nextflow-development

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Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

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npx skill4agent add anthropics/life-sciences nextflow-development

SKILL.md Content

nf-core Pipeline Deployment

Run nf-core bioinformatics pipelines on local or public sequencing data.
Target users: Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses—differential expression, variant calling, or chromatin accessibility analysis.

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:
bash
# 1. Get study info
python scripts/sra_geo_fetch.py info GSE110004

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

# 3. 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.
bash
python scripts/check_environment.py
All critical checks must pass. If any fail, provide fix instructions:

Docker issues

ProblemFix
Not installedInstall from https://docs.docker.com/get-docker/
Permission denied
sudo usermod -aG docker $USER
then re-login
Daemon not running
sudo systemctl start docker

Nextflow issues

ProblemFix
Not installed
curl -s https://get.nextflow.io | bash && mv nextflow ~/bin/
Version < 23.04
nextflow self-update

Java issues

ProblemFix
Not installed / < 11
sudo apt install openjdk-11-jdk
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.
Data TypePipelineVersionGoal
RNA-seq
rnaseq
3.22.2Gene expression
WGS/WES
sarek
3.7.1Variant calling
ATAC-seq
atacseq
2.1.2Chromatin accessibility
Auto-detect from data:
bash
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.
bash
nextflow run nf-core/<pipeline> -r <version> -profile test,docker --outdir test_output
PipelineCommand
rnaseq
nextflow run nf-core/rnaseq -r 3.22.2 -profile test,docker --outdir test_rnaseq
sarek
nextflow run nf-core/sarek -r 3.7.1 -profile test,docker --outdir test_sarek
atacseq
nextflow run nf-core/atacseq -r 2.1.2 -profile test,docker --outdir test_atacseq
Verify:
bash
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

bash
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

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

Samplesheet formats

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

bash
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:
  1. Genome: Which reference to use
  2. 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

bash
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):
bash
--max_cpus 8 --max_memory '32.GB' --max_time '24.h'

Step 6: Verify Outputs

Check completion

bash
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

bash
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 meets pipeline requirements and for verifying analysis results.
Anthropic does not guarantee the accuracy of bioinformatics outputs, and users should follow standard practices for validating computational analyses. This integration is not officially endorsed by or affiliated with the nf-core community.

Attribution

When publishing results, cite the appropriate pipeline. Citations are available in each nf-core repository's CITATIONS.md file (e.g., https://github.com/nf-core/rnaseq/blob/3.22.2/CITATIONS.md).

Licenses