COSMIC Database Overview
COSMIC (Catalogue of Somatic Mutations in Cancer) is the world's largest and most comprehensive database for exploring somatic mutations in human cancer. Access COSMIC's extensive collection of cancer genomics data, including millions of mutations across thousands of cancer types, curated gene lists, mutational signatures, and clinical annotations programmatically.
When to Use This Skill
This skill should be used when:
Downloading cancer mutation data from COSMIC Accessing the Cancer Gene Census for curated cancer gene lists Retrieving mutational signature profiles Querying structural variants, copy number alterations, or gene fusions Analyzing drug resistance mutations Working with cancer cell line genomics data Integrating cancer mutation data into bioinformatics pipelines Researching specific genes or mutations in cancer contexts Prerequisites Account Registration
COSMIC requires authentication for data downloads:
Academic users: Free access with registration at https://cancer.sanger.ac.uk/cosmic/register Commercial users: License required (contact QIAGEN) Python Requirements uv pip install requests pandas
Quick Start 1. Basic File Download
Use the scripts/download_cosmic.py script to download COSMIC data files:
from scripts.download_cosmic import download_cosmic_file
Download mutation data
download_cosmic_file( email="your_email@institution.edu", password="your_password", filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz", output_filename="cosmic_mutations.tsv.gz" )
- Command-Line Usage
Download using shorthand data type
python scripts/download_cosmic.py user@email.com --data-type mutations
Download specific file
python scripts/download_cosmic.py user@email.com \ --filepath GRCh38/cosmic/latest/cancer_gene_census.csv
Download for specific genome assembly
python scripts/download_cosmic.py user@email.com \ --data-type gene_census --assembly GRCh37 -o cancer_genes.csv
- Working with Downloaded Data import pandas as pd
Read mutation data
mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')
Read Cancer Gene Census
gene_census = pd.read_csv('cancer_gene_census.csv')
Read VCF format
import pysam vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz')
Available Data Types Core Mutations
Download comprehensive mutation data including point mutations, indels, and genomic annotations.
Common data types:
mutations - Complete coding mutations (TSV format) mutations_vcf - Coding mutations in VCF format sample_info - Sample metadata and tumor information
Download all coding mutations
download_cosmic_file( email="user@email.com", password="password", filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz" )
Cancer Gene Census
Access the expert-curated list of ~700+ cancer genes with substantial evidence of cancer involvement.
Download Cancer Gene Census
download_cosmic_file( email="user@email.com", password="password", filepath="GRCh38/cosmic/latest/cancer_gene_census.csv" )
Use cases:
Identifying known cancer genes Filtering variants by cancer relevance Understanding gene roles (oncogene vs tumor suppressor) Target gene selection for research Mutational Signatures
Download signature profiles for mutational signature analysis.
Download signature definitions
download_cosmic_file( email="user@email.com", password="password", filepath="signatures/signatures.tsv" )
Signature types:
Single Base Substitution (SBS) signatures Doublet Base Substitution (DBS) signatures Insertion/Deletion (ID) signatures Structural Variants and Fusions
Access gene fusion data and structural rearrangements.
Available data types:
structural_variants - Structural breakpoints fusion_genes - Gene fusion events
Download gene fusions
download_cosmic_file( email="user@email.com", password="password", filepath="GRCh38/cosmic/latest/CosmicFusionExport.tsv.gz" )
Copy Number and Expression
Retrieve copy number alterations and gene expression data.
Available data types:
copy_number - Copy number gains/losses gene_expression - Over/under-expression data
Download copy number data
download_cosmic_file( email="user@email.com", password="password", filepath="GRCh38/cosmic/latest/CosmicCompleteCNA.tsv.gz" )
Resistance Mutations
Access drug resistance mutation data with clinical annotations.
Download resistance mutations
download_cosmic_file( email="user@email.com", password="password", filepath="GRCh38/cosmic/latest/CosmicResistanceMutations.tsv.gz" )
Working with COSMIC Data Genome Assemblies
COSMIC provides data for two reference genomes:
GRCh38 (recommended, current standard) GRCh37 (legacy, for older pipelines)
Specify the assembly in file paths:
GRCh38 (recommended)
filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz"
GRCh37 (legacy)
filepath="GRCh37/cosmic/latest/CosmicMutantExport.tsv.gz"
Versioning Use latest in file paths to always get the most recent release COSMIC is updated quarterly (current version: v102, May 2025) Specific versions can be used for reproducibility: v102, v101, etc. File Formats TSV/CSV: Tab/comma-separated, gzip compressed, read with pandas VCF: Standard variant format, use with pysam, bcftools, or GATK All files include headers describing column contents Common Analysis Patterns
Filter mutations by gene:
import pandas as pd
mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip') tp53_mutations = mutations[mutations['Gene name'] == 'TP53']
Identify cancer genes by role:
gene_census = pd.read_csv('cancer_gene_census.csv') oncogenes = gene_census[gene_census['Role in Cancer'].str.contains('oncogene', na=False)] tumor_suppressors = gene_census[gene_census['Role in Cancer'].str.contains('TSG', na=False)]
Extract mutations by cancer type:
mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip') lung_mutations = mutations[mutations['Primary site'] == 'lung']
Work with VCF files:
import pysam
vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz') for record in vcf.fetch('17', 7577000, 7579000): # TP53 region print(record.id, record.ref, record.alts, record.info)
Data Reference
For comprehensive information about COSMIC data structure, available files, and field descriptions, see references/cosmic_data_reference.md. This reference includes:
Complete list of available data types and files Detailed field descriptions for each file type File format specifications Common file paths and naming conventions Data update schedule and versioning Citation information
Use this reference when:
Exploring what data is available in COSMIC Understanding specific field meanings Determining the correct file path for a data type Planning analysis workflows with COSMIC data Helper Functions
The download script includes helper functions for common operations:
Get Common File Paths from scripts.download_cosmic import get_common_file_path
Get path for mutations file
path = get_common_file_path('mutations', genome_assembly='GRCh38')
Returns: 'GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz'
Get path for gene census
path = get_common_file_path('gene_census')
Returns: 'GRCh38/cosmic/latest/cancer_gene_census.csv'
Available shortcuts:
mutations - Core coding mutations mutations_vcf - VCF format mutations gene_census - Cancer Gene Census resistance_mutations - Drug resistance data structural_variants - Structural variants gene_expression - Expression data copy_number - Copy number alterations fusion_genes - Gene fusions signatures - Mutational signatures sample_info - Sample metadata Troubleshooting Authentication Errors Verify email and password are correct Ensure account is registered at cancer.sanger.ac.uk/cosmic Check if commercial license is required for your use case File Not Found Verify the filepath is correct Check that the requested version exists Use latest for the most recent version Confirm genome assembly (GRCh37 vs GRCh38) is correct Large File Downloads COSMIC files can be several GB in size Ensure sufficient disk space Download may take several minutes depending on connection The script shows download progress for large files Commercial Use Commercial users must license COSMIC through QIAGEN Contact: cosmic-translation@sanger.ac.uk Academic access is free but requires registration Integration with Other Tools
COSMIC data integrates well with:
Variant annotation: VEP, ANNOVAR, SnpEff Signature analysis: SigProfiler, deconstructSigs, MuSiCa Cancer genomics: cBioPortal, OncoKB, CIViC Bioinformatics: Bioconductor, TCGA analysis tools Data science: pandas, scikit-learn, PyTorch Additional Resources COSMIC Website: https://cancer.sanger.ac.uk/cosmic Documentation: https://cancer.sanger.ac.uk/cosmic/help Release Notes: https://cancer.sanger.ac.uk/cosmic/release_notes Contact: cosmic@sanger.ac.uk Citation
When using COSMIC data, cite: Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research. 2019;47(D1):D941-D947.