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Skillsalterlab-cosmic

alterlab-cosmic

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Updated 10-15 19:56

About this skill

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

Does NOT Trigger

Scenario Use Instead
Germline variant pathogenicity (ACMG/AMP, ClinVar stars) alterlab-clinvar
Mutation frequency / OncoPrint / survival in TCGA or MSK cohorts via a keyless API alterlab-cbioportal
CRISPR/RNAi gene dependency in cancer cell lines alterlab-depmap
Population allele frequencies in non-cancer cohorts alterlab-gnomad

Prerequisites

Account Registration

COSMIC requires authentication for data downloads:

Python Requirements

uv pip install requests pandas
# pysam is only needed if you read the VCF-format downloads
uv pip install pysam

Quick Start

COSMIC's current download service delivers each product as a .tar archive (the gzipped TSV or VCF plus a README describing every column) at an explicit release path, e.g. grch38/cosmic/v104/Cosmic_GenomeScreensMutant_Tsv_v104_GRCh38.tar. Scripted downloads are a two-step call: GET https://cancer.sanger.ac.uk/api/mono/products/v1/downloads/scripted?path=<archive path>&bucket=downloads with HTTP Basic auth (email:password) returns JSON with a signed url, which you then fetch without auth. The legacy /cosmic/file_download/... endpoint and legacy names such as CosmicMutantExport.tsv.gz or cancer_gene_census.csv no longer work for scripts — the old endpoint now redirects to the login page.

1. Basic File Download

Use the scripts/download_cosmic.py script to download COSMIC data files:

from scripts.download_cosmic import download_cosmic_file, get_common_file_path

# Cancer Gene Census, current release (v104), GRCh38
download_cosmic_file(
    email="your_email@institution.edu",
    password="your_password",
    filepath=get_common_file_path("gene_census"),
    # = "grch38/cosmic/v104/Cosmic_CancerGeneCensus_Tsv_v104_GRCh38.tar"
)

2. Command-Line Usage

# Download using shorthand data type (prompts for the password)
python scripts/download_cosmic.py user@email.com --data-type mutations

# Download a specific archive path
python scripts/download_cosmic.py user@email.com \
    --filepath grch38/cosmic/v104/Cosmic_CancerGeneCensus_Tsv_v104_GRCh38.tar

# GRCh37 and/or a pinned release
python scripts/download_cosmic.py user@email.com \
    --data-type gene_census --assembly GRCh37 --version v103

If the scripted endpoint changes, copy the command shown under Scripted download for any file on https://cancer.sanger.ac.uk/cosmic/download/cosmic and set COSMIC_SCRIPTED_URL.

3. Working with Downloaded Data

tar -xf Cosmic_CancerGeneCensus_Tsv_v104_GRCh38.tar   # -> gzipped TSV + README

import glob
import pandas as pd

# Column names differ from the legacy exports — check the README in each archive.
gene_census = pd.read_csv(glob.glob("Cosmic_CancerGeneCensus*GRCh38*.tsv.gz")[0], sep="\t")
print(gene_census.columns.tolist())

# VCF products (e.g. VCF/Cosmic_GenomeScreensMutant_Vcf_...) extract to .vcf.gz files
import pysam
vcf = pysam.VariantFile(glob.glob("Cosmic_GenomeScreensMutant*_GRCh38.vcf.gz")[0])

Available Data Types

Every data type downloads through the same download_cosmic_file(...) call shown in Quick Start — only the filepath changes. Use the --data-type shortcut (CLI) or get_common_file_path(...) (Python) to build the path, or pass the filepath directly. See references/cosmic_data_reference.md for full field descriptions.

Data type Shortcut Archive (grch38/cosmic/v104/…, verified 2026-09)
Coding mutations, genome-wide screens (WGS/WES) mutations Cosmic_GenomeScreensMutant_Tsv_v104_GRCh38.tar
Coding mutations, targeted screens targeted_mutations Cosmic_CompleteTargetedScreensMutant_Tsv_v104_GRCh38.tar
Coding mutations (VCF) mutations_vcf VCF/Cosmic_GenomeScreensMutant_Vcf_v104_GRCh38.tar
Non-coding variants (VCF) non_coding_vcf VCF/Cosmic_NonCodingVariants_Vcf_v104_GRCh38.tar
Mutations in CGC genes mutation_census Cosmic_MutantCensus_Tsv_v104_GRCh38.tar
Cancer Gene Census gene_census Cosmic_CancerGeneCensus_Tsv_v104_GRCh38.tar
Resistance mutations resistance_mutations Cosmic_ResistanceMutations_Tsv_v104_GRCh38.tar
Structural variants / breakpoints structural_variants / breakpoints Cosmic_StructuralVariants_Tsv_… / Cosmic_Breakpoints_Tsv_…
Gene fusions fusion_genes Cosmic_Fusion_Tsv_v104_GRCh38.tar
Copy number copy_number Cosmic_CompleteCNA_Tsv_v104_GRCh38.tar
Gene expression gene_expression Cosmic_CompleteGeneExpression_Tsv_v104_GRCh38.tar
Samples / tumour classification sample_info / classification Cosmic_Sample_Tsv_… / Cosmic_Classification_Tsv_…
Mutational signatures signatures separate site — https://cancer.sanger.ac.uk/signatures/downloads/

Notes:

  • Cancer Gene Census is the expert-curated list of cancer genes; its role-in-cancer field splits oncogenes from tumor suppressors (TSG), and Tier 1/2 grades the evidence.
  • The old single "all coding mutations" export is now split into genome-wide and targeted-screen files; combine both for full coverage.
  • Mutational signatures (SBS, DBS, ID, CN, SV; current reference set v3.6, May 2026) are downloaded from the signatures site, not through the product archives.
  • Each product page lists sha256/md5 checksums — verify large downloads.

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 (lower-case directory, upper-case suffix):

# GRCh38 (recommended)
filepath = "grch38/cosmic/v104/Cosmic_GenomeScreensMutant_Tsv_v104_GRCh38.tar"

# GRCh37 (legacy)
filepath = "grch37/cosmic/v104/Cosmic_GenomeScreensMutant_Tsv_v104_GRCh37.tar"

Versioning

  • Archive paths carry an explicit release (v104 = May 2026); the download service lists only versioned paths, so pin one — get_common_file_path() defaults to the current release
  • COSMIC ships two releases a year (May and November: v101 2024-11, v102 2025-05, v103 2025-11, v104 2026-05); check the release notes before assuming
  • For reproducible research, pin the release and record it alongside your results

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

Current files use upper-case column names (e.g. GENE_SYMBOL, SAMPLE_NAME), while tumour site/histology live in the sample/classification tables linked by COSMIC IDs. Confirm exact names in each archive's README before filtering. For a one-off slice (one gene, primary site, or sample) the web Filtered download option avoids pulling the multi-GB files at all.

Filter mutations by gene:

import glob
import pandas as pd

# Extracted from Cosmic_GenomeScreensMutant_Tsv_v104_GRCh38.tar (multi-GB)
tsv = glob.glob('Cosmic_GenomeScreensMutant*GRCh38*.tsv.gz')[0]
mutations = pd.read_csv(tsv, sep='\t', low_memory=False)
tp53_mutations = mutations[mutations['GENE_SYMBOL'] == 'TP53']

Identify cancer genes by role (normalize headers, then look up the role column):

cgc = pd.read_csv(glob.glob('Cosmic_CancerGeneCensus*GRCh38*.tsv.gz')[0], sep='\t')
cgc.columns = cgc.columns.str.upper().str.replace(' ', '_')
role = cgc['ROLE_IN_CANCER'].fillna('')
oncogenes = cgc[role.str.contains('oncogene')]
tumor_suppressors = cgc[role.str.contains('TSG')]

Work with VCF files (GRCh38 coordinates, bgzip + tabix index required for fetch):

import pysam

vcf = pysam.VariantFile(glob.glob('Cosmic_GenomeScreensMutant*GRCh38*.vcf.gz')[0])
for record in vcf.fetch('17', 7668400, 7687500):  # TP53 locus, GRCh38
    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/v104/Cosmic_GenomeScreensMutant_Tsv_v104_GRCh38.tar'

# Get path for gene census, pinned to an older release
path = get_common_file_path('gene_census', version='v103')
# Returns: 'grch38/cosmic/v103/Cosmic_CancerGeneCensus_Tsv_v103_GRCh38.tar'

The accepted data_type shortcuts are the ones in the Available Data Types table above (signatures returns None — use the signatures download site).

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 / HTTP 400

  • Verify the archive path against the download page (release, product name, assembly)
  • Check that the requested release exists (v101–v104 are listed as of 2026-09)
  • Legacy names (CosmicMutantExport.tsv.gz, cancer_gene_census.csv) and GRCh38/cosmic/latest/... paths from older tutorials are not in the current service
  • 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 R&D, commercial products/services, and patient services or clinical reporting require a COSMIC commercial licence: https://www.cosmickb.org/licensing
  • Academic (not-for-profit) 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

Citation

When using COSMIC data, cite the current database paper: Sondka Z, Dhir NB, Carvalho-Silva D, et al. COSMIC: a curated database of somatic variants and clinical data for cancer. Nucleic Acids Research. 2024;52(D1):D1210-D1217. doi:10.1093/nar/gkad986