meaningful pathways in weighted metabolic networks. J Mol Biol 2006; 356:222–36.
Rahman SA, Advani P, Schunk R, et al. Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC). Bioinformatics 2005;21:1189–93.
Pertusi DA, Stine AE, Broadbelt LJ, et al. Efficient searching and annotation of metabolic networks using chemical similarity. Bioinformatics 2014;1–9.
McShan DC, Rao S, Shah I. PathMiner: predicting metabolic pathways by heuristic search. Bioinformatics 2003;19:1692–8.
]]></help>
  <citations/>
</tool>