Predicting bacterial transcription units using sequence and expression data

Bockhorst, Joseph; Qiu, Yu; Glasner, Jeremy D.; Liu, Mingzhu; Blattner, Frederick R.; Craven, Mark

doi:10.1093/bioinformatics/btg1003

Cited by 54 publications

(44 citation statements)

References 21 publications

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“…Given the apparent utility of the phylogenetic barcode information in operon prediction, we next sought to develop a statistical framework in which the predictive value of the data could be tested and into which other information sources could also be added. We and others have observed that the operon prediction problem fits well into an HMM framework (4,34), especially because an HMM framework allows us to estimate the confidence with which each prediction is made (as opposed to rule-based prediction frameworks, which generally do not). Accordingly, we adopted this approach in formulating our algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Examples of the former include methods that rely on microarray-based expression data (3,4,7,26) and others that use different forms of detailed functional annotation (5,30,35). Although these algorithms have shown great promise in terms of being able to predict operon structure with a high degree of specificity and sensitivity, the data they rely on are only available for a select subset of bacterial species, and this limits how widely they can be used.…”

mentioning

confidence: 99%

“…Progress has also been made toward a more generalized method for operon prediction, and a number of groups have constructed algorithms based on a variety of diverse information sources, including codon usage statistics (3,4) and the identification of promoter and terminator sequences (6,28,33,34). Although these data have all proven to be valid predictors of operon structure, it is striking that these studies have also consistently demonstrated that one of the most valuable predictors is simply intergenic distance.…”

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confidence: 99%

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Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Bergman

Passalacqua

Hanna

et al. 2007

Appl Environ Microbiol

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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.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Bergman

Passalacqua

Hanna

et al. 2007

Appl Environ Microbiol

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“…2). k-means clustering was applied 8 with k running from 1 to 16, and the total within-cluster sum of Euclidean distance squares for each clustering was computed. A kink in the sum of squares curve located at a k of 8 indicates that 8 is the optimal number of clusters (32).…”

Section: Strategy For Identifying Genes Regulated By O 2 And/or Fnrmentioning

confidence: 99%

“…Where there are known or predicted polycistronic operons, the other operon members are included for comparison. The transcription start sites are those either predicted by RegulonDB (http:// www.cifn.unam.mx/Computational_Genomics/regulondb/), Bockhorst et al (8), or the Ecocyc database (http://ecocyc.org) or taken from the literature.…”

Section: Strategy For Identifying Genes Regulated By O 2 And/or Fnrmentioning

confidence: 99%

Genome-Wide Expression Analysis Indicates that FNR ofEscherichia coliK-12 Regulates a Large Number of Genes of Unknown Function

Kang¹,

Weber

Qiu³

et al. 2005

J Bacteriol

240

221

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The major regulator controlling the physiological switch between aerobic and anaerobic growth conditions in Escherichia coli is the DNA binding protein FNR. To identify genes controlled by FNR, we used Affymetrix Antisense GeneChips to compare global gene expression profiles from isogenic MG1655 wild-type and ⌬fnr strains grown in glucose minimal media under aerobic or anaerobic conditions. We found that 297 genes contained within 184 operons were regulated by FNR and/or by O 2 levels. The expression of many genes known to be involved in anaerobic respiration and fermentation was increased under anaerobic growth conditions, while that of genes involved in aerobic respiration and the tricarboxylic acid cycle were repressed as expected. The expression of nine operons associated with acid resistance was also increased under anaerobic growth conditions, which may reflect the production of acidic fermentation products. Ninety-one genes with no presently defined function were also altered in expression, including seven of the most highly anaerobically induced genes, six of which we found to be directly regulated by FNR. Classification of the 297 genes into eight groups by k-means clustering analysis indicated that genes with common gene expression patterns also had a strong functional relationship, providing clues for studying the function of unknown genes in each group. Six of the eight groups showed regulation by FNR; while some expression groups represent genes that are simply activated or repressed by FNR, others, such as those encoding functions for chemotaxis and motility, showed a more complex pattern of regulation. A computer search for FNR DNA binding sites within predicted promoter regions identified 63 new sites for 54 genes. We suggest that E. coli MG1655 has a larger metabolic potential under anaerobic conditions than has been previously recognized.

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Bacterial genome organization: comparative expression profiling, operons, regulons, and beyond

Peterson

2005

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

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The sequencing of complete microbial genomes has provided a molecular basis for the diversity of bacterial species. Bacterial gene expression in response to environmental stimuli may be equally diverse and raises the question as to how well what we learn about gene expression in one species translates to another. A problem common to all scientists performing global gene expression lies in determining the significance of gene expression changes as they relate to the functional requirements of the encoded proteins in the biological process under investigation. Four recent studies are presented that highlight some of the issues that confound direct interpretation of gene expression data, including operon structure, functional redundancy of proteins, and the occurrence of nonorthologous genes in bacteria. The concept of comparative expression profiling (CEP) is introduced and discussed in the context of the experimental results observed.

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Predicting bacterial transcription units using sequence and expression data

Abstract: Our experimental results show that we are able to predict operons and localize promoters and terminators with high accuracy. Moreover, our models that use both sequence and expression data are more accurate than those that use only one of these two data sources.

Cited by 54 publications

References 21 publications

Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Genome-Wide Expression Analysis Indicates that FNR ofEscherichia coliK-12 Regulates a Large Number of Genes of Unknown Function

Bacterial genome organization: comparative expression profiling, operons, regulons, and beyond

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