🎯

cosmic-database

🎯Skill

from ovachiever/droid-tings

What it does

Retrieves and queries comprehensive cancer mutation data from the COSMIC database for genomic research and precision oncology.

📦

Part of

ovachiever/droid-tings(370 items)

cosmic-database

Installation

PythonRun Python server

python scripts/download_cosmic.py user@email.com --data-type mutations

📖 Extracted from docs: ovachiever/droid-tings

Need more details? View full documentation on GitHub →

17Installs

AddedFeb 4, 2026

View on GitHub Back to Skills

Skill Details

SKILL.md

"Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication."

Overview

# 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

```

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

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

```python

# 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.

```python

# 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.

```python

# 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

```python

# 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

```python

# 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.

```python

# 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:

```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 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:

```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: