--- name: gene-database description: Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene 在fo (RefSeqs, GO, locations, phenotypes), batch lookups, 用于 gene annotation 和 functional analysis. license: Unknown metadata: skill-author: K-Dense Inc. --- # 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 ```python 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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/