Gget 工作流

# gget 工作流示例 扩展的工作流示例，展示了如何组合多个 gget 模块以完成常见的生物信息学任务。 ## 目录 1. [完整基因分析流水线](#complete-gene-analysis-pipeline) 2. [比较结构生物学](#comparative-structural-biology) 3. [癌症基因组学分析](#cancer-genomics-analysis) 4. [单细胞表达分析](#single-cell-expression-analysis) 5. [构建参考转录组](#building-reference-transcriptomes) 6. [突变影响评估](#mutation-impact-assessment) 7. [药物靶点发现](#drug-target-discovery) --- ## 完整基因分析流水线 从发现到功能注释的基因综合分析。 python import gget import pandas as pd # 第 1 步：搜索感兴趣的基因 print("第 1 步：搜索 GABA 受体基因...") search_results = gget.search(["GABA", "receptor", "alpha"], species="homo_sapiens", andor="and") print(f"找到 {len(search_results)} 个基因") # 第 2 步：获取详细信息 print("n第 2 步：获取详细信息...") gene_ids = search_results["ensembl_id"].tolist()[:5] # 前 5 个基因 gene_info = gget.info(gene_ids, pdb=True) print(gene_info[["ensembl_id", "gene_name", "uniprot_id", "description"]]) # 第 3 步：检索序列 print("n第 3 步：检索序列...") nucleotide_seqs = gget.seq(gene_ids) protein_seqs = gget.seq(gene_ids, translate=True) # 保存序列 with open("gaba_receptors_nt.fasta", "w") as f: f.write(nucleotide_seqs) with open("gaba_receptors_aa.fasta", "w") as f: f.write(protein_seqs) # 第 4 步：获取表达数据 print("n第 4 步：获取组织表达数据...") for gene_id, gene_name in zip(gene_ids, gene_info["gene_name"]): expr_data = gget.archs4(gene_name, which="tissue") print(f"n{gene_name} 表达：") print(expr_data.head()) # 第 5 步：查找相关基因 print("n第 5 步：查找相关基因...") correlated = gget.archs4(gene_info["gene_name"].iloc[0], which="correlation") correlated_top = correlated.head(20) print(correlated_top) # 第 6 步：对相关基因进行富集分析 print("n第 6 步：执行富集分析...") gene_list = correlated_top["gene_symbol"].tolist() enrichment = gget.enrichr(gene_list, database="ontology", plot=True) print(enrichment.head(10)) # 第 7 步：获取疾病关联 print("n第 7 步：获取疾病关联...") for gene_id