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Genome Majority Vote Improves Gene Predictions

Overview of attention for article published in PLoS Computational Biology, November 2011
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Title
Genome Majority Vote Improves Gene Predictions
Published in
PLoS Computational Biology, November 2011
DOI 10.1371/journal.pcbi.1002284
Pubmed ID
Authors

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

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 13%
Sweden 1 2%
Germany 1 2%
Denmark 1 2%
South Africa 1 2%
Unknown 37 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 36%
Student > Ph. D. Student 13 28%
Student > Postgraduate 4 9%
Student > Bachelor 3 6%
Professor 3 6%
Other 3 6%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 64%
Biochemistry, Genetics and Molecular Biology 5 11%
Computer Science 2 4%
Immunology and Microbiology 2 4%
Veterinary Science and Veterinary Medicine 1 2%
Other 3 6%
Unknown 4 9%