ChebiUtils
ChebiDbLite
This is a simple Python implementation of some functionality for locally caching and searching ChEBI data. It works from the ChEBI OBO download file (nightly build) as input and builds a rapid-access Python dictionary of entity data as well as a searchable Whoosh index of commonly searched fields. The main purpose of this library is to enable accessing ChEBI content and traversing common relational patterns quickly.
Beware: this library is in a very preliminary stage of development!
To install, clone the repository to a directory on your local file system and install from there using pip:
pip install -e /path/to/your/directory/chebiutils
To setup the local cache of ChEBI data, run:
from chebidblite import setupdb
setupdb.prepareCacheAndIndex()
The cache and index will be built into the directory specified by the environment variable CHEBIDBLITECACHE, which defaults to the following folder within your user home directory: '~/Library/Caches/chebidblite/'. This takes a few minutes. It only needs to be executed once, but can be re-executed whenever a newer version of ChEBI is required -- e.g., overnight.
Once the cache and index are prepared, they can be accessed from any other Python script as required without the overhead of rebuilding.
To perform searches (for example), use:
from chebidblite import searcher
chebisearcher = searcher.ChebiSearcher()
res = chebisearcher.findAllChildrenOf("CHEBI:15377")
ids = [r.chebi_id for r in res]
names = [r.chebi_name for r in res]
Another example, looking for descendents of a particular class that have structures:
# get all leaf nodes that are descendents of 'tricarboxylic acid' and have structures
res = chebisearcher.findAllLeafChildrenWithStructures(chebisearcher.findChebiIdByName("tricarboxylic acid").chebi_id)
ids = [r.chebi_id for r in res]
names = [r.chebi_name for r in res]
print(names)
And looking for all molecular entities that have a particular role:
res = chebisearcher.findAllWithRole(chebisearcher.findChebiIdByName("nicotinic antagonist").chebi_id)
ids = [r.chebi_id for r in res]
names = [r.chebi_name for r in res]
print(names)
Of course, in addition to the pre-built search functions, you can build up your own queries dynamically:
res = chebisearcher.findAllByQueryString(chebisearcher.IS_A+chebisearcher.findChebiIdByName("subatomic particle").chebi_id+chebisearcher.AND+chebisearcher.IS_A+chebisearcher.findChebiIdByName("molecular entity").chebi_id)
[r.chebi_name for r in res]
If you don't want to search but just want to access the stored database by chebi id (for example), use:
from chebidblite import dblite
dbchebi = dblite.ChebiDbLite()
dbchebi.initialize()
chebi_id = "CHEBI:15377"
water = dbchebi.getEntity(chebi_id)
print(water.chebi_name)
# Direct is_a relationships only:
print(water.is_a)
print([dbchebi.getEntity(e).chebi_name for e in water.is_a])
# All recursively populated ancestor IDs for each entity are stored in a separate map
ancestors_of_water = dbchebi.ancestor_map[chebi_id]
print([dbchebi.getEntity(e).chebi_name for e in ancestors_of_water])