Nextflow Development

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:

# 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.

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.

The bundled samplesheet logic and references were authored against the tested baselines. For a new analysis, inspect the official release notes and schema changes, run the selected release's test profile, and pin the exact passing tag. Do not replace a baseline version string solely because a newer tag exists.

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:

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

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 for active-host-assisted nf-core bioinformatics 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

• nf-core pipelines: MIT License (https://nf-co.re/about)
• Nextflow: Apache License, Version 2.0 (https://www.nextflow.io/about-us.html)
• NCBI SRA Toolkit: Public Domain (https://github.com/ncbi/sra-tools/blob/master/LICENSE)