A multi-approaches-guided genetic algorithm with application to operon prediction
Artificial Intelligence in Medicine
Operon Prediction Using Neural Network Based on Multiple Information of Log-Likelihoods
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
PFP: a computational framework for phylogenetic footprinting in prokaryotic genomes
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Operon Prediction Using Chaos Embedded Particle Swarm Optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: 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. Results: We describe an operon predictor designed to work without extensive training data. We rely on a small set of a priori assumptions about the properties of the genome being annotated that permit estimation of the probability that two adjacent genes lie in a common operon. Predictions integrate several sources of information, including intergenic distance, common functional annotation and a novel formulation of conserved gene order. We validate our predictor both on the known operons of E.coli and on the genome of B.theta, using expression data to evaluate our predictions in the latter. Availability: The software is available online at http://www.cse.wustl.edu/~jbuhler/research/operons Contact: jbuhler@cse.wustl.edu