COSMIC 数据库 Python 工具：癌症体细胞突变数据下载与分析

cosmic-database by davila7/claude-code-templates

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安装命令

npx skills add https://github.com/davila7/claude-code-templates --skill cosmic-database

科研工具生物信息学数据处理

🇨🇳中文介绍

COSMIC 数据库

概述

COSMIC（癌症体细胞突变目录）是全球最大、最全面的数据库，用于探索人类癌症中的体细胞突变。通过编程方式访问 COSMIC 庞大的癌症基因组学数据集合，包括数千种癌症类型中的数百万个突变、精选基因列表、突变特征谱和临床注释。

何时使用此技能

此技能应在以下情况下使用：

从 COSMIC 下载癌症突变数据
访问癌症基因普查以获取精选的癌症基因列表
检索突变特征谱
查询结构变异、拷贝数变异或基因融合
分析耐药性突变
处理癌细胞系基因组学数据
将癌症突变数据整合到生物信息学流程中
在癌症背景下研究特定基因或突变

先决条件

账户注册

COSMIC 要求身份验证才能下载数据：

学术用户：免费访问，需在 https://cancer.sanger.ac.uk/cosmic/register 注册
商业用户：需要许可证（联系 QIAGEN）

Python 要求

uv pip install requests pandas

快速开始

1. 基本文件下载

使用 scripts/download_cosmic.py 脚本下载 COSMIC 数据文件：

from scripts.download_cosmic import download_cosmic_file

# 下载突变数据
download_cosmic_file(
    email="your_email@institution.edu",
    password="your_password",
    filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz",
    output_filename="cosmic_mutations.tsv.gz"
)

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3. 处理下载的数据

import pandas as pd

# 读取突变数据
mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')

# 读取癌症基因普查
gene_census = pd.read_csv('cancer_gene_census.csv')

# 读取 VCF 格式
import pysam
vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz')

下载全面的突变数据，包括点突变、插入缺失和基因组注释。

常见数据类型：

mutations - 完整的编码区突变（TSV 格式）
mutations_vcf - VCF 格式的编码区突变
sample_info - 样本元数据和肿瘤信息

# 下载所有编码区突变
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz"
)

访问专家精选的约 700 多个有充分证据表明与癌症相关的基因列表。

# 下载癌症基因普查
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/cancer_gene_census.csv"
)

识别已知的癌症基因
根据癌症相关性过滤变异
理解基因作用（癌基因 vs 抑癌基因）
为研究选择靶基因

下载用于突变特征分析的特征谱。

# 下载特征定义
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="signatures/signatures.tsv"
)

单碱基替换（SBS）特征
双碱基替换（DBS）特征
插入/缺失（ID）特征

结构变异与融合

访问基因融合数据和结构重排。

可用数据类型：

structural_variants - 结构断点
fusion_genes - 基因融合事件

# 下载基因融合
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/CosmicFusionExport.tsv.gz"
)

检索拷贝数变异和基因表达数据。

可用数据类型：

copy_number - 拷贝数增益/丢失
gene_expression - 过表达/低表达数据

# 下载拷贝数数据
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/CosmicCompleteCNA.tsv.gz"
)

访问带有临床注释的耐药性突变数据。

# 下载耐药性突变
download_cosmic_file(
    email="user@email.com",
    password="password",
    filepath="GRCh38/cosmic/latest/CosmicResistanceMutations.tsv.gz"
)

处理 COSMIC 数据

COSMIC 为两个参考基因组提供数据：

GRCh38（推荐，当前标准）
GRCh37（旧版，用于较旧的流程）

在文件路径中指定组装版本：

# GRCh38（推荐）
filepath="GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz"

# GRCh37（旧版）
filepath="GRCh37/cosmic/latest/CosmicMutantExport.tsv.gz"

在文件路径中使用 latest 以始终获取最新版本
COSMIC 每季度更新一次（当前版本：v102，2025年5月）
可使用特定版本以保证可重复性：v102、v101 等

TSV/CSV：制表符/逗号分隔，gzip 压缩，使用 pandas 读取
VCF：标准变异格式，与 pysam、bcftools 或 GATK 一起使用
所有文件都包含描述列内容的标题行

按基因过滤突变：

import pandas as pd

mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')
tp53_mutations = mutations[mutations['Gene name'] == 'TP53']

按作用识别癌症基因：

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)]

按癌症类型提取突变：

mutations = pd.read_csv('cosmic_mutations.tsv.gz', sep='\t', compression='gzip')
lung_mutations = mutations[mutations['Primary site'] == 'lung']

处理 VCF 文件：

import pysam

vcf = pysam.VariantFile('CosmicCodingMuts.vcf.gz')
for record in vcf.fetch('17', 7577000, 7579000):  # TP53 区域
    print(record.id, record.ref, record.alts, record.info)

有关 COSMIC 数据结构、可用文件和字段描述的全面信息，请参阅 references/cosmic_data_reference.md。此参考包括：

可用数据类型和文件的完整列表
每种文件类型的详细字段描述
文件格式规范
常见文件路径和命名约定
数据更新计划和版本控制
引用信息

在以下情况下使用此参考：

探索 COSMIC 中可用的数据
理解特定字段的含义
确定数据类型的正确文件路径
规划使用 COSMIC 数据的分析工作流程

下载脚本包含用于常见操作的辅助函数：

获取常见文件路径

from scripts.download_cosmic import get_common_file_path

# 获取突变文件的路径
path = get_common_file_path('mutations', genome_assembly='GRCh38')
# 返回：'GRCh38/cosmic/latest/CosmicMutantExport.tsv.gz'

# 获取基因普查的路径
path = get_common_file_path('gene_census')
# 返回：'GRCh38/cosmic/latest/cancer_gene_census.csv'

可用快捷方式：

mutations - 核心编码区突变
mutations_vcf - VCF 格式突变
gene_census - 癌症基因普查
resistance_mutations - 耐药性数据
structural_variants - 结构变异
gene_expression - 表达数据
copy_number - 拷贝数变异
fusion_genes - 基因融合
signatures - 突变特征谱
sample_info - 样本元数据

验证邮箱和密码是否正确
确保账户已在 cancer.sanger.ac.uk/cosmic 注册
检查您的使用场景是否需要商业许可证

验证文件路径是否正确
检查请求的版本是否存在
使用 latest 获取最新版本
确认基因组组装版本（GRCh37 与 GRCh38）正确

COSMIC 文件可能高达数 GB
确保有足够的磁盘空间
下载可能需要几分钟，具体取决于网络连接
脚本会显示大文件的下载进度

商业用户必须通过 QIAGEN 获得 COSMIC 许可证
联系方式：cosmic-translation@sanger.ac.uk
学术访问是免费的，但需要注册

与其他工具的集成

COSMIC 数据可与以下工具良好集成：

变异注释：VEP、ANNOVAR、SnpEff
特征分析：SigProfiler、deconstructSigs、MuSiCa
癌症基因组学：cBioPortal、OncoKB、CIViC
生物信息学：Bioconductor、TCGA 分析工具
数据科学：pandas、scikit-learn、PyTorch

使用 COSMIC 数据时，请引用：Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research. 2019;47(D1):D941-D947.

🇺🇸English

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"
)

2. 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

3. 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.

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COSMIC 数据库 Python 工具：癌症体细胞突变数据下载与分析

🇨🇳中文介绍

COSMIC 数据库

概述

何时使用此技能

先决条件

账户注册

Python 要求

快速开始

1. 基本文件下载

相关 Skills

2. 命令行用法

3. 处理下载的数据

可用数据类型

核心突变

癌症基因普查

突变特征谱

结构变异与融合

拷贝数与表达

耐药性突变

处理 COSMIC 数据

基因组组装

版本控制

文件格式

常见分析模式

数据参考

辅助函数