<tool id="decontaminator" name="decontaminator" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05">
    <description>Decontaminator is a deep learning helping tool that filters out phage or fungi contigs from plant virome RNAseq assemblies</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <xrefs>
        <xref type="bio.tools">decontaminator</xref>
    </xrefs>
    <expand macro="requirements"/>
    <command detect_errors="exit_code"><![CDATA[
    mkdir -p '${predicted_fragments.extra_files_path}' &&
    python '$__tool_directory__/predict.py'
        --test_ds '${fasta_file}'
        --weights '${weights.fields.path}'
        --out_path '${predicted_fragments.extra_files_path}'
        --return_viral True
    && cp '${predicted_fragments.extra_files_path}'/predicted_fragments.tsv predicted_fragments.tsv
    && cp '${predicted_fragments.extra_files_path}'/predicted.tsv predicted.tsv
    && cp '${predicted_fragments.extra_files_path}'/viral.fasta viral.fasta

    ]]></command>
    <inputs>
        <param name="fasta_file" type="data" format="fasta" label="DNA FASTA file"/>
        <param name="weights" type="select" label="Select a reference model" help="If your model of interest is not listed, contact the Galaxy team">
            <options from_data_table="decontaminator_models">
                <validator type="no_options" message="No models are available for the selected input dataset" />
            </options>
        </param>
    </inputs>
    <outputs>
        <data format="tabular" name="predicted_fragments" from_work_dir="predicted_fragments.tsv" label="${tool.name} on ${on_string}: predicted fragments"/>
        <data format="tabular" name="predicted" from_work_dir="predicted.tsv" label="${tool.name} on ${on_string}: predicted "/>
        <data format="fasta" name="viral" from_work_dir="viral.fasta" label="${tool.name} on ${on_string}: viral FASTA file" />
    </outputs>
    <tests>
        <test>
            <param name="fasta_file" value="viruses.fasta"/>
            <param name="weights" value="test"/>
            <output name="predicted_fragments" file="predicted_fragments.tsv"  ftype="tabular" lines_diff="2"/>
            <output name="predicted" file="predicted.tsv"  ftype="tabular" lines_diff="2"/>
            <output name="viral" file="viral.fasta"  ftype="fasta" lines_diff="2"/>
        </test>
    </tests>
    <help>
    <![CDATA[
    Decontaminator is a deep learning method that uses Convolutional Neural Networks (CNNs) and a Random Forest Classifier to identify viruses in
    sequening datasets. More precisely, VirHunter classifies previously assembled contigs as viral, host and bacterial (contamination).
 ]]></help>
</tool>