DOMINE: a comprehensive collection of known and predicted domain-domain interactions.
Abstract
DOMINE is a comprehensive collection of known and predicted domain-domain interactions
(DDIs) compiled from 15 different sources. The updated DOMINE includes 2285 new domain-domain
interactions (DDIs) inferred from experimentally characterized high-resolution three-dimensional
structures, and about 3500 novel predictions by five computational approaches published
over the last 3 years. These additions bring the total number of unique DDIs in the
updated version to 26,219 among 5140 unique Pfam domains, a 23% increase compared
to 20,513 unique DDIs among 4346 unique domains in the previous version. The updated
version now contains 6634 known DDIs, and features a new classification scheme to
assign confidence levels to predicted DDIs. DOMINE will serve as a valuable resource
to those studying protein and domain interactions. Most importantly, DOMINE will not
only serve as an excellent reference to bench scientists testing for new interactions
but also to bioinformaticans seeking to predict novel protein-protein interactions
based on the DDIs. The contents of the DOMINE are available at http://domine.utdallas.edu.
Type
Journal articlePermalink
https://hdl.handle.net/10161/18161Published Version (Please cite this version)
10.1093/nar/gkq1229Publication Info
Yellaboina, Sailu; Tasneem, Asba; Zaykin, Dmitri V; Raghavachari, Balaji; & Jothi,
Raja (2011). DOMINE: a comprehensive collection of known and predicted domain-domain interactions.
Nucleic acids research, 39(Database issue). pp. D730-D735. 10.1093/nar/gkq1229. Retrieved from https://hdl.handle.net/10161/18161.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Asba Tasneem
Clinical Trials Project Leader II
Ph.D., University of Illinois at Urbana-Champaign - 2005
My overarching goal is to facilitate open science and open access of research data
by providing thought leadership, guidance, and support to Duke researchers in data
sharing. I work with Research Investigators and Statistical Scientists to manage data-driven
projects. My work includes overseeing implementation of innovative software solutions
in pragmatic clinical tr

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