bio-workflow-management-wdl-workflows

Basic Task Definition

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Install skill "bio-workflow-management-wdl-workflows" with this command: npx skills add gptomics/bioskills/gptomics-bioskills-bio-workflow-management-wdl-workflows

WDL Workflows

Basic Task Definition

version 1.0

task fastqc { input { File fastq Int threads = 2 }

command <<<
    fastqc -t ~{threads} ~{fastq}
>>>

output {
    File html = glob("*_fastqc.html")[0]
    File zip = glob("*_fastqc.zip")[0]
}

runtime {
    docker: "biocontainers/fastqc:v0.11.9"
    cpu: threads
    memory: "4 GB"
}

}

Simple Workflow

version 1.0

workflow rnaseq { input { File fastq_1 File fastq_2 File salmon_index }

call fastp {
    input:
        reads_1 = fastq_1,
        reads_2 = fastq_2
}

call salmon_quant {
    input:
        reads_1 = fastp.trimmed_1,
        reads_2 = fastp.trimmed_2,
        index = salmon_index
}

output {
    File quant_sf = salmon_quant.quant_file
}

}

Task with All Sections

version 1.0

task bwa_mem { input { File reference File reference_index File reads_1 File reads_2 String sample_id Int threads = 8 }

Int disk_size = ceil(size(reference, "GB") + size(reads_1, "GB") * 3) + 20

command <<<
    bwa mem -t ~{threads} -R "@RG\tID:~{sample_id}\tSM:~{sample_id}" \
        ~{reference} ~{reads_1} ~{reads_2} | \
        samtools sort -@ ~{threads} -o ~{sample_id}.sorted.bam
    samtools index ~{sample_id}.sorted.bam
>>>

output {
    File bam = "~{sample_id}.sorted.bam"
    File bai = "~{sample_id}.sorted.bam.bai"
}

runtime {
    docker: "biocontainers/bwa:v0.7.17"
    cpu: threads
    memory: "16 GB"
    disks: "local-disk " + disk_size + " HDD"
}

}

Scatter (Parallel Execution)

version 1.0

workflow process_samples { input { Array[File] fastq_files File reference }

scatter (fastq in fastq_files) {
    call align {
        input:
            fastq = fastq,
            reference = reference
    }
}

output {
    Array[File] bam_files = align.bam
}

}

Scatter with Paired Files

version 1.0

struct SampleFastqs { String sample_id File fastq_1 File fastq_2 }

workflow paired_alignment { input { Array[SampleFastqs] samples File reference }

scatter (sample in samples) {
    call align {
        input:
            sample_id = sample.sample_id,
            reads_1 = sample.fastq_1,
            reads_2 = sample.fastq_2,
            reference = reference
    }
}

output {
    Array[File] bams = align.bam
}

}

Conditional Execution

version 1.0

workflow conditional_qc { input { File fastq Boolean run_qc = true }

if (run_qc) {
    call fastqc {
        input:
            fastq = fastq
    }
}

output {
    File? qc_report = fastqc.html
}

}

Structs and Complex Types

version 1.0

struct ReferenceData { File fasta File fasta_index File dict File? known_sites }

workflow variant_calling { input { ReferenceData reference Array[File] bam_files }

scatter (bam in bam_files) {
    call haplotype_caller {
        input:
            bam = bam,
            ref_fasta = reference.fasta,
            ref_index = reference.fasta_index,
            ref_dict = reference.dict
    }
}

}

Input JSON

{ "rnaseq.fastq_1": "data/sample1_R1.fq.gz", "rnaseq.fastq_2": "data/sample1_R2.fq.gz", "rnaseq.salmon_index": "ref/salmon_index", "rnaseq.threads": 8 }

Array Inputs JSON

{ "process_samples.samples": [ { "sample_id": "sample1", "fastq_1": "data/sample1_R1.fq.gz", "fastq_2": "data/sample1_R2.fq.gz" }, { "sample_id": "sample2", "fastq_1": "data/sample2_R1.fq.gz", "fastq_2": "data/sample2_R2.fq.gz" } ], "process_samples.reference": "ref/genome.fa" }

Subworkflows

version 1.0

import "qc.wdl" as qc import "align.wdl" as align

workflow main_pipeline { input { File fastq_1 File fastq_2 File reference }

call qc.quality_control {
    input:
        reads_1 = fastq_1,
        reads_2 = fastq_2
}

call align.alignment {
    input:
        reads_1 = quality_control.trimmed_1,
        reads_2 = quality_control.trimmed_2,
        reference = reference
}

}

Runtime Options

runtime { docker: "ubuntu:20.04" cpu: 4 memory: "8 GB" disks: "local-disk 100 HDD" preemptible: 3 maxRetries: 2 zones: "us-central1-a us-central1-b" bootDiskSizeGb: 15 }

String Interpolation and Expressions

version 1.0

task process { input { String sample_id Int memory_gb = 8 Array[File] input_files }

Int memory_mb = memory_gb * 1000
String output_name = sample_id + ".processed.bam"

command <<<
    # Access array elements
    process_tool \
        --memory ~{memory_mb} \
        --inputs ~{sep=' ' input_files} \
        --output ~{output_name}
>>>

output {
    File result = output_name
}

}

File Size and Disk Calculation

version 1.0

task align { input { File reads_1 File reads_2 File reference }

# Calculate disk: input files + 3x for outputs + buffer
Int disk_gb = ceil(size(reads_1, "GB") + size(reads_2, "GB") +
                   size(reference, "GB") * 2) + 50

command <<<
    bwa mem ~{reference} ~{reads_1} ~{reads_2} > aligned.sam
>>>

runtime {
    disks: "local-disk " + disk_gb + " SSD"
}

}

Complete RNA-seq Workflow

version 1.0

workflow rnaseq_pipeline { input { Array[String] sample_ids Array[File] fastq_1_files Array[File] fastq_2_files File salmon_index Int threads = 8 }

scatter (idx in range(length(sample_ids))) {
    call fastp {
        input:
            sample_id = sample_ids[idx],
            reads_1 = fastq_1_files[idx],
            reads_2 = fastq_2_files[idx],
            threads = threads
    }

    call salmon_quant {
        input:
            sample_id = sample_ids[idx],
            reads_1 = fastp.trimmed_1,
            reads_2 = fastp.trimmed_2,
            index = salmon_index,
            threads = threads
    }
}

output {
    Array[File] quant_files = salmon_quant.quant_sf
    Array[File] fastp_reports = fastp.json_report
}

}

task fastp { input { String sample_id File reads_1 File reads_2 Int threads = 4 }

command <<<
    fastp -i ~{reads_1} -I ~{reads_2} \
        -o ~{sample_id}_trimmed_R1.fq.gz \
        -O ~{sample_id}_trimmed_R2.fq.gz \
        --json ~{sample_id}_fastp.json \
        --thread ~{threads}
>>>

output {
    File trimmed_1 = "~{sample_id}_trimmed_R1.fq.gz"
    File trimmed_2 = "~{sample_id}_trimmed_R2.fq.gz"
    File json_report = "~{sample_id}_fastp.json"
}

runtime {
    docker: "quay.io/biocontainers/fastp:0.23.4--hadf994f_2"
    cpu: threads
    memory: "4 GB"
}

}

task salmon_quant { input { String sample_id File reads_1 File reads_2 File index Int threads = 8 }

command <<<
    salmon quant -i ~{index} -l A \
        -1 ~{reads_1} -2 ~{reads_2} \
        -o ~{sample_id}_salmon \
        --threads ~{threads} --validateMappings
>>>

output {
    File quant_sf = "~{sample_id}_salmon/quant.sf"
    File quant_dir = "~{sample_id}_salmon"
}

runtime {
    docker: "quay.io/biocontainers/salmon:1.10.0--h7e5ed60_0"
    cpu: threads
    memory: "16 GB"
}

}

Run Commands

Validate WDL syntax

womtool validate workflow.wdl

Generate inputs template

womtool inputs workflow.wdl > inputs.json

Run with Cromwell (local)

java -jar cromwell.jar run workflow.wdl -i inputs.json

Run with miniwdl (simpler local runner)

miniwdl run workflow.wdl -i inputs.json

Run on Terra

Upload WDL and inputs.json to Terra workspace

Execution Engines

Engine Use Case

Cromwell Full-featured, Google Cloud, AWS, HPC

miniwdl Lightweight local execution

Terra Cloud platform with Cromwell backend

AnVIL NIH cloud platform (Terra-based)

dxWDL DNAnexus platform

Related Skills

  • workflow-management/cwl-workflows - CWL alternative

  • workflow-management/snakemake-workflows - Python-based alternative

  • workflow-management/nextflow-pipelines - Groovy-based alternative

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