Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Abstract

Various computational approaches have been proposed for operon prediction, but most algorithms rely on experimental or functional data that are only available for a small subset of sequenced genomes. In this study, we explored the possibility of using 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. The prediction algorithm performs as well as the best previously reported method, with several significant advantages. It uses fewer data sources and so it is easier to implement, and the method is more broadly applicable than previous methods-it can be applied to essentially every gene in any sequenced bacterial genome. Furthermore, we show that near-optimal performance is easily reached with a generic set of comparative genomes and does not depend on a specific relationship between the subject genome and the comparative set. We applied the algorithm to the Bacillus anthracis genome and found that it successfully predicted all previously verified B. anthracis operons. To further test its performance, we chose a predicted operon (BA1489-92) containing several genes with little apparent functional relatedness and tested their cotranscriptional nature. Experimental evidence shows that these genes are cotranscribed, and the data have interesting implications for B. anthracis biology. Overall, our findings show that this algorithm is capable of highly sensitive and accurate operon prediction in a wide range of bacterial genomes and that these predictions can lead to the rapid discovery of new functional relationships among genes.

The Type III Pantothenate Kinase Encoded by coaX Is Essential for Growth of Bacillus anthracis

“…These results revealed, as suggested by the bioinformatics analyses, that coaX is the first gene in a tricistronic operon. This operon assignment is also consistent with the results obtained with the operon prediction algorithm developed by Bergman et al (5).…”

The Global Transcriptional Responses of Bacillus anthracis Sterne (34F 2 ) and a Δ sodA1 Mutant to Paraquat Reveal Metal Ion Homeostasis Imbalances during Endogenous Superoxide Stress

“…The homologs in B. anthracis maintain this gene order (GBAA2368 to -2372), but in B. anthracis, three downstream genes (GBAA2373, -2374, and -2375) that are not present in the B. subtilis dhb region are also overexpressed after paraquat treatment at levels roughly equivalent to those observed for GBAA2368 to -2372 (Table 3, siderophore biosynthesis). Homologs of these three genes are often found near nonribosomal peptide synthesis operons in other bacteria (73), and the operon prediction algorithm described by Bergman et al (5) predicts that cotranscription of these three genes in B. anthracis is highly probable. End point RT-PCR probing for overlapping transcription products confirms that these genes are included in the B. anthracis bac transcriptional unit (data not shown), further highlighting differences between B. subtilis and B. anthracis.…”

“…One prominent group of genes overexpressed by the ⌬sodA1 mutant during logarithmic growth consists of GBAA1778, -1779, and -1780 (up-regulated 49-, 23-, and 31-fold, respectively). An operon prediction algorithm developed by Bergman et al (5) predicts that the first two genes are most certainly transcribed as one unit, whereas inclusion of the third gene is less certain. End point RT-PCR using primers designed to amplify overlapping gene regions showed that these three genes are, in fact, transcribed as one unit (data not shown).…”

Novel Multiprotein Complexes Identified in the Hyperthermophilic Archaeon Pyrococcus furiosus by Non-denaturing Fractionation of the Native Proteome

“…Its genome sequence (29,30) contains 2125 ORFs. A universal feature of prokaryotic genomes is the organization of genes into operons, which form basic transcriptional units (31) and are important in functional genomics. Using a neural network, we predicted that 1460 ORFs in the P. furiosus genome are contained within 470 operons (32), 349 of which were validated using DNA microarray data (33,34).…”