Biopython Entrez数据库

Entrez是NCBI提供的在线搜索系统。通过集成的全局查询，它支持布尔运算符和字段搜索，从而可以访问几乎所有已知的分子生物学数据库。它返回所有数据库的结果，并提供诸如每个数据库的命中次数，带有原始数据库链接的记录等信息。

下面列出了一些可以通过Entrez访问的流行数据库 -

• Pubmed
• Pubmed Central
• Nucleotide(GenBank序列数据库)
• Protein(序列数据库)
• Genome(整个基因组数据库)
• Structure(三维高分子结构)
• Taxonomy(GenBank中的有机体)
• SNP(单核苷酸多态性)
• UniGene(转录序列的基因导向簇)
• CDD(保守蛋白质结构域数据库)
• 3D域(来自Entrez结构的域)

除上述数据库外，Entrez还提供更多数据库来执行字段搜索。
Biopython提供了一个Entrez特定模块Bio.Entrez来访问Entrez数据库。下面将学习如何使用Biopython访问Entrez -

1. 数据库连接步骤

要添加Entrez的功能，请导入以下模块-

>>> from Bio import Entrez

接下来设置电子邮件以识别谁与下面给出的代码相关联 -

>>> Entrez.email = '<youremail>'

然后，设置Entrez工具参数，默认情况下为Biopython。

>>> Entrez.tool = 'Demoscript'

现在，调用einfo函数以查找索引术语计数，上次更新以及每个数据库的可用链接，如下所示-

>>> info = Entrez.einfo()

einfo方法返回一个对象，该对象通过read方法提供对信息的访问，如下所示 -

>>> data = info.read() 
>>> print(data) 
<?xml version = "1.0" encoding = "UTF-8" ?>
<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD einfo 20130322//EN" 
   "https://eutils.ncbi.nlm.nih.gov/eutils/dtd/20130322/einfo.dtd"> 
<eInfoResult>
   <DbList>
      <DbName>pubmed</DbName> 
      <DbName>protein</DbName>
      <DbName>nuccore</DbName> 
      <DbName>ipg</DbName> 
      <DbName>nucleotide</DbName>
      <DbName>nucgss</DbName> 
      <DbName>nucest</DbName>
      <DbName>structure</DbName>
      <DbName>sparcle</DbName>
      <DbName>genome</DbName>
      <DbName>annotinfo</DbName>
      <DbName>assembly</DbName> 
      <DbName>bioproject</DbName>
      <DbName>biosample</DbName>
      <DbName>blastdbinfo</DbName>
      <DbName>books</DbName> 
      <DbName>cdd</DbName>
      <DbName>clinvar</DbName> 
      <DbName>clone</DbName> 
      <DbName>gap</DbName> 
      <DbName>gapplus</DbName> 
      <DbName>grasp</DbName> 
      <DbName>dbvar</DbName>
      <DbName>gene</DbName> 
      <DbName>gds</DbName> 
      <DbName>geoprofiles</DbName>
      <DbName>homologene</DbName> 
      <DbName>medgen</DbName> 
      <DbName>mesh</DbName>
      <DbName>ncbisearch</DbName> 
      <DbName>nlmcatalog</DbName>
      <DbName>omim</DbName>
      <DbName>orgtrack</DbName>
      <DbName>pmc</DbName>
      <DbName>popset</DbName>
      <DbName>probe</DbName>
      <DbName>proteinclusters</DbName>
      <DbName>pcassay</DbName>
      <DbName>biosystems</DbName> 
      <DbName>pccompound</DbName> 
      <DbName>pcsubstance</DbName> 
      <DbName>pubmedhealth</DbName> 
      <DbName>seqannot</DbName> 
      <DbName>snp</DbName> 
      <DbName>sra</DbName> 
      <DbName>taxonomy</DbName> 
      <DbName>biocollections</DbName> 
      <DbName>unigene</DbName>
      <DbName>gencoll</DbName> 
      <DbName>gtr</DbName>
   </DbList> 
</eInfoResult>

数据为XML格式，要获取数据作为python对象，请在调用Entrez.einfo()方法后立即使用Entrez.read方法-

>>> info = Entrez.einfo() 
>>> record = Entrez.read(info)

在这里，record是一本字典，它具有一个DbList键，如下所示-

>>> record.keys() 
[u'DbList']

访问DbList键返回数据库名称的列表，如下所示 -

>>> record[u'DbList'] 
['pubmed', 'protein', 'nuccore', 'ipg', 'nucleotide', 'nucgss', 
   'nucest', 'structure', 'sparcle', 'genome', 'annotinfo', 'assembly', 
   'bioproject', 'biosample', 'blastdbinfo', 'books', 'cdd', 'clinvar', 
   'clone', 'gap', 'gapplus', 'grasp', 'dbvar', 'gene', 'gds', 'geoprofiles', 
   'homologene', 'medgen', 'mesh', 'ncbisearch', 'nlmcatalog', 'omim', 
   'orgtrack', 'pmc', 'popset', 'probe', 'proteinclusters', 'pcassay', 
   'biosystems', 'pccompound', 'pcsubstance', 'pubmedhealth', 'seqannot', 
   'snp', 'sra', 'taxonomy', 'biocollections', 'unigene', 'gencoll', 'gtr'] 
>>>

基本上，Entrez模块解析Entrez搜索系统返回的XML，并将其提供为python字典和列表。

2. 搜索数据库

要搜索任何一个Entrez数据库，需要使用Bio.Entrez.esearch()模块。它定义如下 -

>>> info = Entrez.einfo() 
>>> info = Entrez.esearch(db = "pubmed",term = "genome") 
>>> record = Entrez.read(info) 
>>>print(record) 
DictElement({u'Count': '1146113', u'RetMax': '20', u'IdList':
['30347444', '30347404', '30347317', '30347292', 
'30347286', '30347249', '30347194', '30347187', 
'30347172', '30347088', '30347075', '30346992', 
'30346990', '30346982', '30346980', '30346969', 
'30346962', '30346954', '30346941', '30346939'], 
u'TranslationStack': [DictElement({u'Count': 
'927819', u'Field': 'MeSH Terms', u'Term': '"genome"[MeSH Terms]', 
u'Explode': 'Y'}, attributes = {})
, DictElement({u'Count': '422712', u'Field': 
'All Fields', u'Term': '"genome"[All Fields]', u'Explode': 'N'}, attributes = {}), 
'OR', 'GROUP'], u'TranslationSet': [DictElement({u'To': '"genome"[MeSH Terms] 
OR "genome"[All Fields]', u'From': 'genome'}, attributes = {})], u'RetStart': '0', 
u'QueryTranslation': '"genome"[MeSH Terms] OR "genome"[All Fields]'}, 
attributes = {})
>>>

如果分配了错误的数据库，那么它将返回 -

>>> info = Entrez.esearch(db = "blastdbinfo",term = "books")
>>> record = Entrez.read(info) 
>>> print(record) 
DictElement({u'Count': '0', u'RetMax': '0', u'IdList': [], 
u'WarningList': DictElement({u'OutputMessage': ['No items found.'], 
   u'PhraseIgnored': [], u'QuotedPhraseNotFound': []}, attributes = {}), 
   u'ErrorList': DictElement({u'FieldNotFound': [], u'PhraseNotFound': 
      ['books']}, attributes = {}), u'TranslationSet': [], u'RetStart': '0', 
      u'QueryTranslation': '(books[All Fields])'}, attributes = {})

如果要跨数据库搜索，则可以使用Entrez.egquery。它与Entrez.esearch相似，只不过它足以指定关键字并跳过数据库参数。

>>>info = Entrez.egquery(term = "entrez") 
>>> record = Entrez.read(info) 
>>> for row in record["eGQueryResult"]: 
... print(row["DbName"], row["Count"]) 
... 
pubmed 458 
pmc 12779 mesh 1 
... 
... 
... 
biosample 7 
biocollections 0

3. 提取记录

Enterz提供了一种特殊的方法，即从Entrez检索和下载记录的全部详细信息。考虑以下简单示例 -

>>> handle = Entrez.efetch(
   db = "nucleotide", id = "EU490707", rettype = "fasta")

现在，可以简单地使用SeqIO对象读取记录：

>>> record = SeqIO.read( handle, "fasta" ) 
>>> record 
SeqRecord(seq = Seq('ATTTTTTACGAACCTGTGGAAATTTTTGGTTATGACAATAAATCTAGTTTAGTA...GAA', 
SingleLetterAlphabet()), id = 'EU490707.1', name = 'EU490707.1', 
description = 'EU490707.1 
Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast', 
dbxrefs = [])