task CNVkit {
  
  String sample
  File ref_dir
  String fasta
  File ref_flat
  File regions
  File hrd
  File tumor_bam
  File tumor_bam_index
  File? normal_bam
  File? normal_bam_index
  String docker
  String cluster_config
  String disk_size
  
  command <<<
    set -o pipefail
    set -e
    nt=$(nproc)
    
    cnvkit.py access ${ref_dir}/${fasta} -o access.bed
    # Prepare the target bed
    cnvkit.py target ${regions} --annotate ${ref_flat} --split --short-names -o my_baits.bed
    if [ ${normal_bam} ]; then
      cnvkit.py autobin ${tumor_bam} ${normal_bam} -t my_baits.bed -g access.bed
    else
      cnvkit.py autobin ${tumor_bam} -t my_baits.bed -g access.bed
    fi
    
    # For each sample...
    cnvkit.py coverage ${tumor_bam} my_baits.target.bed -o ${sample}.T.targetcoverage.cnn
    cnvkit.py coverage ${tumor_bam} my_baits.antitarget.bed -o ${sample}.T.antitargetcoverage.cnn
    if [ ${normal_bam} ]; then
      cnvkit.py coverage ${normal_bam} my_baits.target.bed -o ${sample}.N.targetcoverage.cnn
      cnvkit.py coverage ${normal_bam} my_baits.antitarget.bed -o ${sample}.N.antitargetcoverage.cnn
      # With paired or pooled normals
      cnvkit.py reference *.N.{,anti}targetcoverage.cnn --fasta ${ref_dir}/${fasta} -o reference.cnn
    else
      # With no control sample
      cnvkit.py reference -o reference.cnn -f ${ref_dir}/${fasta} -t my_baits.target.bed -a my_baits.antitarget.bed
    fi
    
    # For each tumor sample...
    cnvkit.py fix ${sample}.T.targetcoverage.cnn ${sample}.T.antitargetcoverage.cnn reference.cnn -o ${sample}.cnr
    cnvkit.py segment ${sample}.cnr -o ${sample}.cns
    
    # Check noise
    cnvkit.py metrics ${sample}.cnr -s ${sample}.cns > ${sample}.stats
    
    # Derive each segment's absolute integer copy number, ploidy must be int value
    PURITY=`awk -F'\t' '{print $6}' ${hrd} | sed -n '2p'`
    cnvkit.py call ${sample}.cns -y -m clonal --purity $PURITY -o ${sample}.call.cns
    cnvkit.py call ${sample}.cnr -y -m clonal --purity $PURITY -o ${sample}.call.cnr

    # Plot the results
    cnvkit.py scatter ${sample}.cnr -s ${sample}.call.cns -o ${sample}.scatter.pdf
    cnvkit.py diagram ${sample}.cnr -s ${sample}.call.cns -o ${sample}.diagram.pdf
    cnvkit.py heatmap ${sample}.cnr ${sample}.call.cns -o ${sample}.heatmap.pdf
    
    # Genemetrics
    mkdir gainloss
    cnvkit.py genemetrics ${sample}.call.cnr -t 0.2 -m 3 -o ${sample}.ratio_cnv.txt
    cnvkit.py genemetrics ${sample}.call.cnr -s ${sample}.call.cns -t 0.2 -m 3 -o ${sample}.segment_cnv.txt
    # Filter genes
    cat ${sample}.ratio_cnv.txt | tail -n+2 | cut -f1 | sort | uniq > ratio_cnv.txt
    cat ${sample}.segment_cnv.txt | tail -n+2 | cut -f1 | sort | uniq > segment_cnv.txt
    comm -12 ratio_cnv.txt segment_cnv.txt > ${sample}.trusted_genes.txt
    for gene in `cat ${sample}.trusted_genes.txt`
    do
      cnvkit.py scatter ${sample}.call.cnr -s ${sample}.call.cns -g $gene -o ./gainloss/${sample}.$gene.scatter.pdf
    done
  >>>
  
  runtime {
    docker: docker
    cluster: cluster_config
    systemDisk: "cloud_ssd 40"
    dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
  }
  
  output {
    File scatter_pdf = "${sample}.scatter.pdf"
    File diagram_pdf = "${sample}.diagram.pdf"
    File heatmap_pdf = "${sample}.heatmap.pdf"
    File cnr = "${sample}.cnr"
    File cns = "${sample}.cns"
    File stats = "${sample}.stats"
    File call_cnr = "${sample}.call.cnr"
    File call_cns = "${sample}.call.cns"
    File ratio_cnv = "${sample}.ratio_cnv.txt"
    File segment_cnv = "${sample}.segment_cnv.txt"
    File gainloss_genes = "${sample}.trusted_genes.txt"
    Array[File] gainloss = glob("./gainloss/*")
  }
}