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Run nf-core Nextflow pipelines for RNA-seq, variant, and ATAC-seq analysis.
The material indicates an open-source, prompt/document-oriented skill with no declared secrets or fixed remote endpoints, so overall risk is low. Caution is still warranted because the described workflow involves fetching public datasets, installing dependencies, and running bioinformatics pipelines, which would introduce normal local execution, file access, and external dependency retrieval risks if actually used.
The material and system checks indicate no required secrets or environment variables. No API tokens, cloud credentials, or account passwords are requested, so credential exposure appears limited.
The README explicitly includes fetching public sequencing data from GEO/SRA and obtaining external dependencies such as Docker/Nextflow. While no fixed service endpoint is declared, practical use would involve network access to public data and dependency sources, potentially sending query parameters or sample identifiers.
The documentation includes commands to run Python scripts, execute Nextflow pipelines, and install/update software, and it references Docker, Java, and system-level commands. This is standard local code execution and process spawning behavior and should be used in a controlled environment.
The skill is intended to process local FASTQ files, generate samplesheets, and verify outputs, indicating expected access to local sequencing data and output files. The material does not show permissions beyond its stated purpose, but it still entails local access to potentially large research datasets.
The source is an open-source GitHub repository and is system-labeled as prompt-only and open-source, which supports auditability. No closed-source exfiltration or clear malicious indicators are evident. Still, the repository has 0 stars, no declared license, and unknown maintenance status, which lowers confidence but does not by itself justify a high-risk rating.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "nextflow-development" skill from askskill: 1. Download https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/bio-research/skills/nextflow-development/SKILL.md 2. Save it as ~/.claude/skills/nextflow-development/SKILL.md 3. Reload skills and tell me it's ready
Use nf-core/rnaseq to analyze local FASTQ files, generate a samplesheet, and produce gene expression quantification, QC results, and files needed for differential expression analysis.
Provides runnable pipeline configuration, samplesheet structure guidance, and a list of expression and QC outputs.
Use nf-core/sarek for variant calling on WGS/WES sequencing data, and explain the required input format, reference genome setup, and main output files.
Returns a Sarek execution plan and lists key output files for SNPs, indels, and structural variants.
I have GEO/SRA accessions such as GSE, GSM, or SRR. Help me organize the download and analysis steps and choose the right nf-core pipeline for reproducible reanalysis.
Provides an end-to-end plan from public accessions to samplesheet creation, data download, and downstream analysis.
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.
- [ ] 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
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:
Then continue to Step 1.
Run first. Pipeline will fail without passing environment.
python scripts/check_environment.py
All critical checks must pass. If any fail, provide fix instructions:
| 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 |
| Problem | Fix |
|---|---|
| Not installed | curl -s https://get.nextflow.io | bash && mv nextflow ~/bin/ |
| Version < 23.04 | nextflow self-update |
| 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.
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:
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.
python scripts/generate_samplesheet.py /path/to/data <pipeline> -o samplesheet.csv
The script:
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