Snakemake Workflow Engine

Hard rules

1. No fabricated citations. Every cited work must resolve to a verifiable record (DOI, PMID, arXiv ID, ISBN, URL with retrieval date, or stable identifier). If a source cannot be resolved, the skill must say so explicitly rather than presenting the claim as established.
2. No claim without provenance. Every quantitative or factual claim must point to a file, line, table, figure, dataset, or external source the agent can show. "Trust me" is not provenance.
3. No silent failure. Every script invocation, API call, or tool use must declare its exit status and what to do on non-zero. A skill that silently swallows errors is a violation.

When to use

• Building reproducible multi-step data pipelines (FASTQ → BAM → variants → annotation).
• Scaling the same workflow from local development to a SLURM cluster without code changes.
• Processing many samples identically using wildcard-based rules ({sample}).
• Managing dependencies automatically — only re-run steps whose inputs changed.
• Pinning tool versions per rule (Conda, pixi, or container) for full reproducibility.
• Producing DAG visualisations and dry-run previews before committing compute.
• Reusing validated community modules via Snakemake Wrappers and snakemake-workflow-catalog.

When NOT to use

• The task graph is dynamic (fan-out from a database) or time-scheduled → use Airflow or Prefect.
• The pipeline is single tool, single output → a shell script is simpler.
• You need containerised, cloud-native, Groovy dataflow → use Nextflow (and nf-core).
• You need CWL/WDL standards compliance (clinical genomics, GA4GH) → use cwltool or Cromwell/WDL.

Prerequisites

• Python ≥ 3.11
• snakemake ≥ 8.x (check command -v snakemake first)
• graphviz (for --dag visualisation)
• Conda/mamba, pixi, or Apptainer for per-rule environments
• SLURM/LSF/PBS for HPC execution

conda install -c conda-forge -c bioconda snakemake
# or: pip install snakemake
snakemake --version          # 8.x.x

Core workflow

1. Declare a target rule all with the final outputs you want to produce.
2. Write rules mapping input files → output files via a shell:, run:, or script: block.
3. Use wildcards ({sample}) for sample-agnostic rules and expand() to materialise the target list.
4. Add resources (mem_mb, runtime, threads) to compute-heavy rules.
5. Pin environments with conda:, container:, or pixi: per rule.
6. Dry-run with snakemake -n to confirm the DAG.
7. Execute with a profile (--profile profiles/slurm) for HPC/cloud.

Code patterns

Minimal two-rule pipeline

# Snakefile
SAMPLES = ["sampleA", "sampleB"]

rule all:
    input:
        expand("results/{sample}.sorted.bam", sample=SAMPLES)

rule align:
    input:
        fastq="data/{sample}.fastq",
        ref="refs/genome.fa"
    output:
        bam="results/{sample}.sorted.bam"
    threads: 4
    shell:
        "bwa mem -t {threads} {input.ref} {input.fastq} "
        "| samtools sort -@ {threads} -o {output.bam}"

snakemake -n            # dry-run
snakemake --cores 8     # execute

Wildcard constraints + multi-extension output

rule process:
    input:
        "data/{sample}_{rep}.fastq"
    output:
        "results/{sample}_{rep}.txt"
    wildcard_constraints:
        sample="[A-Za-z]+",
        rep="\d+"

rule bwa_index:
    input:  "refs/genome.fa"
    output: multiext("refs/genome.fa", ".amb", ".ann", ".bwt", ".pac", ".sa")
    shell:  "bwa index {input}"

Externalise configuration

# config/config.yaml
# samples: [ctrl, treat]
# threads: {align: 8, sort: 4}
# min_mapq: 20

configfile: "config/config.yaml"
SAMPLES = config["samples"]

rule filter_reads:
    input:  "results/{sample}.bam"
    output: "results/{sample}.filtered.bam"
    params: mapq=config["min_mapq"]
    threads: config["threads"]["sort"]
    shell:  "samtools view -q {params.mapq} -b {input} > {output}"

Per-rule resources (drives SLURM/LSF profiles)

rule variant_calling:
    input:
        bam="results/{sample}.deduped.bam",
        ref="refs/genome.fa"
    output:
        vcf="variants/{sample}.vcf.gz"
    resources:
        mem_mb=16000,
        runtime=240,
        disk_mb=20000
    threads: 8
    shell:
        "bcftools mpileup -f {input.ref} {input.bam} "
        "| bcftools call -m -Oz -o {output.vcf}"

Per-rule Conda environment

rule star_align:
    input:
        reads="data/{sample}.fastq",
        genome_dir="refs/star_index/"
    output:
        bam="star_out/{sample}/Aligned.sortedByCoord.out.bam"
    conda:  "envs/star.yaml"
    threads: 8
    shell:
        "STAR --runThreadN {threads} --genomeDir {input.genome_dir} "
        "--readFilesIn {input.reads} --outSAMtype BAM SortedByCoordinate"

Per-rule container (Docker/Apptainer)

rule gatk_haplotypecaller:
    input:
        bam="results/{sample}.bam",
        ref="refs/genome.fa"
    output:
        gvcf="gvcfs/{sample}.g.vcf.gz"
    container: "docker://broadinstitute/gatk:4.4.0.0"
    shell:
        "gatk HaplotypeCaller -I {input.bam} -R {input.ref} "
        "-O {output.gvcf} -ERC GVCF"

Reusable modules via configfile includes

# Snakefile
module qc:
    snakefile: "modules/qc/Snakefile"
    config: config

use rule * from qc as qc_*

Run as a module (publish to PyPI / share across projects)

# workflow/Snakefile — exported as `my_pipeline`
configfile: "config.yaml"
SAMPLES = config["samples"]

rule all:
    input: expand("out/{sample}.bam", sample=SAMPLES)

rule align:
    input:  "in/{sample}.fastq"
    output: "out/{sample}.bam"
    shell:  "bwa mem refs/genome.fa {input} | samtools view -b > {output}"

snakemake --module workdir my_pipeline       # install
# In another Snakefile:
# module my_pipeline:
#     snakefile: "workflow/Snakefile"

Special output types

rule sort_bam:
    input:  "results/{sample}.raw.bam"
    output: temp("results/{sample}.sorted_temp.bam")  # auto-deleted after consumers run
    shell:  "samtools sort {input} -o {output}"

rule final_report:
    input:  "results/{sample}.vcf.gz"
    output: protected("reports/{sample}.final.vcf.gz")  # write-protected
    shell:  "cp {input} {output}"

rule validate_bam:
    input:  "results/{sample}.bam"
    output: touch("checkpoints/{sample}.validated")  # empty flag
    shell:  "samtools quickcheck {input} && echo OK"

Auto-discover samples from disk

from pathlib import Path
SAMPLES = sorted(p.stem.replace(".fastq", "") for p in Path("data/").glob("*.fastq"))

rule all:
    input: expand("results/{sample}.bam", sample=SAMPLES)

Aggregation rule across all samples

rule multiqc:
    input:
        expand("qc/{sample}_fastqc.zip", sample=SAMPLES),
        expand("results/{sample}.flagstat.txt", sample=SAMPLES)
    output:
        "multiqc/multiqc_report.html"
    shell:  "multiqc qc/ results/ -o multiqc/"

SLURM profile (the high-value step)

mkdir -p profiles/slurm
cat > profiles/slurm/config.yaml <<'EOF'
executor: slurm
jobs: 100
default-resources:
  mem_mb: 4000
  runtime: 60
use-conda: true
use-apptainer: true
latency-wait: 30
rerun-incomplete: true
EOF

snakemake --profile profiles/slurm --cores 256 -n        # dry-run
snakemake --profile profiles/slurm --cores 256         # submit
snakemake --profile profiles/slurm --report report.html # post-run HTML report

Common pitfalls

• No rule all → Snakemake only runs the first rule. Always declare your final targets.
• Ambiguous wildcards (AmbiguousRuleException) → add wildcard_constraints: or use ruleorder.
• Missing outputs (MissingOutputException) → check the rule's shell: cwd and that all output paths are created.
• Cluster jobs OOM or timeout → increase mem_mb / runtime; check benchmark: to measure real use.
• Conda env build fails → add conda-forge before bioconda, pin versions.
• Re-running unexpectedly → output mtime older than input; snakemake --touch or delete and re-run.
• Protected output can't be overwritten → remove the file or drop protected().

Validation

• snakemake -n → dry-run shows the expected DAG; run before every production launch.
• snakemake --dag | dot -Tpdf > workflow_dag.pdf → visualise the DAG.
• snakemake --report report.html → post-run HTML with stats per rule.
• Per-rule benchmark: "benchmarks/{rule}/{sample}.txt" → real runtime and memory.

Open alternatives

References

• Snakemake docs: https://snakemake.readthedocs.io/
• Snakemake paper: Mölder et al. 2021, F1000Research 10:33, 10.12688/f1000research.29032.2
• Snakemake workflow catalog: https://snakemake.github.io/snakemake-workflow-catalog/
• nf-core (parallel Nextflow ecosystem): https://nf-co.re/
• Companion skill: ors-data-engineering-nextflow-workflow-engine
• Companion skill: ors-data-engineering-fair-data-principles (DVC integration)

Changelog

• 1.0.0 (2026-06-10): Initial adaptation by Pradyumna Jayaram from snakemake-workflow-engine (SciAgent-Skills-main/scientific-computing/snakemake-workflow-engine).

Cross-references

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