Which genome assemblies are supported?

The cosmic-database skill supports both the current GRCh38 standard and the legacy GRCh37 assembly for all major data types.

Can I filter data for specific genes like TP53?

Yes, the skill includes Python implementation patterns using pandas to filter downloaded mutation data by gene name, sample ID, or tissue type.

What types of mutational signatures can I download?

You can retrieve the latest definitions for Single Base Substitution (SBS), Doublet Base Substitution (DBS), and Insertion/Deletion (ID) signatures.

Is a COSMIC account required to use this skill?

Yes, users must register for an account at cancer.sanger.ac.uk/cosmic. Access is free for academic users, while commercial users require a license from QIAGEN.

What file formats does this skill handle?

The skill supports standard bioinformatics formats including TSV and CSV (often gzip compressed) for tabular data, and VCF for variant records.

Cosmic Cancer Database Access

Name: Cosmic Cancer Database Access
Author: Microck

byMicrock

•

Data Science & ML

Accesses the COSMIC cancer mutation database to retrieve somatic mutations, curated gene lists, and mutational signatures for precision oncology research.

About

This skill empowers researchers and bioinformaticians to programmatically access the world's most comprehensive database of somatic mutations in human cancer. It streamlines the retrieval of high-quality genomic data, including the Cancer Gene Census, mutational signatures, structural variants, and drug resistance information. By automating the download and filtering of large-scale datasets across GRCh38 and GRCh37 assemblies, it enables seamless integration of cancer genomics into bioinformatics pipelines, helping users identify oncogenic drivers and analyze mutational patterns with precision.

Key Features

Access to drug resistance mutations and clinical annotations
81 GitHub stars
Support for both GRCh38 and GRCh37 genome assemblies
Automated download of somatic mutation data and Cancer Gene Census lists
Helper functions for filtering mutations by gene, primary site, or cancer type
Retrieval of mutational signature profiles including SBS, DBS, and ID

Use Cases

Prioritizing variants by cross-referencing research data with the Cancer Gene Census
Analyzing mutational signatures to identify underlying carcinogenic processes
Building bioinformatics pipelines that integrate clinical drug resistance data

About

Key Features

Access to drug resistance mutations and clinical annotations
81 GitHub stars
Support for both GRCh38 and GRCh37 genome assemblies
Automated download of somatic mutation data and Cancer Gene Census lists
Helper functions for filtering mutations by gene, primary site, or cancer type
Retrieval of mutational signature profiles including SBS, DBS, and ID

Use Cases

Prioritizing variants by cross-referencing research data with the Cancer Gene Census
Analyzing mutational signatures to identify underlying carcinogenic processes
Building bioinformatics pipelines that integrate clinical drug resistance data