Agent skill

bio-workflow-management-nextflow-pipelines

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npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-workflow-management-nextflow-pipelines

SKILL.md

name: bio-workflow-management-nextflow-pipelines description: Create scalable, containerized bioinformatics pipelines with Nextflow DSL2 supporting Docker, Singularity, and cloud execution. Use when building portable pipelines with container support, running workflows on cloud platforms (AWS, Google Cloud), or leveraging nf-core community pipelines. tool_type: cli primary_tool: Nextflow measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

Nextflow Pipelines

Basic Pipeline Structure

groovy
// main.nf
nextflow.enable.dsl=2

params.reads = "data/*_{1,2}.fq.gz"
params.outdir = "results"

process FASTQC {
    input:
    tuple val(sample_id), path(reads)

    output:
    path("*.html"), emit: html
    path("*.zip"), emit: zip

    script:
    """
    fastqc ${reads}
    """
}

workflow {
    Channel.fromFilePairs(params.reads)
        | FASTQC
}

DSL2 Modules

groovy
// modules/fastqc.nf
process FASTQC {
    tag "${sample_id}"
    publishDir "${params.outdir}/qc", mode: 'copy'

    input:
    tuple val(sample_id), path(reads)

    output:
    tuple val(sample_id), path("*.html"), emit: html
    tuple val(sample_id), path("*.zip"), emit: zip

    script:
    """
    fastqc -t ${task.cpus} ${reads}
    """
}
groovy
// main.nf
include { FASTQC } from './modules/fastqc'
include { ALIGN } from './modules/align'

workflow {
    reads_ch = Channel.fromFilePairs(params.reads)
    FASTQC(reads_ch)
    ALIGN(reads_ch)
}

Config File

groovy
// nextflow.config
params {
    reads = "data/*_{1,2}.fq.gz"
    outdir = "results"
    genome = "ref/genome.fa"
}

process {
    cpus = 4
    memory = '8 GB'
    time = '2h'

    withName: 'ALIGN' {
        cpus = 16
        memory = '32 GB'
    }
}

profiles {
    docker {
        docker.enabled = true
    }
    singularity {
        singularity.enabled = true
    }
    slurm {
        process.executor = 'slurm'
    }
}

Container Support

groovy
process SALMON_QUANT {
    container 'quay.io/biocontainers/salmon:1.10.0--h7e5ed60_0'

    input:
    tuple val(sample_id), path(reads)
    path(index)

    output:
    tuple val(sample_id), path("${sample_id}"), emit: quant

    script:
    """
    salmon quant -i ${index} -l A -1 ${reads[0]} -2 ${reads[1]} \
        -o ${sample_id} --threads ${task.cpus}
    """
}

Channel Operations

groovy
// From file pairs
Channel.fromFilePairs("data/*_{1,2}.fq.gz")
    .set { reads_ch }

// From path
Channel.fromPath("data/*.bam")
    .map { file -> tuple(file.baseName, file) }
    .set { bam_ch }

// From samplesheet
Channel.fromPath(params.samplesheet)
    .splitCsv(header: true)
    .map { row -> tuple(row.sample, file(row.fastq_1), file(row.fastq_2)) }
    .set { samples_ch }

// Combine channels
reads_ch.combine(reference_ch)

Subworkflows

groovy
// subworkflows/qc.nf
include { FASTQC } from '../modules/fastqc'
include { MULTIQC } from '../modules/multiqc'

workflow QC {
    take:
    reads

    main:
    FASTQC(reads)
    MULTIQC(FASTQC.out.zip.collect())

    emit:
    qc_report = MULTIQC.out.report
}
groovy
// main.nf
include { QC } from './subworkflows/qc'
include { ALIGN } from './subworkflows/align'

workflow {
    reads = Channel.fromFilePairs(params.reads)
    QC(reads)
    ALIGN(reads)
}

Cluster Execution

groovy
// nextflow.config for SLURM
process {
    executor = 'slurm'
    queue = 'normal'
    clusterOptions = '--account=myproject'

    withLabel: 'high_memory' {
        memory = '128 GB'
        queue = 'highmem'
    }
}

executor {
    name = 'slurm'
    queueSize = 100
    submitRateLimit = '10 sec'
}

AWS/Cloud Execution

groovy
// nextflow.config for AWS Batch
process {
    executor = 'awsbatch'
    queue = 'my-batch-queue'
}

aws {
    region = 'us-east-1'
    batch {
        cliPath = '/usr/local/bin/aws'
    }
}
bash
# Run on AWS
nextflow run main.nf -profile awsbatch -bucket-dir s3://my-bucket/work

Resource Labels

groovy
process {
    withLabel: 'process_low' {
        cpus = 2
        memory = '4 GB'
        time = '1h'
    }
    withLabel: 'process_medium' {
        cpus = 8
        memory = '16 GB'
        time = '4h'
    }
    withLabel: 'process_high' {
        cpus = 16
        memory = '64 GB'
        time = '12h'
    }
}
groovy
process ALIGN {
    label 'process_high'
    // ...
}

Error Handling

groovy
process RISKY_PROCESS {
    errorStrategy 'retry'
    maxRetries 3
    memory { 8.GB * task.attempt }

    script:
    """
    memory_intensive_command
    """
}

process OPTIONAL_PROCESS {
    errorStrategy 'ignore'
    // ...
}

Caching and Resume

bash
# Resume from last run
nextflow run main.nf -resume

# Clean work directory
nextflow clean -f

# Show execution trace
nextflow log

Complete RNA-seq Pipeline

groovy
nextflow.enable.dsl=2

params.reads = "data/*_{1,2}.fq.gz"
params.salmon_index = "ref/salmon_index"
params.outdir = "results"

process FASTP {
    tag "${sample_id}"
    publishDir "${params.outdir}/trimmed", mode: 'copy'

    input:
    tuple val(sample_id), path(reads)

    output:
    tuple val(sample_id), path("${sample_id}_{1,2}.trimmed.fq.gz"), emit: reads
    path("${sample_id}.json"), emit: json

    script:
    """
    fastp -i ${reads[0]} -I ${reads[1]} \
        -o ${sample_id}_1.trimmed.fq.gz -O ${sample_id}_2.trimmed.fq.gz \
        --json ${sample_id}.json --thread ${task.cpus}
    """
}

process SALMON_QUANT {
    tag "${sample_id}"
    publishDir "${params.outdir}/salmon", mode: 'copy'

    input:
    tuple val(sample_id), path(reads)
    path(index)

    output:
    tuple val(sample_id), path("${sample_id}"), emit: quant

    script:
    """
    salmon quant -i ${index} -l A -1 ${reads[0]} -2 ${reads[1]} \
        -o ${sample_id} --threads ${task.cpus}
    """
}

process MULTIQC {
    publishDir "${params.outdir}", mode: 'copy'

    input:
    path('*')

    output:
    path("multiqc_report.html")

    script:
    """
    multiqc .
    """
}

workflow {
    reads_ch = Channel.fromFilePairs(params.reads)
    index_ch = Channel.fromPath(params.salmon_index)

    FASTP(reads_ch)
    SALMON_QUANT(FASTP.out.reads, index_ch.first())

    qc_files = FASTP.out.json.collect()
        .mix(SALMON_QUANT.out.quant.collect())
    MULTIQC(qc_files.collect())
}

Related Skills

  • workflow-management/snakemake-workflows - Snakemake alternative
  • workflows/rnaseq-to-de - End-to-end RNA-seq
  • read-qc/fastp-workflow - QC processes

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