bio-workflow-management-nf-core-pipelines

name	bio-workflow-management-nf-core-pipelines
description	Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.
origin	openai4s
category	bioskills/workflow-management
metadata	{"tool_type":"cli","primary_tool":"nf-core","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: Nextflow 24.04+, nf-core/tools 3.0+, Docker 24+ or Singularity 3.8+

Before using code patterns, verify installed versions match. If versions differ:

• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Note: -r <tag> pins the pipeline to an immutable release; without it nextflow run nf-core/<pipe> pulls whatever the mutable default branch is today, so results are not reproducible. Pin the container engine too (a pipeline release ships digest-pinned images; -profile docker uses them, :latest does not). Schema validation moved from the deprecated nf-validation plugin to nf-schema; a current pipeline uses nf-schema, so validate samplesheets against the pipeline's shipped schema, not a hand-written one.

nf-core Pipelines

"Run a curated community pipeline on my samples" -> Select a versioned nf-core pipeline, pin its release, choose a container profile, validate a samplesheet against the pipeline schema, point it at references, and run it, reading the aggregated MultiQC report at the end.

• CLI: nextflow run nf-core/<pipeline> -r <version> -profile <container>,<institution> --input samplesheet.csv --outdir results -resume
• CLI: nf-core pipelines list / nf-core pipelines download (browse and cache pipelines)

The governing principle: ADOPT a community pipeline before authoring a new one

For any mainstream analysis - RNA-seq, germline/somatic variant calling, ATAC-seq, ChIP-seq, methylation, amplicon/metagenomics, single-cell - a curated nf-core pipeline already encodes years of QC, edge-case handling, CI/nf-test regression tests, institutional configs for hundreds of HPCs, a standardized samplesheet+schema, and MultiQC reporting (Ewels 2020 Nat Biotechnol 38:276-278). Reinventing that in hand-written Nextflow is months of work and worse QC: the community pipeline has already found the bugs a bespoke version will rediscover, and it is maintained across every future tool update and reference build. The decision every biologist should default toward is ADOPT, not BUILD.

The corollary trap is treating "adopt" as "run once and trust the number". A community pipeline is only reproducible if the RUN is pinned: -r pins the pipeline version, the release's digest-pinned containers pin the software, --genome/reference URIs pin the reference data (Wratten et al. 2021 Nat Methods 18:1161-1168; Grüning et al. 2018 Cell Syst 6:631-635). An unpinned nextflow run nf-core/rnaseq on the default branch with a :latest engine is exactly as irreproducible as a hand-rolled script - the curation buys nothing if the invocation is loose. Author from scratch only for genuinely novel logic with no community pipeline, an unsupported combination of steps, or an institutional constraint no config can express (see workflow-management/nextflow-pipelines).

Decision: adopt an nf-core pipeline vs author a new one

Situation	Verdict	Why
Mainstream analysis with an existing nf-core pipeline (rnaseq, sarek, atacseq, ...)	ADOPT	Curated, CI-tested, institutional configs, MultiQC; DIY is worse QC
A supported pipeline plus a few extra params/references	ADOPT + configure	-c custom.config, -params-file, --genome; no authoring needed
Genuinely novel method, no community pipeline exists	BUILD	Author in Nextflow; still install tested nf-core modules, do not hand-write wrappers
An unsupported ORDER/combination of otherwise-standard steps	BUILD (or fork)	Scaffold with nf-core pipelines create; reuse nf-core modules install
One-off, few linear steps, single sample, single machine	Neither	A plain script is honest; a workflow manager is overhead below this threshold

Decision: which container profile by platform

Platform	Profile	Why
Laptop / workstation with Docker	-profile docker	Simplest; needs root/daemon; digest-pinned images from the release
Shared HPC (no root, has Singularity/Apptainer)	-profile singularity	Rootless; the default on most academic clusters
Cluster allowing Podman	-profile podman	Rootless Docker-compatible alternative
No container engine available at all	-profile conda	Last resort; slower, less reproducible than a pinned image
Named institution in nf-core/configs (uppmax, crick, ...)	-profile singularity,<institution>	Institutional config sets executor, queues, max_memory; comma, no space
Smoke test before real data	-profile test,docker	Ships a tiny public dataset; proves the install end-to-end in minutes

Profiles are comma-separated with NO spaces and applied left-to-right (later overrides earlier), so -profile test,docker runs the test dataset under Docker, and -profile singularity,uppmax layers the institutional config over Singularity.

Decision: --genome/iGenomes vs custom references

Reference source	Use when	Caveat
--genome GRCh38 (iGenomes)	A standard build suffices and convenience matters	iGenomes builds are frozen/aging; the annotation may lag current releases
Explicit --fasta + --gtf (+ --gff)	A specific build/patch or a non-model organism is needed	Pin the exact reference version; record its URI for provenance
Pipeline builds its own index vs --<tool>_index	Reusing an index across runs saves hours	A stale index built from a different FASTA silently corrupts results

The reference layer is a reproducibility layer in its own right: --genome GRCh38 without a recorded iGenomes snapshot pins less than an explicit --fasta/--gtf URI pair. Prefer explicit references and record their source when the result must be reproduced.

The run pattern (pin everything)

# Smoke test first: tiny public dataset proves the install + engine end to end.
nextflow run nf-core/rnaseq -r 3.14.0 -profile test,docker --outdir results_test

# Real run: -r pins the release, -profile picks the engine, --input is the samplesheet.
nextflow run nf-core/rnaseq -r 3.14.0 \
    -profile singularity \
    --input samplesheet.csv \
    --genome GRCh38 \
    --outdir results \
    -resume

• -r 3.14.0 is the pipeline REVISION (a git tag). It is MANDATORY: without it the run tracks the mutable default branch and is not reproducible.
• -profile singularity selects the container engine (comma-add an institutional config: -profile singularity,uppmax).
• Single-dash options (-r, -profile, -resume, -c, -params-file) are NEXTFLOW options; double-dash options (--input, --genome, --outdir, --max_memory) are PIPELINE parameters. Mixing up the dash count is the most common invocation error.
• Config precedence, low to high: the pipeline's built-in nextflow.config -> conf/base.config -> selected profiles -> -c custom.config -> -params-file params.yaml -> a --param on the command line. A later source overrides an earlier one.

Parameters can be supplied in a YAML/JSON file instead of long command lines, which is the reproducible-provenance form:

# params.yaml  (nextflow run nf-core/rnaseq -r 3.14.0 -profile singularity -params-file params.yaml)
input: samplesheet.csv
genome: GRCh38
outdir: results
aligner: star_salmon

The samplesheet and schema validation

Every nf-core pipeline reads a CSV samplesheet whose exact columns are defined by the pipeline's shipped schema (assets/schema_input.json) and validated by the nf-schema plugin at launch, before any compute is spent. Wrong or misordered columns fail fast with a schema error rather than hours in.

sample,fastq_1,fastq_2,strandedness
CONTROL_REP1,/data/ctrl1_R1.fastq.gz,/data/ctrl1_R2.fastq.gz,auto
CONTROL_REP2,/data/ctrl2_R1.fastq.gz,/data/ctrl2_R2.fastq.gz,auto
TREAT_REP1,/data/treat1_R1.fastq.gz,/data/treat1_R2.fastq.gz,auto

• Column names are pipeline-specific: nf-core/rnaseq uses sample,fastq_1,fastq_2,strandedness; nf-core/sarek uses patient,sample,lane,fastq_1,fastq_2. Read the pipeline's docs/usage.md for the exact schema, never guess.
• A single-end sample leaves fastq_2 empty; multiple rows sharing one sample value are merged (technical replicates / multiple lanes), which is how the pipeline knows to concatenate them.
• Absolute paths or URLs are safest; relative paths resolve against the launch directory.

Threading columns into the pipeline is handled by the META MAP convention (worth understanding when reading logs or outputs): each sample flows internally as a tuple [ meta, files ] where meta is a map like [ id:'CONTROL_REP1', single_end:false ]. The samplesheet columns become meta keys, so sample identity and pairing travel WITH the files through every step - which is why outputs and the MultiQC report are labelled by the sample value from the sheet.

Institutional configs and resource limits

nf-core/configs supplies ready-made profiles for hundreds of clusters (executor, queues, module system, resource ceilings). Use a named one when it exists; otherwise write a small custom config.

// custom.config  (nextflow run ... -c custom.config)
process {
    executor = 'slurm'
    queue    = 'normal'
    // Clamp per-process resource escalation to the real node ceiling, so an auto-retry that
    // doubles memory never requests more than a node has. resourceLimits is the nf-core/tools 3.0+
    // form (Nextflow 24.04+); it replaced the deprecated params.max_cpus/max_memory/max_time + check_max().
    resourceLimits = [ cpus: 32, memory: 128.GB, time: 48.h ]
}

• nf-core pipelines escalate resources on retry (a task that OOM-kills retries with more memory); process.resourceLimits caps that escalation so a request stays schedulable. A pipeline built on the pre-3.0 template instead reads params.max_cpus/max_memory/max_time (the check_max() pattern, deprecated and removed from the template in tools 3.0) - match whichever the pinned -r release ships. Set the ceiling to the real node/queue limits either way.
• Do not edit the pipeline's own conf/base.config; layer overrides through -c custom.config so the pipeline stays a clean, updatable checkout.

Resume, stub, and previewing the plan

# -resume reuses cached tasks whose inputs+script+container hash is unchanged.
nextflow run nf-core/rnaseq -r 3.14.0 -profile singularity --input samplesheet.csv --outdir results -resume

# -stub runs each process's stub block (touch fake outputs) to validate wiring in seconds.
nextflow run nf-core/rnaseq -r 3.14.0 -profile test,docker --outdir results -stub

• -resume keys on a hash of each task's inputs, resolved script, and container reference. On a network filesystem (Lustre/NFS) unreliable mtimes cause spurious cache misses; the standard fix is cache 'lenient' in a custom config. Deleting work/ destroys the resume cache - a re-run then recomputes everything.
• -stub validates that the samplesheet, profile, and channel wiring are correct without running any tool, which is the fast pre-flight before committing an HPC allocation.

Building a new pipeline (only when adoption does not fit)

# Scaffold a standardized pipeline (template, CI, lint, nf-test) - current tools syntax.
nf-core pipelines create

# Install a pre-written, tested module instead of hand-writing a tool wrapper.
nf-core modules install fastqc
nf-core subworkflows install bam_sort_stats_samtools

# Lint against the template and run the module's nf-test snapshot tests.
nf-core pipelines lint
nf-core modules test fastqc

Even when building, reuse the community's tested modules rather than hand-writing bwa/samtools/fastqc wrappers. Authoring mechanics (channels, DSL2, resume internals) live in workflow-management/nextflow-pipelines.

Interpreting MultiQC output

Every nf-core run aggregates per-tool QC into a single multiqc_report.html under the output directory (plus parsed multiqc_data/ tables). Read it before trusting any downstream result:

• The General Statistics table is per-sample; scan for an outlier column (low aligned %, high duplication, skewed GC, adapter content) that flags a failed library BEFORE it contaminates differential analysis.
• Section order mirrors the pipeline steps (e.g. FastQC -> trimming -> alignment -> quantification for rnaseq); a section missing for one sample means that sample failed a step - cross-check the Nextflow log.
• MultiQC reports what the tools measured; it does not decide pass/fail. Set thresholds from the assay, and treat the report as the triage surface, not the verdict (read-qc/quality-reports).

Common Errors

Symptom	Cause	Fix
Results differ between runs / cannot reproduce a published run	no -r, so the mutable default branch was used	always pin -r <version>; record it alongside results
Unknown configuration profile or only one profile applied	-profile test docker with a space	use a comma, no space: -profile test,docker
Launch fails immediately with a schema/validation error	samplesheet columns wrong, misordered, or misnamed for this pipeline	match the pipeline's assets/schema_input.json / docs/usage.md exactly
--input or --genome "is not a valid parameter"	used a single dash (-input) - that is a Nextflow option namespace	pipeline params take double dash; Nextflow options (-r, -profile, -resume) take single
-resume re-runs everything on the cluster	mtime-based cache misses on a network filesystem	add cache 'lenient' via -c custom.config; never delete work/
Container/tool "command not found" at runtime	no container engine profile selected (bare nextflow run)	add -profile docker/singularity/conda
Task unschedulable, requests more memory than any node	retry escalation exceeded the node ceiling	set process.resourceLimits = [cpus:, memory:, time:] (pre-3.0 pipelines: params.max_memory/max_cpus/max_time)
Wrong/aging annotation with --genome	iGenomes builds are frozen and can lag current releases	supply explicit --fasta/--gtf for a specific build and record the URI

Related Skills

• workflow-management/nextflow-pipelines - Author a Nextflow pipeline from scratch when no community pipeline fits
• workflow-management/snakemake-workflows - Rule-based alternative engine for pipeline authoring
• workflows/rnaseq-to-de - Take an nf-core/rnaseq count matrix into differential expression
• read-qc/quality-reports - Interpret the FastQC/MultiQC QC surface a pipeline emits

References

• Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. 2020. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38(3):276-278.
• Di Tommaso P, Chatzou M, Floden EW, Prieto Barja P, Palumbo E, Notredame C. 2017. Nextflow enables reproducible computational workflows. Nat Biotechnol 35(4):316-319.
• Wratten L, Wilm A, Göke J. 2021. Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers. Nat Methods 18:1161-1168.
• Grüning B, Chilton J, Köster J, et al. 2018. Practical computational reproducibility in the life sciences. Cell Syst 6(6):631-635.