基因数据库

基因数据库

概述

基因数据库技能提供对多个基因数据库的统一访问，包括NCBI Gene、Ensembl、UniProt和HGNC。它允许查询基因信息，包括基因符号、描述、基因组位置、功能注释、蛋白质序列、疾病关联和表达模式。

何时使用此技能

使用基因数据库当：

• 查找基因信息：获取基因符号、描述、基因组位置
• 功能注释：获取基因功能、通路、GO术语
• 蛋白质信息：获取蛋白质序列、结构、功能域
• 疾病关联：查找与疾病相关的基因
• 表达模式：获取基因表达数据
• 跨数据库查询：跨多个数据库查询基因信息
• 基因映射：在不同数据库之间映射基因标识符

核心功能

1. NCBI Gene

按基因符号查询

from scripts.gene_query import GeneQuery

# 初始化
gene_query = GeneQuery()

# 按基因符号查询
gene_info = gene_query.query_ncbi_gene("BRCA1")

# 获取基因信息
print(f"基因符号: {gene_info['symbol']}")
print(f"描述: {gene_info['description']}")
print(f"基因组位置: {gene_info['chromosome']}:{gene_info['start']}-{gene_info['end']}")
print(f"基因ID: {gene_info['gene_id']}")

按基因ID查询

# 按NCBI Gene ID查询
gene_info = gene_query.query_ncbi_gene_by_id(672)

print(f"基因符号: {gene_info['symbol']}")
print(f"描述: {gene_info['description']}")

2. Ensembl

按基因符号查询

# 按基因符号查询Ensembl
gene_info = gene_query.query_ensembl("BRCA1")

# 获取Ensembl信息
print(f"Ensembl ID: {gene_info['ensembl_id']}")
print(f"基因符号: {gene_info['symbol']}")
print(f"基因组位置: {gene_info['chromosome']}:{gene_info['start']}-{gene_info['end']}")
print(f"生物型: {gene_info['biotype']}")

按Ensembl ID查询

# 按Ensembl ID查询
gene_info = gene_query.query_ensembl_by_id("ENSG00000012048")

print(f"基因符号: {gene_info['symbol']}")
print(f"描述: {gene_info['description']}")

3. UniProt

按基因符号查询

# 按基因符号查询UniProt
protein_info = gene_query.query_uniprot("BRCA1")

# 获取蛋白质信息
print(f"UniProt ID: {protein_info['uniprot_id']}")
print(f"蛋白质名称: {protein_info['protein_name']}")
print(f"基因名称: {protein_info['gene_name']}")
print(f"蛋白质长度: {protein_info['length']}")

按UniProt ID查询

# 按UniProt ID查询
protein_info = gene_query.query_uniprot_by_id("P38398")

print(f"蛋白质名称: {protein_info['protein_name']}")
print(f"基因名称: {protein_info['gene_name']}")

4. HGNC

按基因符号查询

# 按基因符号查询HGNC
gene_info = gene_query.query_hgnc("BRCA1")

# 获取HGNC信息
print(f"HGNC ID: {gene_info['hgnc_id']}")
print(f"基因符号: {gene_info['symbol']}")
print(f"基因名称: {gene_info['name']}")
print(f"符号状态: {gene_info['status']}")

5. 跨数据库查询

综合基因信息

# 获取综合基因信息
gene_info = gene_query.get_comprehensive_gene_info("BRCA1")

# 获取所有数据库的信息
print(f"NCBI Gene ID: {gene_info['ncbi']['gene_id']}")
print(f"Ensembl ID: {gene_info['ensembl']['ensembl_id']}")
print(f"UniProt ID: {gene_info['uniprot']['uniprot_id']}")
print(f"HGNC ID: {gene_info['hgnc']['hgnc_id']}")

6. 批量查询

批量查询基因

# 批量查询多个基因
gene_symbols = ["BRCA1", "BRCA2", "TP53", "EGFR"]

results = gene_query.batch_query_genes(gene_symbols)

# 处理结果
for gene_symbol, gene_info in results.items():
    print(f"{gene_symbol}: {gene_info['description']}")

7. 基因标识符映射

映射基因标识符

# 映射基因符号到Ensembl ID
ensembl_ids = gene_query.map_symbol_to_ensembl(["BRCA1", "BRCA2"])

# 映射Ensembl ID到基因符号
symbols = gene_query.map_ensembl_to_symbol(["ENSG00000012048", "ENSG00000139618"])

# 映射基因符号到UniProt ID
uniprot_ids = gene_query.map_symbol_to_uniprot(["BRCA1", "BRCA2"])

高级功能

1. 获取基因序列

# 获取基因序列
gene_sequence = gene_query.get_gene_sequence("BRCA1")

print(f"基因长度: {len(gene_sequence)} bp")
print(f"序列: {gene_sequence[:100]}...")

2. 获取蛋白质序列

# 获取蛋白质序列
protein_sequence = gene_query.get_protein_sequence("BRCA1")

print(f"蛋白质长度: {len(protein_sequence)} aa")
print(f"序列: {protein_sequence[:100]}...")

3. 获取基因注释

# 获取基因注释
annotations = gene_query.get_gene_annotations("BRCA1")

# 获取GO术语
go_terms = annotations['go_terms']
print(f"生物过程: {go_terms['biological_process']}")
print(f"分子功能: {go_terms['molecular_function']}")
print(f"细胞组分: {go_terms['cellular_component']}")

# 获取通路
pathways = annotations['pathways']
print(f"通路: {pathways}")

4. 获取疾病关联

# 获取疾病关联
diseases = gene_query.get_disease_associations("BRCA1")

for disease in diseases:
    print(f"疾病: {disease['name']}")
    print(f"关联类型: {disease['association_type']}")
    print(f"来源: {disease['source']}")

5. 获取表达数据

# 获取表达数据
expression = gene_query.get_expression_data("BRCA1")

# 获取组织表达
tissue_expression = expression['tissue_expression']
print(f"组织表达: {tissue_expression}")

# 获取发育阶段表达
developmental_expression = expression['developmental_expression']
print(f"发育阶段表达: {developmental_expression}")

常见工作流

工作流1：查找基因信息

from scripts.gene_query import GeneQuery

# 初始化
gene_query = GeneQuery()

# 查询基因
gene_symbol = "BRCA1"

# 获取综合信息
gene_info = gene_query.get_comprehensive_gene_info(gene_symbol)

# 打印信息
print(f"基因符号: {gene_symbol}")
print(f"描述: {gene_info['ncbi']['description']}")
print(f"基因组位置: {gene_info['ensembl']['chromosome']}:{gene_info['ensembl']['start']}-{gene_info['ensembl']['end']}")
print(f"蛋白质长度: {gene_info['uniprot']['length']} aa")

工作流2：批量查询基因

from scripts.gene_query import GeneQuery

# 初始化
gene_query = GeneQuery()

# 批量查询
gene_symbols = ["BRCA1", "BRCA2", "TP53", "EGFR", "KRAS"]

results = gene_query.batch_query_genes(gene_symbols)

# 创建摘要表
import pandas as pd

summary = []
for gene_symbol, gene_info in results.items():
    summary.append({
        '基因符号': gene_symbol,
        '描述': gene_info['ncbi']['description'],
        '染色体': gene_info['ensembl']['chromosome'],
        '蛋白质长度': gene_info['uniprot']['length']
    })

df = pd.DataFrame(summary)
print(df)

工作流3：基因功能分析

from scripts.gene_query import GeneQuery

# 初始化
gene_query = GeneQuery()

# 查询基因
gene_symbol = "TP53"

# 获取注释
annotations = gene_query.get_gene_annotations(gene_symbol)

# 打印GO术语
print(f"生物过程: {annotations['go_terms']['biological_process'][:5]}")
print(f"分子功能: {annotations['go_terms']['molecular_function'][:5]}")
print(f"细胞组分: {annotations['go_terms']['cellular_component'][:5]}")

# 打印通路
print(f"通路: {annotations['pathways'][:5]}")

工作流4：疾病基因分析

from scripts.gene_query import GeneQuery

# 初始化
gene_query = GeneQuery()

# 查询疾病相关基因
disease = "breast cancer"

# 查找相关基因
genes = gene_query.search_disease_genes(disease)

# 获取基因信息
for gene_symbol in genes[:10]:
    gene_info = gene_query.get_comprehensive_gene_info(gene_symbol)
    print(f"{gene_symbol}: {gene_info['ncbi']['description']}")

工作流5：基因标识符映射

from scripts.gene_query import GeneQuery

# 初始化
gene_query = GeneQuery()

# 映射标识符
gene_symbols = ["BRCA1", "BRCA2", "TP53"]

# 映射到Ensembl ID
ensembl_ids = gene_query.map_symbol_to_ensembl(gene_symbols)
print(f"Ensembl IDs: {ensembl_ids}")

# 映射到UniProt ID
uniprot_ids = gene_query.map_symbol_to_uniprot(gene_symbols)
print(f"UniProt IDs: {uniprot_ids}")

# 映射到NCBI Gene ID
ncbi_ids = gene_query.map_symbol_to_ncbi(gene_symbols)
print(f"NCBI Gene IDs: {ncbi_ids}")

最佳实践

1. 使用综合查询：使用get_comprehensive_gene_info获取所有数据库的信息
2. 批量查询：使用批量查询功能提高效率
3. 标识符映射：使用标识符映射功能在不同数据库之间转换
4. 错误处理：始终处理查询错误和缺失数据
5. 缓存结果：缓存查询结果以避免重复请求
6. 验证结果：验证查询结果的准确性

与其他工具集成

与gget集成

import gget
from scripts.gene_query import GeneQuery

# 使用gget查询
gene_info = gget.info(["BRCA1"])

# 使用gene_query获取更详细的信息
gene_query = GeneQuery()
detailed_info = gene_query.get_comprehensive_gene_info("BRCA1")

与biopython集成

from Bio import Entrez
from scripts.gene_query import GeneQuery

# 使用Biopython查询
Entrez.email = "your.email@example.com"
handle = Entrez.esearch(db="gene", term="BRCA1[Gene]")
record = Entrez.read(handle)

# 使用gene_query获取更多信息
gene_query = GeneQuery()
gene_info = gene_query.get_comprehensive_gene_info("BRCA1")

与pandas集成

import pandas as pd
from scripts.gene_query import GeneQuery

# 批量查询
gene_query = GeneQuery()
gene_symbols = ["BRCA1", "BRCA2", "TP53", "EGFR"]

results = gene_query.batch_query_genes(gene_symbols)

# 创建DataFrame
df = pd.DataFrame.from_dict(results, orient='index')
print(df)

故障排除

问题：查询失败

• 解决方案：检查网络连接，验证基因符号，检查API限制

问题：数据不完整

• 解决方案：尝试其他数据库，检查基因符号是否正确

问题：标识符映射失败

• 解决方案：验证标识符格式，检查数据库版本

问题：批量查询很慢

• 解决方案：减少查询数量，使用缓存，或分批查询

问题：API限制

• 解决方案：实现速率限制，使用缓存，或等待一段时间后重试

其他资源

• NCBI Gene: https://www.ncbi.nlm.nih.gov/gene/
• Ensembl: https://www.ensembl.org/
• UniProt: https://www.uniprot.org/
• HGNC: https://www.genenames.org/
• gget文档: https://pachterlab.github.io/gget/
• Biopython文档: https://biopython.org/