/nextflow-development

name: nextflow-development
description: 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.

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](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](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/rnaseq.md)
• [references/pipelines/sarek.md](references/pipelines/sarek.md)
• [references/pipelines/atacseq.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](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
• `