Skip to content
Development
Skill

/cosmic-database

Query COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants. REST API v3.1 supports gene/sample/variant queries; free registration. For germline use clinvar-database; for drug-target data use opentargets-database or

From plugin
sciagent-skills
384200 skills

Claude Code

$ npx -y skills add jaechang-hits/SciAgent-Skills --skill cosmic-database --agent claude-code

SKILL.md

cosmic-database.SKILL.md
name: "cosmic-database"
description: "Query COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants. REST API v3.1 supports gene/sample/variant queries; free registration. For germline use clinvar-database; for drug-target data use opentargets-database or chembl-database-bioactivity."
license: "CC-BY-NC-SA-4.0"

COSMIC Somatic Cancer Mutations Database

Overview

COSMIC (Catalogue Of Somatic Mutations In Cancer) is the world's largest expert-curated database of somatic mutations in cancer, covering 6.7M+ coding mutations, 40,000+ cancer samples, 19,000+ genes across all cancer types. It includes the Cancer Gene Census (critical cancer genes), mutational signatures (SBS, DBS, ID), drug resistance variants, copy number data, gene expression, and methylation. The REST API v3.1 enables programmatic queries; most features are freely accessible after registration.

When to Use

  • Checking whether a specific somatic variant in a cancer gene is annotated in COSMIC (frequency, cancer type distribution)
  • Retrieving all somatic mutations in a gene of interest across COSMIC cancer samples
  • Accessing COSMIC Cancer Gene Census classifications (Tier 1/2, role: oncogene/TSG/fusion)
  • Looking up mutational signature attributions for samples or cancer types
  • Identifying drug resistance variants (pharmacogenomic data) from COSMIC drug resistance database
  • Building cancer driver gene lists for bioinformatic pipelines
  • For germline/inherited variants use `clinvar-database`; for drug-target associations use `opentargets-database`

Prerequisites

  • **Python packages**: `requests`, `pandas`
  • **Data requirements**: gene symbols (HGNC), COSMIC mutation IDs (COSM), sample IDs, or genomic coordinates
  • **Environment**: internet connection; free account registration at https://cancer.sanger.ac.uk/cosmic/register
  • **Rate limits**: authenticated requests only; 10 requests/second max; API key required
pip install requests pandas
# Register at https://cancer.sanger.ac.uk/cosmic/register to obtain API credentials

Quick Start

import requests
import base64

# COSMIC API requires base64-encoded email:password authentication
EMAIL = "your_registered@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

# Get mutations for KRAS gene
r = requests.get(f"{BASE}/mutations",
                 headers=HEADERS,
                 params={"gene_name": "KRAS", "limit": 5})
r.raise_for_status()
data = r.json()
print(f"Total KRAS mutations: {data['meta']['total']}")
for m in data["data"][:3]:
    print(f"  {m['mutation_id']:15s} AA: {m.get('mutation_aa')} | Cancer: {m.get('primary_site')}")

Core API

Query 1: Gene Mutations Search

Retrieve all COSMIC somatic mutations for a gene, with cancer type and amino acid change.

import requests, base64, pandas as pd

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

def get_gene_mutations(gene, limit=100, cancer_site=None):
    params = {"gene_name": gene, "limit": limit}
    if cancer_site:
        params["primary_site"] = cancer_site
    r = requests.get(f"{BASE}/mutations", headers=HEADERS, params=params)
    r.raise_for_status()
    return r.json()

data = get_gene_mutations("TP53", limit=20)
print(f"Total TP53 mutations in COSMIC: {data['meta']['total']}")

rows = []
for m in data["data"][:10]:
    rows.append({
        "mutation_id": m.get("mutation_id"),
        "mutation_aa": m.get("mutation_aa"),
        "mutation_cds": m.get("mutation_cds"),
        "primary_site": m.get("primary_site"),
        "histology": m.get("primary_histology"),
        "count": m.get("count"),
    })
df = pd.DataFrame(rows)
print(df.head())
# Filter by cancer site
data_lung = get_gene_mutations("TP53", cancer_site="lung", limit=20)
print(f"\nTP53 mutations in lung cancer: {data_lung['meta']['total']}")

Query 2: Cancer Gene Census

Retrieve the COSMIC Cancer Gene Census — classified cancer driver genes.

import requests, base64, pandas as pd

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

r = requests.get(f"{BASE}/genes", headers=HEADERS, params={"limit": 100})
r.raise_for_status()
data = r.json()
print(f"Total genes in COSMIC: {data['meta']['total']}")

# Get Cancer Gene Census genes
r_cgc = requests.get(f"{BASE}/genes",
                     headers=HEADERS,
                     params={"cgc_tier": "1", "limit": 50})
cgc_data = r_cgc.json()
print(f"\nCGC Tier 1 genes: {cgc_data['meta']['total']}")

rows = []
for g in cgc_data["data"][:15]:
    rows.append({
        "gene": g.get("gene_name"),
        "tier": g.get("cgc_tier"),
        "role": g.get("role_in_cancer"),
        "mutation_types": g.get("mutation_types"),
        "tumour_types": str(g.get("tumour_types_somatic", []))[:80],
    })
df = pd.DataFrame(rows)
print(df.to_string(index=False))

Query 3: Specific Mutation Lookup

Retrieve details for a known COSMIC mutation ID (COSM…).

import requests, base64

EMAIL = "your@email.com"
PASSWORD = "your_password"
token = base64.b64encode(f"{EMAIL}:{PASSWORD}".encode()).decode()

BASE = "https://cancer.sanger.ac.uk/cosmic/api"
HEADERS = {"Authorization": f"Basic {token}"}

# KRAS G12D mutation
mutation_id = "COSM521"
r = requests.get(f"{BASE}/mutations/{mutation_id}", headers=HEADERS)
r.raise_for_status()
m = r.json()

print(f"Mutation ID : {m.get('mutation_id')}")
print(f"Gene        : {m.get('gene_name')}")
print(f"AA change   : {m.get('mutation_aa')}")
print(f"CDS change  :
Read more
@jaechang-hits
Ships withsciagent-skills

Turn your AI coding agent into a life sciences expert — 199 bioinformatics skills for Claude Code covering RNA-seq, single-cell analysis, genomics, proteomics, drug discovery, and more. Boosted BixBench from 65% to 92%. Open source.

Stars on GitHub

Momentum

stars in total
384
Star history of jaechang-hits/SciAgent-Skills on GitHub, a chart by star-history.com

Other skills on sciagent-skills.

More skills from other plugins.

brainstorming
Auto-invoked

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…

@obra@obraInstall Skill
ask-matt

ask-matt

Ask which skill or flow fits your situation. A router over the skills in this repo.

skills11d309k
@mattpocock@mattpocockInstall Skill
accessibility
Auto-invoked

accessibility

Design, implement, and audit accessible UI to WCAG 2.2 Level AA across Web, iOS, and Android — semantic ARIA roles and labels,…

ecc12d293k
@affaan-m@affaan-mInstall Skill
compiler-commit

compiler-commit

Use when you want to verify compiler changes and commit with the correct convention. Runs tests, lint, and format, then commits…

react2d252k
@facebook@facebookInstall Skill

Questions about cosmic-database.

Which coding agents can use cosmic-database?
cosmic-database installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.