Metadata-Version: 2.1
Name: iCEED
Version: 1.1
Summary: Package for customized extraction of enzyme data from bioinformatics databases
Home-page: UNKNOWN
Author: Rajiv Karbhal
Author-email: rajivkarbhal@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: requests

iCEED: 
Package for customized extraction of subsets of data from enzyme entry/ies available in various Bioinformatics databases.

Background and Necessity:

	Enzyme data is an essential component of bioinformatics databases as enzymes are the key molecules that drive biochemical reactions in living organisms. 
	The enzyme data in bioinformatics databases is extensive, with many databases providing enzyme information including information on their sequence, structure, function, physicochemical properties, reaction and pathways, etc. 
	Some databases archive extensive data on enzymes, while others comprise limited information.
	Common problems that researchers face when accessing enzyme data include difficulties in data retrieval and inconsistencies in data formats across different databases.
	One of the major issues is different databases provide different levels of annotation, leading to discrepancies in enzyme information.
	Additionally, there are inconsistencies or errors in the data due to incomplete or inaccurate annotations, or due to differences in data curation practices across databases. 
	Another problem is the integration of data from multiple sources, as data from different sources can have different formats, making it difficult to integrate and compare data. 
	Another issue is the fragmentation of enzyme data across various databases, which can make it challenging to find all relevant information in one place.
	Therefore there is a need for a dedicated package to access and extract enzyme data from various resources. 

Description:

iCEED package provides different modules for customized extraction of enzyme data from following bioinformatics databases:

	1) NCBI nucleotide database
	
	2) UniProt Protein sequece database
	
	3) PDB protein 3D structure database
	
	4) PubMed
	
	4) KEGG
	
	6) BRENDA
	
	7) MetaCyc
	
	8) InterPro
	
	9) PFAM
	
	10) Prosite
	
	11) ExpolrEnz
	
	12) IntEnz
	
	13) Expasy Enzyme

Users can extract the data in a customized fashion by using two options,  i) Using EC number only ii) organism-specific enzyme data extraction using taxonomic ID and EC number

There are a total of 11 modules:

	1) ec2seq: For extracting nucleotide and protein sequences of enzyme
	
	2) ec2PDB: For extracting 3D structure of enzyme
	
	3) ec2repath: For extracting reaction and pathway data of enzyme
	
	4) ec2mol: For extracting small molecule data of enzyme
	
	5) ec2param: For extracting physicochemical parameters of enzyme
	
	6) ec2dofam: For extracting domain and family data of enzyme
	
	7) ec2site: For extracting active site and other important functional sites of the enzyme
	
	8) ec2go: For extracting Gene Ontology data of enzyme
	
	9) ec2pub: For extracting published literature on enzyme
	
	10) ec2org: For extracting organism data from which the enzyme is characterized
	
	11) ec2syn: For extracting synonyms of the enzyme

Installation:
To install this Package, simply run the following command:

	pip install iCEED

Dependencies:

This package requires the following dependencies:

	Python 3.5 or above
	requests

Usage:

After installing the package, you can use it in your Python code as follows:

	from iCEED import ORGEC2PDB
	orgst = ORGEC2PDB("1.1.1.1", "9606") #Providing EC number and taxonomic ID as input
	print(orgst.orgstr()) # Calling orgstr function and printing result

The Examples folder contains detailed examples for each module.

Contributions:

We welcome contributions from the community! If you find a bug, have a feature request, or would like to contribute code, please contact us via email.

License:

This package is licensed under the MIT license. See the LICENSE file for more details.



