Genome Majority Vote Improves Gene Predictions

Michael E. Wall is with Los Alamos National Laboratory, Sindhu Raghavan is with UT Austin and Los Alamos National Laboratory, Judith D. Cohn is with Los Alamos National Laboratory, John Dunbar is with Los Alamos National Laboratory.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.This work was primarily funded by Los Alamos National Laboratory Directed Research and Development program (LDRD) grant 20080138DR. MEW and JDC received additional support from NIH/National Library of Medicine grant R01LM010120, and MEW received additional support from LDRD grant 20110435DR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Computer Science