Nextflow Development

What This Skill Does

• This skill assists users in deploying nf-core bioinformatics pipelines for analyzing sequencing data.
• It guides researchers, even those without extensive bioinformatics expertise, through the process of running pipelines for tasks like differential expression analysis and variant calling.
• The skill focuses on simplifying the deployment and execution of these complex pipelines.

When to Use

• Run RNA-seq pipelines for differential expression analysis.
• Execute WGS/WES pipelines for variant calling.
• Deploy ATAC-seq pipelines for chromatin accessibility analysis.
• Analyze local sequencing data using nf-core pipelines.
• Process data obtained from public repositories like GEO/SRA.
• Troubleshoot environment issues related to Docker and Nextflow.

Key Features

• Provides a step-by-step workflow checklist for pipeline deployment.
• Offers scripts for acquiring data from GEO/SRA.
• Includes environment checks to ensure proper setup.
• Provides fix instructions for common Docker and Nextflow issues.
• Supports running test profiles to validate pipeline configuration.
• Guides users in creating samplesheets for their data.

Manual Installation

Workflow Checklist

• Step 0: Acquire data (if from GEO/SRA) [ ] Step 1: Environment check (MUST pass) [ ] Step 2: Select pipeline (confirm with user) [ ] Step 3: Run test profile (MUST pass) [ ] Step 4: Create samplesheet [ ] Step 5: Configure & run (confirm genome with user) [ ] Step 6: Verify outputs

Step 0: Acquire Data (GEO/SRA Only)

Skip this step if user has local FASTQ files.

For public datasets, fetch from GEO/SRA first. See references/geo-sra-acquisition.md for the full workflow.

Quick start:

1. Get study info

python scripts/sra_geo_fetch.py info GSE110004

1. Download (interactive mode) python scripts/sra_geo_fetch.py download GSE110004 -o ./fastq -i

2. Generate samplesheet python scripts/sra_geo_fetch.py samplesheet GSE110004 --fastq-dir ./fastq -o samplesheet.csv

DECISION POINT: After fetching study info, confirm with user: Which sample subset to download (if multiple data types) Suggested genome and pipeline

Then continue to Step 1.

Step 1: Environment Check

• Run first.
• Pipeline will fail without passing environment.

python scripts/check_environment.py

All critical checks must pass. If any fail, provide fix instructions:

Docker issues

Nextflow issues

Java issues

Do not proceed until all checks pass. For HPC/Singularity, see references/troubleshooting.md.

Step 2: Select Pipeline

DECISION POINT: Confirm with user before proceeding.

Auto-detect from data: python scripts/detect_data_type.py /path/to/data

For pipeline-specific details: references/pipelines/rnaseq.md references/pipelines/sarek.md references/pipelines/atacseq.md

Step 3: Run Test Profile

• Validates environment with small data.
• MUST pass before real data.

nextflow run nf-core/<pipeline> -r <version> -profile test,docker --outdir test_output

Verify: ls test_output/multiqc/multiqc_report.html grep "Pipeline completed successfully" .nextflow.log

If test fails, see references/troubleshooting.md.

Step 4: Create Samplesheet

Generate automatically

python scripts/generate_samplesheet.py /path/to/data <pipeline> -o samplesheet.csv

The script: Discovers FASTQ/BAM/CRAM files Pairs R1/R2 reads Infers sample metadata Validates before writing

For sarek: Script prompts for tumor/normal status if not auto-detected.

Validate existing samplesheet

python scripts/generate_samplesheet.py --validate samplesheet.csv <pipeline>

Samplesheet formats

rnaseq: sample,fastq_1,fastq_2,strandedness SAMPLE1,/abs/path/R1.fq.gz,/abs/path/R2.fq.gz,auto

sarek: patient,sample,lane,fastq_1,fastq_2,status patient1,tumor,L001,/abs/path/tumor_R1.fq.gz,/abs/path/tumor_R2.fq.gz,1 patient1,normal,L001,/abs/path/normal_R1.fq.gz,/abs/path/normal_R2.fq.gz,0

atacseq: sample,fastq_1,fastq_2,replicate CONTROL,/abs/path/ctrl_R1.fq.gz,/abs/path/ctrl_R2.fq.gz,1

Step 5: Configure & Run

5a. Check genome availability

python scripts/manage_genomes.py check <genome> If not installed: python scripts/manage_genomes.py download <genome>

Common genomes: GRCh38 (human), GRCh37 (legacy), GRCm39 (mouse), R64-1-1 (yeast), BDGP6 (fly)

5b. Decision points

DECISION POINT: Confirm with user:

Genome: Which reference to use Pipeline-specific options:

• rnaseq: aligner (star_salmon recommended, hisat2 for low memory)
• sarek: tools (haplotypecaller for germline, mutect2 for somatic)
• atacseq: read_length (50, 75, 100, or 150)

5c. Run pipeline

nextflow run nf-core/<pipeline>
-r <version>
-profile docker
--input samplesheet.csv
--outdir results
--genome <genome>
-resume

Key flags: -r: Pin version -profile docker: Use Docker (or singularity for HPC) --genome: iGenomes key -resume: Continue from checkpoint

Resource limits (if needed): --max_cpus 8 --max_memory '32. GB' --max_time '24.h'

Step 6: Verify Outputs

Check completion

ls results/multiqc/multiqc_report.html grep "Pipeline completed successfully" .nextflow.log

Key outputs by pipeline

rnaseq: results/star_salmon/salmon.merged.gene_counts.tsv - Gene counts results/star_salmon/salmon.merged.gene_tpm.tsv - TPM values

sarek: results/variant_calling/*/ - VCF files results/preprocessing/recalibrated/ - BAM files

atacseq: results/macs2/narrowPeak/ - Peak calls results/bwa/mergedLibrary/bigwig/ - Coverage tracks

Quick Reference

For common exit codes and fixes, see references/troubleshooting.md.

Resume failed run

nextflow run nf-core/<pipeline> -resume

References

references/geo-sra-acquisition.md - Downloading public GEO/SRA data references/troubleshooting.md - Common issues and fixes references/installation.md - Environment setup references/pipelines/rnaseq.md - RNA-seq pipeline details references/pipelines/sarek.md - Variant calling details references/pipelines/atacseq.md - ATAC-seq details

Disclaimer

• This skill is provided as a prototype example demonstrating how to integrate nf-core bioinformatics pipelines into Claude Code for automated analysis workflows.
• The current implementation supports three pipelines (rnaseq, sarek, and atacseq), serving as a foundation that enables the community to expand support to the full set of nf-core pipelines.

It is intended for educational and research purposes and should not be considered production-ready without appropriate validation for your specific use case. Users are responsible for ensuring their computing environment meet