Genome Majority Vote Improves Gene Predictions

By Michael E. Wall, Sindhu Raghavan, Judith D. Cohn, John Dunbar. 2011

17 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

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In Molecular Biology

Citations in the corpus

SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery (2013, methodology): “Such rank-by-vote approaches have been used to build consensus among conflicting information in other fields, such as virtual screening and gene expression prediction[33-36].”