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Technology Detail
Technology DescriptionOperon prediction for sequenced bacterial genomes without experimental information
CategoryTechnology
PRCUniversity of Michigan
PubMed ID17122389
AuthorBergman NH, Passalacqua KD, Hanna PC, Qin ZS
Publication DescriptionAn algorithm has been developed to predict operons in a wide range of bacterial genomes for the purpose of discovering new functional relationships among genes.
MethodologyWe use phylogenetic information to aid in operon prediction, and we constructed a Bayesian hidden Markov model that incorporates comparative genomic data with traditional predictors, such as intergenic distances.
Resourcehttp://www.sph.umich.edu/~qin/hmm/
Contact National Institute of Allergy and Infectious Disease National Institutes of Health National Institutes of Health