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Nucleic Acids Research, 2002, Vol. 30, No. 6 1418-1426
© 2002 Oxford University Press

Mining Bacillus subtilis chromosome heterogeneities using hidden Markov models

Pierre Nicolas1,2,*, Laurent Bize3, Florence Muri2, Mark Hoebeke1, François Rodolphe1, S. Dusko Ehrlich3, Bernard Prum2 and Philippe Bessières1

1Laboratoire de Mathématique, Informatique et Génome, INRA, Route de Saint-Cyr, F-78026 Versailles cedex, France, 2Laboratoire de Statistique et Génome, CNRS, Tour Évry2, 523 place des terrasses de l’Agora, F-91034 Évry, France and 3Laboratoire de Génétique Microbienne, INRA, F-78352 Jouy-en-Josas cedex, France

We present here the use of a new statistical segmentation method on the Bacillus subtilis chromosome sequence. Maximum likelihood parameter estimation of a hidden Markov model, based on the expectation-maximization algorithm, enables one to segment the DNA sequence according to its local composition. This approach is not based on sliding windows; it enables different compositional classes to be separated without prior knowledge of their content, size and localization. We compared these compositional classes, obtained from the sequence, with the annotated DNA physical map, sequence homologies and repeat regions. The first heterogeneity revealed discriminates between the two coding strands and the non-coding regions. Other main heterogeneities arise; some are related to horizontal gene transfer, some to t-enriched composition of hydrophobic protein coding strands, and others to the codon usage fitness of highly expressed genes. Concerning potential and established gene transfers, we found 9 of the 10 known prophages, plus 14 new regions of atypical composition. Some of them are surrounded by repeats, most of their genes have unknown function or possess homology to genes involved in secondary catabolism, metal and antibiotic resistance. Surprisingly, we notice that all of these detected regions are a + t-richer than the host genome, raising the question of their remote sources.

* To whom correspondence should be addressed at: Laboratoire de Mathématique, Informatique et Génome, INRA, Route de Saint-Cyr, F-78026 Versailles cedex, France. Tel: +33 1 30 83 33 52; Fax: +33 1 30 83 33 59; Email: nicolas{at}versailles.inra.fr Present address:Laurent Bize, Laboratoire de Biométrie, INRA, chemin de Borde-Rouge, Auzeville, BP 27, F-31326 Castanet-Tolosan cedex, France


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