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hangsuUNChangsuUNC
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‎wdl/Himito_build.wdl‎

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version 1.0
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workflow Himito_build {
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input {
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File fasta
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File reference_fa
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String prefix
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String reference_header
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String sampleid
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String data_type
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Int kmer_size
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}
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call Build {
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input:
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bam = fasta,
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reference = reference_fa,
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prefix = prefix,
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kmer_size = kmer_size,
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sampleid = sampleid
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}
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output {
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File graph = Build.graph
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}
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}
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task Build {
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input {
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File bam
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File reference
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String prefix
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String sampleid
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Int kmer_size
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}
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command <<<
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set -euxo pipefail
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if [[ ~{bam} == *.gz ]]; then
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gunzip -c ~{bam} > ~{prefix}.fasta
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/Himito/target/release/Himito build -k ~{kmer_size} -r ~{reference} -i ~{prefix}.fasta -o ~{sampleid}.~{prefix}.gfa
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else
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/Himito/target/release/Himito build -k ~{kmer_size} -r ~{reference} -i ~{bam} -o ~{sampleid}.~{prefix}.gfa
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fi
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>>>
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output {
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File graph = "~{sampleid}.~{prefix}.gfa"
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}
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runtime {
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docker: "us.gcr.io/broad-dsp-lrma/hangsuunc/himito:v1"
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memory: "2 GB"
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cpu: 1
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disks: "local-disk 10 SSD"
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}
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}

‎wdl/Himito_filter_methylation.wdl‎

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version 1.0
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workflow Himito_call {
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input {
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File whole_genome_bam
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File whole_genome_bai
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File reference_fa
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File reference_fai
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File truth_vcf
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File truth_tbi
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String prefix
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String sampleid
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String data_type
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Array[Float] max_methylation_threshold
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Int kmer_size
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}
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scatter (t in max_methylation_threshold) {
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call Filter {
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input:
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bam = whole_genome_bam,
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bai = whole_genome_bai,
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prefix = prefix + "_" + t,
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threshold = t
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}
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call Build {
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input:
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bam = Filter.mt_bam,
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reference = reference_fa,
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prefix = prefix + "_" + t,
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kmer_size = kmer_size,
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sampleid = sampleid
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}
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call Call {
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input:
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graph_gfa = Build.graph,
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reference = reference_fa,
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prefix = prefix + "_" + t,
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kmer_size = kmer_size,
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sampleid=sampleid,
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data_type = data_type
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}
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call VCFEval as Himito_Eval {
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input:
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query_vcf = Call.vcf,
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reference_fa = reference_fa,
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reference_fai = reference_fai,
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base_vcf = truth_vcf,
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base_vcf_index = truth_tbi,
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query_output_sample_name = sampleid + "_" + t + "_Himito",
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}
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}
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output {
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Array[File] vcf_file = Call.vcf
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Array[File] Himito_summary_file = Himito_Eval.summary_statistics
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}
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}
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struct RuntimeAttr {
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Float? mem_gb
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Int? cpu_cores
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Int? disk_gb
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Int? boot_disk_gb
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Int? preemptible_tries
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Int? max_retries
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String? docker
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}
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struct RuntimeAttributes {
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Int disk_size
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Int cpu
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Int memory
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}
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task Filter {
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input {
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File bam
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File bai
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String prefix
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Float threshold
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}
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command <<<
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set -euxo pipefail
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/Himito/target/release/Himito filter -i ~{bam} -c chrM -m ~{prefix}_mt.bam -n ~{prefix}_numts.bam -f ~{threshold}
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>>>
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output {
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File mt_bam = "~{prefix}_mt.bam"
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File numts_bam = "~{prefix}_numts.bam"
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}
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runtime {
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docker: "us.gcr.io/broad-dsp-lrma/hangsuunc/himito:v1"
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memory: "1 GB"
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cpu: 1
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disks: "local-disk 300 SSD"
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}
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}
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task Build {
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input {
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File bam
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File reference
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String prefix
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String sampleid
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Int kmer_size
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}
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command <<<
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set -euxo pipefail
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/Himito/target/release/Himito build -k ~{kmer_size} -r ~{reference} -i ~{bam} -o ~{sampleid}.~{prefix}.gfa
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>>>
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output {
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File graph = "~{sampleid}.~{prefix}.gfa"
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}
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runtime {
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docker: "us.gcr.io/broad-dsp-lrma/hangsuunc/himito:v1"
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memory: "2 GB"
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cpu: 1
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disks: "local-disk 10 SSD"
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}
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}
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task Call {
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input {
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File graph_gfa
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File reference
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String prefix
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String sampleid
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String data_type
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Int kmer_size
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}
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command <<<
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set -euxo pipefail
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/Himito/target/release/Himito call -g ~{graph_gfa} -r ~{reference} -k ~{kmer_size} -d ~{data_type} -s ~{sampleid} -o ~{sampleid}.~{prefix}.vcf
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>>>
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output {
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# File graph = "~{prefix}.annotated.gfa"
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File vcf = "~{sampleid}.~{prefix}.vcf"
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}
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runtime {
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docker: "us.gcr.io/broad-dsp-lrma/hangsuunc/himito:v1"
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memory: "2 GB"
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cpu: 1
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disks: "local-disk 10 SSD"
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}
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}
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task VCFEval {
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input {
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# Input VCF Files
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File query_vcf
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File reference_fa
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File reference_fai
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File base_vcf
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File base_vcf_index
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String query_output_sample_name
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# Runtime params
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Int? preemptible
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RuntimeAttributes runtimeAttributes = {"disk_size": ceil(2 * size(query_vcf, "GB") + 2 * size(base_vcf, "GB") + size(reference_fa, "GB")) + 50,
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"cpu": 8, "memory": 16}
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}
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command <<<
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set -xeuo pipefail
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# Compress and Index vcf files
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bcftools view ~{query_vcf} -O z -o ~{query_output_sample_name}.vcf.gz
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bcftools index -t ~{query_output_sample_name}.vcf.gz
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# split multiallelic sites in the base_vcf
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bcftools norm \
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-f ~{reference_fa} \
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-m -both ~{base_vcf} \
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-O z \
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-o ~{query_output_sample_name}.base.normed.vcf.gz
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bcftools index -t ~{query_output_sample_name}.base.normed.vcf.gz
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# rtg vcfeval
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rtg format -o rtg_ref ~{reference_fa}
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rtg vcfeval \
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-b ~{query_output_sample_name}.base.normed.vcf.gz \
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-c ~{query_output_sample_name}.vcf.gz \
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-o reg \
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-t rtg_ref \
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--squash-ploidy \
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--sample ALT,ALT
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mkdir output_dir
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cp reg/summary.txt output_dir/~{query_output_sample_name}_summary.txt
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>>>
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runtime {
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docker: "us.gcr.io/broad-dsde-methods/vcfeval_docker:v1.1-tmp"
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preemptible: select_first([preemptible, 0])
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disks: "local-disk " + runtimeAttributes.disk_size + " HDD"
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cpu: runtimeAttributes.cpu
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memory: runtimeAttributes.memory + " GB"
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}
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output {
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File summary_statistics = "output_dir/~{query_output_sample_name}_summary.txt"
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}
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}

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