Detection of new genes in a bacterial genome using Markov models for three gene classes

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Abstract
We further investigated the statistical features of the three classes of Escherichia coli genes that have been previously delineated by factorial correspondence analysis and dynamic clustering methods. A phased Markov model for a nucleotide sequence of each gene class was developed and employed for gene prediction using the GeneMark program. The protein-coding region prediction accuracy was determined for classspecific Markov models of different orders when the programs implementing these models were applied to gene sequences from the same or other classes. It is shown that at least two training sets and two program versions derived for different classes of E.coli genes are necessary in order to achieve a high accuracy of coding region prediction for uncharacterized sequences. Some annotated E.coli genes from Class I and Class III are shown to be spurious, whereas many open reading frames (ORFs) that have not been annotated in GenBank as genes are predicted to encode proteins. The amino acid sequences of the putative products of these ORFs initially did not show similarity to already known proteins. However, conserved regions have been identified in several of them by screening the latest entries in protein sequence databases and applying methods for motif search, while some other of these new genes have been identified in independent experiments.