# Bio Pipeline Runner > Plan and assemble an nf-core bioinformatics run (rnaseq, sarek, atacseq) with a schema-validated samplesheet, pinned version, and correct profile. - Skill: `matrixx0070/bio-pipeline-runner` (Agent Skill) - Install (CLI): `npx skillmds@latest add matrixx0070/bio-pipeline-runner` - Raw SKILL.md: https://api.skillmd.com/api/skills/matrixx0070/bio-pipeline-runner/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: Matrixx0070 (https://skillmd.com/u/matrixx0070) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/matrixx0070/bio-pipeline-runner --- ## When to use Use this when the user wants to run a standardized nf-core pipeline on sequencing data and needs the samplesheet, config, and command assembled correctly the first time — rnaseq (expression), sarek (variants), or atacseq (open chromatin). **Not for:** custom one-off analyses with no nf-core equivalent, QC of an already-produced count matrix (use bio-single-cell-qc), or interpreting biology from finished results. ## Method 1. Match pipeline to the assay. **Decision:** rnaseq for DGE/quantification, sarek for germline/somatic variants, atacseq for open chromatin. If the assay doesn't map cleanly, stop and clarify rather than force-fit. 2. Pin the version with `-r ` and pick the reference (iGenomes key or explicit FASTA/GTF). 3. Build the samplesheet in the pipeline's exact schema: - rnaseq: `sample,fastq_1,fastq_2,strandedness` - sarek: `patient,sample,lane,fastq_1,fastq_2` - atacseq: `sample,fastq_1,fastq_2,replicate` Validate paths exist and paired-end mates match. 4. Choose the execution profile: docker/singularity/conda for containers plus an executor (local/slurm) and resource limits via `-c`. **Decision:** singularity on shared HPC (no root), docker on a personal box. 5. Dry-run with `-profile test` or `-stub` to catch schema errors before burning compute. 6. Launch, then inspect MultiQC and `pipeline_info` execution reports for failures and resource waste. ## Example Bulk RNA-seq, 6 samples, reverse-stranded, on a SLURM cluster. Choose `nf-core/rnaseq -r 3.14.0`, genome `GRCh38`. Samplesheet rows read `ctrl_1,/data/c1_R1.fq.gz,/data/c1_R2.fq.gz,reverse`. Profile: `-profile singularity` with a `slurm.config` capping 16 CPU / 72 GB per process. `-profile test` passes, then the full run. MultiQC STAR alignment rate ~92% confirms the stranded setting was right. ## Pitfalls - Leaving strandedness as `auto` when the kit is known — silently wrong DGE if guessed incorrectly. - Not pinning `-r`, so a later default-branch change makes the run irreproducible. - Skipping the `-profile test` dry-run and discovering a samplesheet typo after hours of alignment. - Requesting more memory per process than the node has, so every job pends forever. ## Output format ``` # nf-core Run Plan — Pipeline: nf-core/ Version: -r Genome: Rationale: ## samplesheet.csv
## Command nextflow run nf-core/ -r \ --input samplesheet.csv --outdir results \ --genome -profile , -c ## Post-run checklist MultiQC sections to check | common failure modes | where results land ``` The run executes on the user's compute — report commands, never fabricate results. ## Reference ### Pipeline selection | Assay / question | nf-core pipeline | Core output | |------------------|-----------------|-------------| | Bulk gene expression / DGE | `rnaseq` | Gene & transcript counts, QC | | Germline / somatic variants (WGS/WES/panel) | `sarek` | VCFs (SNV/indel/CNV/SV), annotation | | Chromatin accessibility | `atacseq` | Peaks, consensus set, QC | | ChIP / TF binding | `chipseq` | Peaks, coverage | | Differential exon/isoform | `rnasplice` | Splicing events | | Amplicon / metagenomics | `ampliseq` / `mag` | ASVs / MAGs | | scRNA-seq (alignment→matrix) | `scrnaseq` | Count matrix (feeds bio-single-cell-qc) | ### nf-core/rnaseq (v3.14+) key params - `--input` samplesheet: `sample,fastq_1,fastq_2,strandedness` — strandedness ∈ `forward` (e.g. some Ligation kits), `reverse` (Illumina TruSeq stranded / dUTP — most common), `unstranded`, `auto`. Set it explicitly when the kit is known; `auto` (salmon inference) is a fallback, not a default. - Aligner: `--aligner star_salmon` (default, recommended — gives gene + transcript quant), `star_rsem`, or `--pseudo_aligner salmon`/`kallisto` (alignment-free, fast). - Reference: `--genome GRCh38` (iGenomes) **or** explicit `--fasta`/`--gtf` (iGenomes GTFs are dated — a current GENCODE/Ensembl GTF is often better). - Useful: `--extra_salmon_quant_args`, `--skip_biotype_qc`, `--remove_ribo_rna` (+ `--ribo_database_manifest`), `--umitools_*` for UMI protocols. - QC bundled: FastQC, trimming (Trim Galore/fastp), STAR logs, RSeQC, Qualimap, dupRadar, Preseq, DESeq2 sample-similarity PCA, all rolled into MultiQC. Sanity check: STAR uniquely-mapped ≥ ~80–90%, assigned reads high, and the strandedness bar plot matches your declared setting. ### nf-core/sarek (v3.4+) key params - `--input`: `patient,sample,lane,fastq_1,fastq_2` (or `bam`/`cram` + `.crai`). Group tumor/normal by shared `patient`, set `status` (0=normal,1=tumor) for somatic. - `--wes` for exome/panel (add `--intervals target.bed` — critical for runtime & correctness); omit for WGS. - `--tools`: callers e.g. `strelka,mutect2,manta,cnvkit,ascat,deepvariant,haplotypecaller,freebayes,vep,snpeff`. Germline → `haplotypecaller`/`deepvariant`; somatic → `mutect2`/`strelka`; SV → `manta`; CNV → `cnvkit`/`ascat`. - `--genome GATK.GRCh38` (bundles known-sites for BQSR); `--joint_germline` for cohort GVCF joint calling. `--step` can resume at `mapping|markduplicates|prepare_recalibration|recalibrate|variant_calling|annotate`. ### nf-core/atacseq (v2.1+) key params - `--input`: `sample,fastq_1,fastq_2,replicate` (replicate integer groups technical/biological reps for consensus peaks). - `--read_length` (drives MACS2 effective genome size), `--narrow_peak` (default; omit → broad), `--macs_gsize` (or auto from genome). Blacklist filtering is applied for supported genomes. - QC: TSS enrichment, FRiP, fragment-length periodicity (nucleosome laddering), library complexity — inspect in MultiQC. ### Reproducibility & profiles - **Always** pin `-r ` (e.g. `-r 3.14.0`); an unpinned run tracks the default branch and drifts. - Container/env profile: `-profile docker` (personal box, root), `singularity`/`apptainer` (shared HPC, no root — most common), `conda` (last resort), plus executor via a `-c custom.config` (`process.executor = 'slurm'`, queue, `withName`/`withLabel` resource overrides). nf-core resource labels: `process_low` (~2 CPU/12 GB), `process_medium` (~6 CPU/36 GB), `process_high` (~12 CPU/72 GB), `process_high_memory` (~200 GB, e.g. STAR index for large genomes). Never request more than a node offers or jobs pend forever. - Rough guidance: human WGS sarek ≈ 100–300 GB scratch + tens of core-hours/sample; STAR RNA index needs ~30–40 GB RAM; use `-resume` to reuse cached tasks; set `-work-dir` on fast scratch and clean it after. - **Dry run first:** `-profile test,` (tiny bundled data) or `-stub` catches samplesheet/schema errors before burning real compute. `--outdir` is required; results land under it with `pipeline_info/` (execution_report.html, timeline, trace) and `multiqc/`.