PMID:15539453

Citation	Westover, BP, Buhler, JD, Sonnenburg, JL and Gordon, JI (2005) Operon prediction without a training set. Bioinformatics 21:880-8
Abstract	Annotation of operons in a bacterial genome is an important step in determining an organism's transcriptional regulatory program. While extensive studies of operon structure have been carried out in a few species such as Escherichia coli, fewer resources exist to inform operon prediction in newly sequenced genomes. In particular, many extant operon finders require a large body of training examples to learn the properties of operons in the target organism. For newly sequenced genomes, such examples are generally not available; moreover, a model of operons trained on one species may not reflect the properties of other, distantly related organisms. We encountered these issues in the course of predicting operons in the genome of Bacteroides thetaiotaomicron (B.theta), a common anaerobe that is a prominent component of the normal adult human intestinal microbial community.
Links	PubMed Online version:10.1093/bioinformatics/bti123
Keywords	Algorithms; Artificial Intelligence; Bacteroides/genetics; Chromosome Mapping/methods; Escherichia coli/genetics; Genome, Bacterial; Models, Genetic; Models, Statistical; Operon/genetics; Pattern Recognition, Automated/methods; Software
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