BioClients 
Python package for access to online biomedical resources,
usually via REST APIs. Modules generally include
Client.py for command-line use and Utils.py for
integration into other code. With the advent of HTTP web services,
first SOAP/XML and then mostly REST/JSON, many online APIs
require very similar methods for data search, requests
and transforms into usable formats, often TSV.
Availability and installation
Source at https://github.com/jeremyjyang/BioClients;
releases available via pypi.org:
https://pypi.org/project/BioClients/
(pip3 install BioClients).
However, current development snapshot recommended.
(First download or clone.)
$ cd BioClients
$ python3 setup.py install
Dependencies
- Python 3.6+
- Python packages:
pandas,requests,urllib,json,xml,yaml,psycopg2,tqdm, etc.
Modules
Allen • AMP-T2D • BindingDb • BioGrid • BRENDA • CDC • Chem2Bio2RDF • ChEMBL • Disease Ontology • DisGeNet • DNorm • DrugCentral • EnsEMBL • FDA • Gene Ontology • GWAS Catalog • HUGO • HumanBase • iCite • IDG • JensenLab • LINCS • MaayanLab • MeSH • MONARCH • MyGene • NCBO • OMIM • Open Targets • Panther • PDB • PubChem • PubMed • PubTator • Reactome • RXNorm • STRINGDB • TCGA • • UMLS • UniProt • Wikidata • WikiPathways
Miscellaneous utilities: UTIL
Usage Example
$ python3 -m BioClients.pubchem.Client -h
Design pattern
Generally each module includes command-line app Client.py which calls
functions in a corresponding Utils.py, providing all capabilities
by import of the module. Functions can write to an output file
or return a Pandas dataframe (if output file unspecified).
Data structures and formats, XML, JSON, and TSV
BioClients is designed to be simple and practical, and XML, JSON and TSV are likewise simple in many respects, yet a great deal of conceptual and technological progress is reflected. XML and JSON can represent arbitrarily complex data objects, comprised of nested lists, dictionaries, and trees of primary types. TSV represents tables of rows and columns, related by common keys, reflecting the development of SQL and relational databases. Transforming JSON to TSV, as these clients generally do, projects data objects to tables useful for many applications (e.g. machine learning).
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