# Alterlab Cosmic

> Access the COSMIC catalogue of somatic mutations in cancer to query somatic mutations, the Cancer Gene Census, mutational signatures, and gene fusions (authentication required). Use when curating known cancer driver genes, looking up recurrent somatic mutations in a gene, or interpreting mutational signatures for cancer research and precision oncology. Not for germline pathogenicity calls (use alterlab-clinvar) or interactive cohort visualization like OncoPrints and survival from study data (use alterlab-cbioportal). Part of the AlterLab Academic Skills suite.

- Skill: `alterlab-ieu/alterlab-cosmic` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add alterlab-ieu/alterlab-cosmic`
- Raw SKILL.md: https://api.skillmd.com/api/skills/alterlab-ieu/alterlab-cosmic/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- License: MIT
- Author: AlterLab-IEU (https://skillmd.com/u/alterlab-ieu)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/alterlab-ieu/alterlab-cosmic

---


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

## Quick Start

### 1. Basic File Download

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

```python
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"
)
```

### 2. Command-Line Usage

```bash
# 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
```

### 3. Working with Downloaded Data

```python
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

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              | File (GRCh38/cosmic/latest/...)        |
|-----------------------|-----------------------|----------------------------------------|
| Coding mutations      | `mutations`           | `CosmicMutantExport.tsv.gz`            |
| Coding mutations (VCF)| `mutations_vcf`       | `VCF/CosmicCodingMuts.vcf.gz`          |
| Cancer Gene Census    | `gene_census`         | `cancer_gene_census.csv`               |
| Resistance mutations  | `resistance_mutations`| `CosmicResistanceMutations.tsv.gz`     |
| Structural variants   | `structural_variants` | `CosmicStructExport.tsv.gz`            |
| Gene fusions          | `fusion_genes`        | `CosmicFusionExport.tsv.gz`            |
| Copy number           | `copy_number`         | `CosmicCompleteCNA.tsv.gz`             |
| Gene expression       | `gene_expression`     | `CosmicCompleteGeneExpression.tsv.gz`  |
| Sample metadata       | `sample_info`         | `CosmicSample.tsv.gz`                  |
| Mutational signatures | `signatures`          | `signatures/signatures.tsv`            |

Notes:
- **Cancer Gene Census** is the expert-curated list of cancer genes; use its
  `Role in Cancer` field to split oncogenes from tumor suppressors (TSG).
- **Mutational signatures** cover Single Base Substitution (SBS), Doublet Base
  Substitution (DBS), and Insertion/Deletion (ID) profiles.
- The `signatures` path is assembly-independent (no `GRCh38/` prefix).

## 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:
```python
# 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 ships roughly one to two releases per year; check the
  [release notes](https://cancer.sanger.ac.uk/cosmic/release_notes) for the
  current version number rather than assuming it
- For reproducible research, pin an explicit version (e.g. `v102`) in the
  filepath instead of `latest`, 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

**Filter mutations by gene**:
```python
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**:
```python
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**:
```python
mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')
lung_mutations = mutations[mutations['Primary site'] == 'lung']
```

**Work with VCF files**:
```python
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
```python
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'
```

The accepted `data_type` shortcuts are the ones in the Available Data Types table above.

## 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 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