Prediction of translation initiation site for microbial genomes with TriTISA

We report a new and simple method, TriTISA, for accurate prediction of translation initiation site (TIS) of microbial genomes. TriTISA classifies all candidate TISs into three categories based on evolutionary properties, and characterizes them in terms of Markov models. Then, it employs a Bayesian methodology for the selection of true TIS with a non-supervised, iterative procedure. Assessment on experimentally verified TIS data shows that TriTISA is overall better than all other methods of the state-of-the-art for microbial genome TIS prediction. In particular, TriTISA is shown to have a robust accuracy independent of the quality of initial annotation.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000261996400020&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Biochemical Research MethodsBiotechnology & Applied MicrobiologyComputer Science, Interdisciplinary ApplicationsMathematical & Computational BiologyStatistics & ProbabilitySCI(E)PubMed13ARTICLE1123-1252