【3】数据分析-9-gen annoation -- mygene

对基因ID、基因名字进行注释,获得基因ID,物种,名字等信息

一、安装

pip install mygene

二、使用

In [1]: import mygene

In [2]: mg = mygene.MyGeneInfo()

根据gene id 获得其对应的名字
In [3]: mg.getgene(1017)
Out[3]:
{'_id': '1017',
 'entrezgene': 1017,
 'name': 'cyclin-dependent kinase 2',
 'symbol': 'CDK2',
 'taxid': 9606}

In [4]: mg.getgene(1017, 'name,symbol,refseq')
Out[4]:
{'_id': '1017',
 'name': 'cyclin-dependent kinase 2',
 'refseq': {'genomic': ['AC_000144.1',
   'NC_000012.11',
   'NG_028086.1',
   'NT_029419.12',
   'NW_001838059.1'],
  'protein': ['NP_001789.2', 'NP_439892.2'],
  'rna': ['NM_001798.3', 'NM_052827.2']},
 'symbol': 'CDK2'}

In [5]: mg.getgene(1017, 'name,symbol,refseq.rna')
Out[5]:
{'_id': '1017',
 'name': 'cyclin-dependent kinase 2',
 'refseq': {'rna': ['NM_001798', 'NM_052827']},
 'symbol': 'CDK2'}


In [6]: mg.getgenes([1017,1018,'ENSG00000148795'])
Out[6]:
[{'_id': '1017',
  'entrezgene': 1017,
  'name': 'cyclin-dependent kinase 2',
  'query': '1017',
  'symbol': 'CDK2',
  'taxid': 9606},
 {'_id': '1018',
  'entrezgene': 1018,
  'name': 'cyclin-dependent kinase 3',
  'query': '1018',
  'symbol': 'CDK3',
  'taxid': 9606},
 {'_id': '1586',
  'entrezgene': 1586,
  'name': 'cytochrome P450, family 17, subfamily A, polypeptide 1',
  'query': 'ENSG00000148795',
  'symbol': 'CYP17A1',
  'taxid': 9606}]

In [7]: mg.getgenes([1017,1018,'ENSG00000148795'], as_dataframe=True)
Out[7]:
                  _id  entrezgene  \
query
1017             1017        1017
1018             1018        1018
ENSG00000148795  1586        1586

                                                              name   symbol  \
query
1017                                     cyclin-dependent kinase 2     CDK2
1018                                     cyclin-dependent kinase 3     CDK3
ENSG00000148795  cytochrome P450, family 17, subfamily A, polyp...  CYP17A1

                 taxid
query
1017              9606
1018              9606
ENSG00000148795   9606

[3 rows x 5 columns]

In [8]:  mg.query('cdk2', size=5)
Out[8]:
{'hits': [{'_id': '1017',
   '_score': 373.24667,
   'entrezgene': 1017,
   'name': 'cyclin-dependent kinase 2',
   'symbol': 'CDK2',
   'taxid': 9606},
  {'_id': '12566',
   '_score': 353.90176,
   'entrezgene': 12566,
   'name': 'cyclin-dependent kinase 2',
   'symbol': 'Cdk2',
   'taxid': 10090},
  {'_id': '362817',
   '_score': 264.88477,
   'entrezgene': 362817,
   'name': 'cyclin dependent kinase 2',
   'symbol': 'Cdk2',
   'taxid': 10116},
  {'_id': '52004',
   '_score': 21.221401,
   'entrezgene': 52004,
   'name': 'CDK2-associated protein 2',
   'symbol': 'Cdk2ap2',
   'taxid': 10090},
  {'_id': '143384',
   '_score': 18.617256,
   'entrezgene': 143384,
   'name': 'CDK2-associated, cullin domain 1',
   'symbol': 'CACUL1',
   'taxid': 9606}],
 'max_score': 373.24667,
 'took': 10,
 'total': 28}

In [9]: mg.query('reporter:1000_at')
Out[9]:
{'hits': [{'_id': '5595',
   '_score': 11.163337,
   'entrezgene': 5595,
   'name': 'mitogen-activated protein kinase 3',
   'symbol': 'MAPK3',
   'taxid': 9606}],
 'max_score': 11.163337,
 'took': 6,
 'total': 1}

In [10]: mg.query('symbol:cdk2', species='human')
Out[10]:
{'hits': [{'_id': '1017',
   '_score': 84.17707,
   'entrezgene': 1017,
   'name': 'cyclin-dependent kinase 2',
   'symbol': 'CDK2',
   'taxid': 9606}],
 'max_score': 84.17707,
 'took': 27,
 'total': 1}

In [11]: mg.querymany([1017, '695'], scopes='entrezgene', species='human')
Finished.
Out[11]:
[{'_id': '1017',
  'entrezgene': 1017,
  'name': 'cyclin-dependent kinase 2',
  'query': '1017',
  'symbol': 'CDK2',
  'taxid': 9606},
 {'_id': '695',
  'entrezgene': 695,
  'name': 'Bruton agammaglobulinemia tyrosine kinase',
  'query': '695',
  'symbol': 'BTK',
  'taxid': 9606}]

In [12]: mg.querymany([1017, '695'], scopes='entrezgene', species=9606)
Finished.
Out[12]:
[{'_id': '1017',
  'entrezgene': 1017,
  'name': 'cyclin-dependent kinase 2',
  'query': '1017',
  'symbol': 'CDK2',
  'taxid': 9606},
 {'_id': '695',
  'entrezgene': 695,
  'name': 'Bruton agammaglobulinemia tyrosine kinase',
  'query': '695',
  'symbol': 'BTK',
  'taxid': 9606}]

In [13]: mg.querymany([1017, '695'], scopes='entrezgene', species=9606, as_dataframe=True)
Finished.
Out[13]:
        _id  entrezgene                                       name symbol  \
query
1017   1017        1017                  cyclin-dependent kinase 2   CDK2
695     695         695  Bruton agammaglobulinemia tyrosine kinase    BTK

       taxid
query
1017    9606
695     9606

[2 rows x 5 columns]

In [14]: mg.querymany([1017, '695', 'NA_TEST'], scopes='entrezgene', species='human')
Finished.
Out[14]:
[{'_id': '1017',
  'entrezgene': 1017,
  'name': 'cyclin-dependent kinase 2',
  'query': '1017',
  'symbol': 'CDK2',
  'taxid': 9606},
 {'_id': '695',
  'entrezgene': 695,
  'name': 'Bruton agammaglobulinemia tyrosine kinase',
  'query': '695',
  'symbol': 'BTK',
  'taxid': 9606},
 {'notfound': True, 'query': 'NA_TEST'}]

# query all human kinases using fetch_all parameter:
In [15]: kinases = mg.query('name:kinase', species='human', fetch_all=True)
In [16]: kinases
Out [16]" <generator object _fetch_all at 0x7fec027d2eb0>

# kinases is a Python generator, now you can loop through it to get all 1073 hits:
In [16]: for gene in kinases:
   ....:     print gene['_id'], gene['symbol']
Out [16]: <output omitted here>

参考资料

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