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

• ID mapping using mygene module in Python

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:

• [email protected] (public discussion)
• [email protected] (reach devs privately)
• Github issues
• on twitter @mygeneinfo
• Post a question on BioStars.org with tag #mygene.