cosmic-database

Retrieve COSMIC cancer mutation data via authenticated Python workflows.

33.0k|3.2k|Updated Oct 19, 2025
One-click install
npx skills add https://github.com/K-Dense-AI/claude-scientific-skills --skill cosmic-database
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: cosmic-database
Source: https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-databases/cosmic-database
Command: npx skills add https://github.com/K-Dense-AI/claude-scientific-skills --skill cosmic-database

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, pandas, pysam, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Manually navigating and downloading vast cancer mutation data from COSMIC is complex and time-consuming. This Skill automates authenticated access to COSMIC, streamlining cancer research, precision oncology, and bioinformatics pipelines by providing rapid access to critical genomic data.

Core Features & Use Cases

  • Mutation Data Download: Retrieve comprehensive somatic mutations (SNVs, indels), structural variants, and gene fusions in TSV, CSV, or VCF formats.
  • Cancer Gene Census: Access the expert-curated list of ~700+ cancer genes, including their roles (oncogene, tumor suppressor) and associated mutation types.
  • Mutational Signatures: Download signature profiles (SBS, DBS, ID) for advanced mutational signature analysis, aiding in understanding cancer etiology.
  • Use Case: Automatically download the latest COSMIC mutation data for a specific gene (e.g., TP53) and filter it by lung cancer samples, providing immediate, actionable insights for targeted therapy research.

Quick Start

Download the Cancer Gene Census (replace with your COSMIC credentials)

from scripts.download_cosmic import download_cosmic_file download_cosmic_file( email="[email protected]", password="your_password", filepath="GRCh38/cosmic/latest/cancer_gene_census.csv", output_filename="cancer_gene_census.csv" )

Read the downloaded data with pandas

import pandas as pd gene_census = pd.read_csv('cancer_gene_census.csv') print(gene_census.head())

Frequently Asked Questions about cosmic-database

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I download COSMIC cancer mutation data programmatically?

Download COSMIC cancer mutation data using authenticated Python scripts that retrieve somatic variants, structural variants, and other genomic data in TSV, CSV, or VCF formats for integration into bioinformatics pipelines. Requires COSMIC account credentials and genome assembly selection (GRCh38 or GRCh37).

What is the Cancer Gene Census and how do I access it?

The Cancer Gene Census is an expert-curated list of ~700+ cancer genes with their roles (oncogene, tumor suppressor) and associated mutation types. Access it programmatically through authenticated COSMIC retrieval to integrate cancer genomics data into precision oncology workflows.

Can I filter COSMIC mutations by cancer type and gene for targeted therapy research?

Yes. Retrieve COSMIC mutation data for specific genes and filter by cancer samples programmatically, then process with pandas to extract actionable insights for targeted therapy research and precision oncology applications.

What genome assemblies and data versions does COSMIC support?

COSMIC supports GRCh38 and GRCh37 genome assemblies with versioning options for latest or specific releases. Select your assembly and version when configuring authenticated data retrieval for somatic variants, mutational signatures, and other genomic datasets.

What input formats and dependencies do I need for COSMIC data analysis?

Use Python with requests, pandas, and pysam libraries to retrieve and process COSMIC data. Outputs are TSV, CSV, or VCF files that integrate directly into bioinformatics pipelines for cancer genomics analysis.

How do I use mutational signatures for cancer etiology analysis?

Download mutational signature profiles (SBS, DBS, ID) from COSMIC to analyze cancer etiology and underlying mutational processes. Process signatures programmatically to understand driver mechanisms in precision oncology.