Intro
MyGene.Info provides simple-to-use REST web services to query/retrieve gene annotation data.
It's designed with simplicity and performance emphasized. mygene, is an easy-to-use Python
wrapper to access MyGene.Info services.
Since v3.1.0, mygene Python package has become a thin wrapper of underlying biothings_client package, a universal Python client for all BioThings APIs, including MyGene.info. The installation of mygene will install biothings_client automatically. The following code snippets are essentially equivalent:
-
Continue using mygene package
In [1]: import mygene In [2]: mg = mygene.MyGeneInfo()
-
Use biothings_client package directly
In [1]: from biothings_client import get_client In [2]: mg = get_client('gene')
After that, the use of mg instance is exactly the same, e.g. the usage examples below.
Requirements
python >=2.7 (including python3)
(Python 2.6 might still work, but it's not supported any more since v3.1.0.)
biothings_client (>=0.2.0, install using "pip install biothings_client")
Optional dependencies
pandas (install using "pip install pandas") is required for returning a list of gene objects as DataFrame.
Installation
- Option 1
- pip install mygene
- Option 2
download/extract the source code and run:
python setup.py install- Option 3
install the latest code directly from the repository:
pip install -e git+https://github.com/biothings/mygene.py#egg=mygene
Version history
CHANGES.txt
Tutorial
Documentation
http://mygene-py.readthedocs.org/
Usage
In [1]: import mygene
In [2]: mg = mygene.MyGeneInfo()
In [3]: mg.getgene(1017)
Out[3]:
{'_id': '1017',
'entrezgene': 1017,
'name': 'cyclin-dependent kinase 2',
'symbol': 'CDK2',
'taxid': 9606,
...
}
# use "fields" parameter to return a subset of fields
In [4]: mg.getgene(1017, fields='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, fields=['name', 'symbol', 'refseq.rna'])
Out[5]:
{'_id': '1017',
'name': 'cyclin-dependent kinase 2',
'refseq': {'rna': ['NM_001798.5', 'NM_052827.3']},
'symbol': 'CDK2'}
In [6]: mg.getgenes([1017,1018,'ENSG00000148795'], fields='name,symbol,entrezgene,taxid')
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}]
# return results in Pandas DataFrame
In [7]: mg.getgenes([1017,1018,'ENSG00000148795'], fields='name,symbol,entrezgene,taxid', 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>Contact
- Drop us any question or feedback:
-
- biothings@googlegroups.com (public discussion)
- help@mygene.info (reach devs privately)
- Github issues
- on twitter @mygeneinfo
- Post a question on BioStars.org with tag #mygene.
