Data & ReproClinical MedicineFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-clinical-databases-somatic-signatures

Maintainer FreedomIntelligence · Last updated April 1, 2026

Extract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.

OpenClawNanoClawAnalysisReproductionbio-clinical-databases-somatic-signatures🧬 bioinformatics (gptomics bio-* suite)bioinformatics — clinical databases & variant analysisextract

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-clinical-databases-somatic-signatures

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Python: SigProfilerExtractor.sigpro() for de novo signature extraction.
R: MutationalPatterns::fit_to_signatures() for fitting to COSMIC signatures.
Extract mutational signatures from my tumor samples" → Decompose somatic mutation catalogs into mutational signatures (SBS, DBS, ID) to identify DNA damage mechanisms and mutagenic processes in cancer genomes. Python: SigProfilerExtractor.sigpro() for de novo signature extraction R: MutationalPatterns::fit_to_signatures() for fitting to COSMIC signatures.
genInstall.install('GRCh38').

Source Doc

Excerpt From SKILL.md

SigProfiler Workflow

Goal: Extract de novo mutational signatures and decompose to COSMIC reference signatures from somatic VCFs.

Approach: Generate a 96-trinucleotide-context mutation matrix with SigProfilerMatrixGenerator, extract signatures via NMF with SigProfilerExtractor, and fit to COSMIC with SigProfilerAssignment.

Install and Generate Matrix

from SigProfilerMatrixGenerator import install as genInstall
from SigProfilerMatrixGenerator.scripts import SigProfilerMatrixGeneratorFunc as matGen

## Output: SBS96 matrix (96 trinucleotide contexts)

matrices = matGen.SigProfilerMatrixGeneratorFunc(
    project='my_project',
    genome='GRCh38',
    vcfFiles='/path/to/vcf_directory',
    plot=True,
    exome=False  # Set True for WES
)