genome majority vote improves gene predictions基因组多数投票提高基因预测.pdfVIP

Genome Majority Vote Improves Gene Predictions 1,2,3 1,4 1 5 Michael E. Wall *, Sindhu Raghavan , Judith D. Cohn , John Dunbar 1 Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America, 2 Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America, 3 Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America, 4 Department of Computer Science, The University of Texas at Austin, Austin, Texas, United States of America, 5 Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America Abstract Recent studies have noted extensive inconsistencies in gene start sites among orthologous genes in related microbial genomes. Here we provide the first documented evidence that imposing gene start consistency improves the accuracy of gene start-site prediction. We applied an algorithm using a genome majority vote (GMV) scheme to increase the consistency of gene starts among orthologs. We used a set of validated Escherichia coli genes as a standard to quantify accuracy. Results showed that the GMV algorithm can correct hundreds of gene prediction errors in sets of five or ten genomes while introducing few errors. Using a conservative calculation, we project that GMV would resolve many inconsistencies and errors in publicly available microbial gene maps. Our simple and logical solution provides a notable advance toward accurate gene maps. Citation: Wall ME, Raghavan S, Cohn JD, Dunbar J (2011) Genome Majority Vote Improves Gene Predictions. PLoS Comput Biol 7(11): e1002284. doi:10.1371/ journal.pcb