Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2% of the translation starts of GenBank annotated Bacillus subtilis genes and 94.4% of translation starts in an experimentally validated set of Escherichia coli genes. We have also observed that GeneMarkS detects prokaryotic genes, in terms of identifying open reading frames containing real genes, with an accuracy matching the level of the best currently used gene detection methods. Accurate translation start prediction, in addition to the refinement of protein sequence N-terminal data, provides the benefit of precise positioning of the sequence region situated upstream to a gene start. Therefore, sequence motifs related to transcription and translation regulatory sites can be revealed and analyzed with higher precision. These motifs were shown to possess a significant variability, the functional and evolutionary connections of which are discussed.

The task of gene identification frequently confronting researchers working with both novel and well studied genomes can be conveniently and reliably solved with the help of the GeneMark web software (). The website provides interfaces to the GeneMark family of programs designed and tuned for gene prediction in prokaryotic, eukaryotic and viral genomic sequences. Currently, the server allows the analysis of nearly 200 prokaryotic and >10 eukaryotic genomes using species-specific versions of the software and pre-computed gene models. In addition, genes in prokaryotic sequences from novel genomes can be identified using models derived on the spot upon sequence submission, either by a relatively simple heuristic approach or by the full-fledged self-training program GeneMarkS. A database of reannotations of >1000 viral genomes by the GeneMarkS program is also available from the web site. The GeneMark website is frequently updated to provide the latest versions of the software and gene models.

Computer methods of accurate gene finding in DNA sequences require models of protein coding and non-coding regions derived either from experimentally validated training sets or from large amounts of anonymous DNA sequence. Here we propose a new, heuristic method producing fairly accurate inhomogeneous Markov models of protein coding regions. The new method needs such a small amount of DNA sequence data that the model can be built 'on the fly' by a web server for any DNA sequence >400 nt. Tests on 10 complete bacterial genomes performed with the GeneMark.hmm program demonstrated the ability of the new models to detect 93.1% of annotated genes on average, while models built by traditional training predict an average of 93.9% of genes. Models built by the heuristic approach could be used to find genes in small fragments of anonymous prokaryotic genomes and in genomes of organelles, viruses, phages and plasmids, as well as in highly inhomogeneous genomes where adjustment of models to local DNA composition is needed. The heuristic method also gives an insight into the mechanism of codon usage pattern evolution.

The IbeA (ibe10) gene is an invasion determinant contributing to E. coli K1 invasion of the blood-brain barrier. This gene has been cloned and characterized from the chromosome of an invasive cerebrospinal fluid isolate of E. coli K1, strain RS218 (018:K1: H7). In the present study, a genetic island of meningitic E. coli containing ibeA (GimA) has been identified. A 20.3-kb genomic DNA island unique to E. coli K1 strains has been cloned and sequenced from an RS218 E. coli K1 genomic DNA library. Fourteen new genes have been identified in addition to the ibeA. The DNA sequence analysis indicated that the ibeA gene cluster was localized to the 98 min region and consisted of four operons, ptnIPKC, cglDTEC, gcxKRCI and ibeRAT. The G+C content (46.2%) of unique regions of the island is substantially different from that (50.8%) of the rest of the E. coli chromosome. By computer-assisted analysis of the sequences with DNA and protein databases (GenBank and PROSITE databases), the functions of the gene products could be anticipated, and were assigned to the functional categories of proteins relating to carbon source metabolism and substrate transportation. Glucose was shown to enhance E. coli penetration of human brain microvascular endothelial cells and exogenous cAMP was able to block the stimulating effect of glucose, suggesting that catabolic regulation may play a role in control of E. coli K1 invasion gene expression. Our data suggest that this genetic island may contribute to E. coli invasion of the blood-brain barrier through a carbon-source-regulated process.

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