相信 Entrez 的强大是有目共睹的，BioPython 将它几乎所有操作都封装为方法，使我们可以更加方便的利用这个强悍工具。对于分析比对多个序列文件时的工作量说多了都是泪。比如，老板让你比对自己测定序列与 NCBI 库中序列，并构建相应的进化树，而这个序列需要大于100条。我想你的心情不会和下载一条序列时那么平静，那么，接下来通过BioPython提供的接口来实现快速的自动化序列下载。
一、自动获取氨基酸序列数据 1. 利用 Nucleotide 数据库来查询所有 oct4 基因的序列数据，为了展示基础的流程，这里采用逐条下载的方式 from Bio import Entrez,SeqIO"example@163.com" "exampleScript" "oct4" )0 for ele in read_egquery["eGQueryResult" ]:if ele["MenuName" ] == "Nucleotide" :"Count" ]"nucleotide" , term="oct4" , retmax=total)"IdList" ][:2 ]'nucleotide' , id =ids, rettype='fasta' )with open ("res/oct4.fasta" ,"w" ) as file:"nucleotide" , id =ids, retmode="xml" )print (read_efetch_xml)"nuccore" , id =ids, rettype="gb" , retmode="text" )with open ("res/oct4.gb" ,"w" ) as file:"nuccore" , id =ids, rettype="gb" , retmode="text" )"gb" )for ele in parse_efetch_gb:print (ele.name, ele.annotations[from Bio import Entrez,SeqIO"example@163.com" "exampleScript" "oct4" )0 for ele in read_egquery["eGQueryResult" ]:if ele["MenuName" ] == "Nucleotide" :"Count" ]"nucleotide" , term="oct4" , retmax=total)"IdList" ][:2 ]'nucleotide' , id =ids, rettype='fasta' )with open ("res/oct4.fasta" ,"w" ) as file:"nucleotide" , id =ids, retmode="xml" )print (read_efetch_xml)"nuccore" , id =ids, rettype="gb" , retmode="text" )with open ("res/oct4.gb" ,"w" ) as file:"nuccore" , id =ids, rettype="gb" , retmode="text" )"gb" )for ele in parse_efetch_gb:print (ele.name, ele.annotations['molecule_type' ], ele.seq)
1.2 用历史记录特性提高效率
还记得上一篇教程中提到的历史记录吗？
利用这个特性，不仅可以减轻 Entrez 服务器的负载，更可以同时获取多条数据，节省大量时间精力
from Bio import Entrez"example@163.com" "exampleScript" "nucleotide" , term="oct4" , usehistory="y" )"WebEnv" ]"QueryKey" ]5 10 with open ("res/res_env_oct4.fasta" , "w" ) as res_file:for start in range (0 , total, step):min (total, start+step)print ("Download record %i to %i" % (start+1 , end))"nucleotide" , rettype="fasta" , retmode=from Bio import Entrez"example@163.com" "exampleScript" "nucleotide" , term="oct4" , usehistory="y" )"WebEnv" ]"QueryKey" ]5 10 with open ("res/res_env_oct4.fasta" , "w" ) as res_file:for start in range (0 , total, step):min (total, start+step)print ("Download record %i to %i" % (start+1 , end))"nucleotide" , rettype="fasta" , retmode="text" , retstart=start, retmax=step, webenv=webenv, query_key=query_key)
二、自动获取参考文献 1. 利用PubMed数据库来查询所有关于小鼠的文献资料，为了展示基础的流程，这里采用逐条下载的方式 from Bio import Entrezfrom Bio import Medline"example@163.com" "exampleScript" "pubmed" , term="mouse" , RetMax="100" )"IdList" ]print ("Total: " , read_esearch["Count" ])"pubmed" , id =idlist, rettype="medline" , retmode="text" , )with open ("res/mouse_pubmed.xls" , "w" ) as file:"title\tauthors\tsource\tPubMed\n" )for i, ele in enumerate (list (parse_medline)):'TI' ] + "\t" + "," .join(ele['AU' ]) + "\t" + ele['SO' ] + "\t" + ele['PMID' ] + "\n" from Bio import Entrezfrom Bio import Medline"example@163.com" "exampleScript" "pubmed" , term="mouse" , RetMax="100" )"IdList" ]print ("Total: " , read_esearch["Count" ])"pubmed" , id =idlist, rettype="medline" , retmode="text" , )with open ("res/mouse_pubmed.xls" , "w" ) as file:"title\tauthors\tsource\tPubMed\n" )for i, ele in enumerate (list (parse_medline)):'TI' ] + "\t" + "," .join(ele['AU' ]) + "\t" + ele['SO' ] + "\t" + ele['PMID' ] + "\n" print (i, line)
2. 提高上面脚本的效率，这里我们来查询近一年的关于 Sus scrofa 的综述 from Bio import Entrez"example@163.com" "exampleScript" "pubmed" , term="Sus scrofa" , reldate=365 , ptyp="Review" , usehistory="y" )int (read_esearch["Count" ])"WebEnv" ]"QueryKey" ]10 5 print ("Result items: " , total)with open ("res/recent_review_sus_scrofa.txt" , "w" ) as file:for start in range (0 , total, step):print ("Download record %i to %i" % (start + 1 , int (start+step)))"pubmed" , retstart=start, retmax=step, webenv=webenv, query_key=query_key, rettype="medline" , retmode=from Bio import Entrez"example@163.com" "exampleScript" "pubmed" , term="Sus scrofa" , reldate=365 , ptyp="Review" , usehistory="y" )int (read_esearch["Count" ])"WebEnv" ]"QueryKey" ]10 5 print ("Result items: " , total)with open ("res/recent_review_sus_scrofa.txt" , "w" ) as file:for start in range (0 , total, step):print ("Download record %i to %i" % (start + 1 , int (start+step)))"pubmed" , retstart=start, retmax=step, webenv=webenv, query_key=query_key, rettype="medline" , retmode="text" )
三、获取物种谱系
NCBI 提供了很多生物相关数据库，用法几乎差不多，可以根据自身研究或者感兴趣的方向自行选择。
下面的例子是利用NCBI中的分类库 Taxonomy 来查询我们人类在分类学中的位置。
from Bio import Entrez"example@163.com" "exampleScript" "Taxonomy" , term="Homo sapiens" )id = read_esearch["IdList" ][0 ]"Taxonomy" , id =id , retmode="xml" )print (read_eftech[0 ][from Bio import Entrez"example@163.com" "exampleScript" "Taxonomy" , term="Homo sapiens" )id = read_esearch["IdList" ][0 ]"Taxonomy" , id =id , retmode="xml" )print (read_eftech[0 ]["Lineage" ])
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