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 Problem Fix Not installed Install 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 Problem Fix Not installed curl -s https://get.nextflow.io | bash && mv nextflow ~/bin/ Version < 23.04 nextflow self-update Java issues Problem Fix 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 Type Pipeline Version Goal RNA-seq rnaseq 3.22.2 Gene expression WGS/WES sarek 3.7.1 Variant calling ATAC-seq atacseq 2.1.2 Chromatin accessibility 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 Pipeline Command 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: 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 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 )