Agent skill

bio-workflow-management-snakemake-workflows

View SKILL.md on GitHub Repository
Stars 2,009
Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-workflow-management-snakemake-workflows

SKILL.md

name: bio-workflow-management-snakemake-workflows description: Build reproducible bioinformatics pipelines with Snakemake using rules, wildcards, and automatic dependency resolution. Use when creating Python-based workflows, automating multi-step analyses with make-like dependency tracking, or running pipelines on HPC clusters with SLURM. tool_type: python primary_tool: Snakemake measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

Snakemake Workflows

Compatible with Snakemake 7.x, 8.x, and 9.x. For Snakemake 8.0+, use --executor instead of --cluster.

Basic Rule Structure

python
# Snakefile

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

rule align:
    input:
        r1 = "data/{sample}_R1.fq.gz",
        r2 = "data/{sample}_R2.fq.gz",
        index = "ref/genome.fa"
    output:
        bam = "aligned/{sample}.bam"
    threads: 8
    shell:
        "bwa mem -t {threads} {input.index} {input.r1} {input.r2} | "
        "samtools sort -@ {threads} -o {output.bam}"

Config File

yaml
# config.yaml
samples:
  - sample1
  - sample2
  - sample3

reference: "ref/genome.fa"
threads: 8
python
# Snakefile
configfile: "config.yaml"

SAMPLES = config["samples"]
REF = config["reference"]

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

Wildcards and Expand

python
# Define samples
SAMPLES = ["A", "B", "C"]
CHROMOSOMES = [str(i) for i in range(1, 23)] + ["X", "Y"]

# Expand for all combinations
rule all:
    input:
        expand("results/{sample}_{chrom}.vcf", sample=SAMPLES, chrom=CHROMOSOMES)

# Access wildcard in rule
rule process:
    input:
        "data/{sample}.bam"
    output:
        "results/{sample}.vcf"
    params:
        sample = lambda wildcards: wildcards.sample
    shell:
        "bcftools call -s {params.sample} {input} > {output}"

Python Integration

python
rule analyze:
    input:
        counts = "data/{sample}_counts.txt"
    output:
        results = "results/{sample}_de.csv"
    run:
        import pandas as pd
        counts = pd.read_csv(input.counts, sep='\t')
        # Process data
        results = counts.groupby('gene').sum()
        results.to_csv(output.results)

Conda Environments

python
rule fastqc:
    input:
        "data/{sample}.fq.gz"
    output:
        "qc/{sample}_fastqc.html"
    conda:
        "envs/qc.yaml"
    shell:
        "fastqc {input} -o qc/"
yaml
# envs/qc.yaml
channels:
  - bioconda
  - conda-forge
dependencies:
  - fastqc=0.12.1
  - multiqc=1.14

Container Support

python
rule align:
    input:
        r1 = "data/{sample}_R1.fq.gz"
    output:
        bam = "aligned/{sample}.bam"
    container:
        "docker://biocontainers/bwa:v0.7.17"
    shell:
        "bwa mem {input} | samtools sort -o {output}"
bash
# Run with Singularity
snakemake --use-singularity --singularity-args "-B /data"

Resource Management

python
rule memory_intensive:
    input:
        "data/{sample}.bam"
    output:
        "results/{sample}.vcf"
    threads: 4
    resources:
        mem_mb = 16000,
        time = "4:00:00",
        gpu = 1
    shell:
        "variant_caller --threads {threads} {input} > {output}"

Cluster Execution

yaml
# cluster.yaml
__default__:
    partition: normal
    time: "1:00:00"
    mem: 4G

align:
    partition: normal
    time: "8:00:00"
    mem: 32G
    cpus-per-task: 8
bash
# Snakemake 8.x with executor plugin
pip install snakemake-executor-plugin-slurm
snakemake --executor slurm --jobs 100

# Snakemake 7.x cluster execution
snakemake --cluster "sbatch --partition={cluster.partition} \
    --time={cluster.time} --mem={cluster.mem}" \
    --cluster-config cluster.yaml --jobs 100

# Or use profile (both versions)
snakemake --profile slurm

Checkpoints

python
# For rules that determine downstream files dynamically
checkpoint split_samples:
    input:
        "data/all_samples.txt"
    output:
        directory("split/")
    shell:
        "split_script.py {input} {output}"

def get_split_files(wildcards):
    checkpoint_output = checkpoints.split_samples.get(**wildcards).output[0]
    return expand("split/{sample}.txt", sample=glob_wildcards("split/{sample}.txt").sample)

rule aggregate:
    input:
        get_split_files
    output:
        "results/aggregated.txt"
    shell:
        "cat {input} > {output}"

Modular Workflows

python
# Snakefile
include: "rules/qc.smk"
include: "rules/align.smk"
include: "rules/call.smk"

rule all:
    input:
        rules.qc_all.input,
        rules.call_all.input
python
# rules/qc.smk
rule fastqc:
    input: "data/{sample}.fq.gz"
    output: "qc/{sample}_fastqc.html"
    shell: "fastqc {input} -o qc/"

rule qc_all:
    input: expand("qc/{sample}_fastqc.html", sample=SAMPLES)

Temporary and Protected Files

python
rule align:
    input:
        "data/{sample}.fq.gz"
    output:
        bam = temp("aligned/{sample}.unsorted.bam"),  # Auto-deleted
        sorted = protected("aligned/{sample}.bam")     # Write-protected
    shell:
        "bwa mem {input} > {output.bam} && "
        "samtools sort {output.bam} -o {output.sorted}"

Benchmarking

python
rule heavy_computation:
    input:
        "data/{sample}.bam"
    output:
        "results/{sample}.txt"
    benchmark:
        "benchmarks/{sample}.tsv"
    shell:
        "heavy_tool {input} > {output}"

Logging

python
rule process:
    input:
        "data/{sample}.bam"
    output:
        "results/{sample}.txt"
    log:
        "logs/{sample}.log"
    shell:
        "(command {input} > {output}) 2> {log}"

Run Commands

bash
# Dry run (show what would be executed)
snakemake -n

# Run with 8 cores
snakemake --cores 8

# Force rerun of specific rule
snakemake --forcerun align

# Run until specific target
snakemake results/sample1.vcf

# Generate DAG visualization
snakemake --dag | dot -Tpdf > dag.pdf

# Generate report
snakemake --report report.html

Complete RNA-seq Workflow

python
configfile: "config.yaml"

SAMPLES = config["samples"]

rule all:
    input:
        "results/multiqc_report.html",
        "results/deseq2_results.csv"

rule fastp:
    input:
        r1 = "data/{sample}_R1.fq.gz",
        r2 = "data/{sample}_R2.fq.gz"
    output:
        r1 = "trimmed/{sample}_R1.fq.gz",
        r2 = "trimmed/{sample}_R2.fq.gz",
        json = "qc/{sample}_fastp.json"
    threads: 4
    shell:
        "fastp -i {input.r1} -I {input.r2} -o {output.r1} -O {output.r2} "
        "--json {output.json} --thread {threads}"

rule salmon_quant:
    input:
        r1 = "trimmed/{sample}_R1.fq.gz",
        r2 = "trimmed/{sample}_R2.fq.gz",
        index = config["salmon_index"]
    output:
        directory("salmon/{sample}")
    threads: 8
    shell:
        "salmon quant -i {input.index} -l A -1 {input.r1} -2 {input.r2} "
        "-o {output} --threads {threads}"

rule multiqc:
    input:
        expand("qc/{sample}_fastp.json", sample=SAMPLES),
        expand("salmon/{sample}", sample=SAMPLES)
    output:
        "results/multiqc_report.html"
    shell:
        "multiqc qc/ salmon/ -o results/"

rule deseq2:
    input:
        quants = expand("salmon/{sample}", sample=SAMPLES),
        metadata = config["metadata"]
    output:
        "results/deseq2_results.csv"
    script:
        "scripts/deseq2.R"

Related Skills

  • workflow-management/nextflow-pipelines - Nextflow alternative
  • workflows/rnaseq-to-de - RNA-seq workflow
  • read-qc/fastp-workflow - QC in pipelines

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

FreedomIntelligence/OpenClaw-Medical-Skills

vcf-annotator

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

chemist-analyst

Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-alignment-io

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

sleep-analyzer

分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

2,009 275
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results