Treffer: The COMBREX Project: Design, Methodology, and Initial Results

Title:
The COMBREX Project: Design, Methodology, and Initial Results
Publisher Information:
Public Library of Science
Publication Year:
2013
Collection:
Columbia University: Academic Commons
Document Type:
Fachzeitschrift article in journal/newspaper
Language:
English
DOI:
10.7916/D8P26ZJZ
Accession Number:
edsbas.8B8086F4
Database:
BASE

Weitere Informationen

Prior to the “genomic era,” when the acquisition of DNA sequence involved significant labor and expense, the sequencing of genes was strongly linked to the experimental characterization of their products. Sequencing at that time directly resulted from the need to understand an experimentally determined phenotype or biochemical activity. Now that DNA sequencing has become orders of magnitude faster and less expensive, focus has shifted to sequencing entire genomes. Since biochemistry and genetics have not, by and large, enjoyed the same improvement of scale, public sequence repositories now predominantly contain putative protein sequences for which there is no direct experimental evidence of function. Computational approaches attempt to leverage evidence associated with the ever-smaller fraction of experimentally analyzed proteins to predict function for these putative proteins. Maximizing our understanding of function over the universe of proteins in toto requires not only robust computational methods of inference but also a judicious allocation of experimental resources, focusing on proteins whose experimental characterization will maximize the number and accuracy of follow-on predictions. COMBREX (COMputational BRidges to EXperiments, http://combrex.bu.edu) is an NIH-funded enterprise that has brought computational and experimental biologists together, with the goal of greatly improving our overall understanding of microbial protein function [1],[2]. Since its inception, it has made significant progress toward the following goals: identifying the minority of proteins that have already been experimentally characterized, serving as a public repository of novel protein function predictions made by diverse methods, producing a clear chain of evidence from experiment to prediction, identifying (“recommending”) those functional predictions whose verification will contribute most to our overall understanding of protein function, and actually funding the experiments to test function. The recommendation system is a ...