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PMID: 11751220 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

A probabilistic method for identifying start codons in bacterial genomes.

Bioinformatics (Oxford, England) ·Vol. 17 ·No. 12 ·2001-12-00 ·Pages 1123-30

Suzek BE, Ermolaeva MD, Schreiber M, Salzberg SL

Abstract

As the pace of genome sequencing has accelerated, the need for highly accurate gene prediction systems has grown. Computational systems for identifying genes in prokaryotic genomes have sensitivities of 98-99% or higher (Delcher et al., Nucleic Acids Res., 27, 4636-4641, 1999). These accuracy figures are calculated by comparing the locations of verified stop codons to the predictions. Determining the accuracy of start codon prediction is more problematic, however, due to the relatively small number of start sites that have been confirmed by independent, non-computational methods. Nonetheless, the accuracy of gene finders at predicting the exact gene boundaries at both the 5' and 3' ends of genes is of critical importance for microbial genome annotation, especially in light of the important signaling information that is sometimes found on the 5' end of a protein coding region. In this paper we propose a probabilistic method to improve the accuracy of gene identification systems at finding precise translation start sites. The new system, RBSfinder, is tested on a validated set of genes from Escherichia coli, for which it improves the accuracy of start site locations predicted by computational gene finding systems from the range 67-77% to 90% correct.

MeSH Terms
Algorithms Binding Sites Codon, Initiator Genome, Bacterial Models, Genetic Models, Statistical Ribosomes
Chemicals
Codon, Initiator
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Suzek B E
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
Ermolaeva M D
Schreiber M
Salzberg S L
Article Info
Journal
Bioinformatics (Oxford, England)
Abbr.
Bioinformatics
ISSN
1367-4803
Published
2001-12-00
Pages
1123-30
Language
English
Region
England
NLM ID
9808944
Subset
IM
Grants
NLM NIH HHS · R01-LM 06845 · United States
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