Can I use this for drug resistance research?

Yes, the skill provides specific access to drug resistance mutation data with clinical annotations to support precision medicine research.

What file formats can I download using this skill?

The skill facilitates the download and processing of TSV, CSV, and VCF formats, typically provided in gzip compressed format for efficiency.

Does this skill require a COSMIC account?

Yes, users must register at the COSMIC website. Academic access is free with registration, while commercial users require a license from QIAGEN.

Which genome assemblies are supported?

The skill supports both GRCh38 (the current standard) and GRCh37 (legacy) reference genomes for all mutation data.

How do I handle the large file sizes in COSMIC?

The included Python scripts provide progress bars for large downloads and use efficient streaming to handle files that can often reach several gigabytes.

COSMIC Cancer Database

Name: COSMIC Cancer Database
Author: K-Dense-AI

byK-Dense-AI

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2,188

Data Science & ML

Integrates the world's most comprehensive cancer mutation database into your research workflow to query somatic mutations, signatures, and gene census data.

About

The COSMIC Database skill provides seamless access to the Catalogue of Somatic Mutations in Cancer, the premier global resource for exploring human cancer genomics. It enables researchers and bioinformaticians to programmatically download and analyze curated datasets including the Cancer Gene Census, mutational signatures, structural variants, and drug resistance mutations. By bridging the gap between raw genomic data and actionable research insights, this skill streamlines precision oncology workflows and genomic pipeline integration directly within the Claude environment.

Key Features

Direct integration with Python data tools like Pandas and Pysam
2,188 GitHub stars
Access to expert-curated Cancer Gene Census for 700+ genes
Retrieval of mutational signature profiles including SBS, DBS, and ID
Support for both GRCh38 and GRCh37 genome assemblies
Programmatic download of comprehensive somatic mutation datasets

Use Cases

Analyzing mutational signatures to identify underlying cancer-causing processes
Automating cancer mutation data extraction for bioinformatics pipelines
Filtering genomic variants against known cancer gene lists for precision oncology

About

Key Features

Direct integration with Python data tools like Pandas and Pysam
2,188 GitHub stars
Access to expert-curated Cancer Gene Census for 700+ genes
Retrieval of mutational signature profiles including SBS, DBS, and ID
Support for both GRCh38 and GRCh37 genome assemblies
Programmatic download of comprehensive somatic mutation datasets

Use Cases

Analyzing mutational signatures to identify underlying cancer-causing processes
Automating cancer mutation data extraction for bioinformatics pipelines
Filtering genomic variants against known cancer gene lists for precision oncology