DOMINE: a comprehensive collection of known and predicted domain-domain interactions.
Repository Usage Stats
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.
Published Version (Please cite this version)10.1093/nar/gkq1229
Publication InfoTasneem, Asba; Yellaboina, Sailu; 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.
More InfoShow full item record
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