Is this skill suitable for commercial research?

Yes, provided your organization has the appropriate license. Commercial users must license COSMIC through QIAGEN before using this integration.

Do I need an account to use the COSMIC database skill?

Yes, COSMIC requires authentication. Academic users can register for free at the COSMIC website, while commercial users require a license from QIAGEN.

How is the data formatted for analysis?

Data is typically provided in TSV, CSV, or VCF formats, often gzip-compressed, making it fully compatible with Python tools like pandas, pysam, and standard bioinformatics pipelines.

Which genome assemblies are supported?

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

Can I download specific types of cancer data?

Yes, you can specifically download data for mutations, gene fusions, copy number alterations, resistance mutations, and mutational signatures using shorthand data types.

COSMIC Cancer Database Integration

Name: COSMIC Cancer Database Integration
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Accesses and analyzes somatic mutation data from the world's largest cancer genomics database for precision oncology research.

About

This skill enables Claude to interact with the Catalogue of Somatic Mutations in Cancer (COSMIC), the most comprehensive global resource for cancer genomics. It provides programmatic access to curated gene lists via the Cancer Gene Census, mutational signatures, structural variants, and drug resistance data. By automating the download and processing of large-scale genomic datasets, this tool streamlines bioinformatics workflows, allowing researchers to integrate high-quality somatic mutation data directly into their analysis pipelines and precision medicine studies.

Key Features

Analysis of mutational signatures (SBS, DBS, ID)
3 GitHub stars
Integration with pandas and VCF processing tools
Support for GRCh38 and GRCh37 genome assemblies
Programmatic access to millions of somatic mutations
Retrieval of expert-curated Cancer Gene Census data

Use Cases

Extracting mutational signature profiles for tumor etiology research
Identifying known cancer-driving mutations in patient sequencing data
Filtering genomic variants using the Cancer Gene Census for precision oncology

About

Key Features

Analysis of mutational signatures (SBS, DBS, ID)
3 GitHub stars
Integration with pandas and VCF processing tools
Support for GRCh38 and GRCh37 genome assemblies
Programmatic access to millions of somatic mutations
Retrieval of expert-curated Cancer Gene Census data

Use Cases

Extracting mutational signature profiles for tumor etiology research
Identifying known cancer-driving mutations in patient sequencing data
Filtering genomic variants using the Cancer Gene Census for precision oncology