title: Mining high-throughput experimental data to link gene and function.
abstract: Nearly 2200 genomes that encode around 6 million proteins have now been sequenced. Around 40% of these proteins are of unknown function, even when function is loosely and minimally defined as 'belonging to a superfamily'. In addition to in silico methods, the swelling stream of high-throughput experimental data can give valuable clues for linking these unknowns with precise biological roles. The goal is to develop integrative data-mining platforms that allow the scientific community at large to access and utilize this rich source of experimental knowledge. To this end, we review recent advances in generating whole-genome experimental datasets, where this data can be accessed, and how it can be used to drive prediction of gene function.
authors.affiliation: Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Crysten E
authors.initials: CE
authors.lastname: Blaby-Haas
authors.forename: Valérie
authors.initials: V
authors.lastname: de Crécy-Lagard
mesh_terms: D000818:Animals; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D005796:Genes; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D051379:Mice; D010641:Phenotype