Operons in
            <i>Escherichia coli</i>
            : Genomic analyses and predictions

Salgado, Heladia; Moreno–Hagelsieb, Gabriel; Smith, Temple F.; Collado‐Vides, Julio

doi:10.1073/pnas.110147297

Cited by 317 publications

(344 citation statements)

References 24 publications

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“…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. The distances between genes within an operon tend to be considerably shorter than the distances between genes that are not cotranscribed, and in several recently developed algorithms, intergenic distance was shown to be more informative than any other data source, including even microarray-based expression data (3,7,28,34). In addition, this trend appears to be universal in bacterial genomes, making it a very attractive option for a generalized, portable prediction algorithm (7,15,33).…”

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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%

“…In addition, this trend appears to be universal in bacterial genomes, making it a very attractive option for a generalized, portable prediction algorithm (7,15,33). Unfortunately, intergenic distance alone only allows for a specificity of ϳ65 to 70% when tested on a large set of experimentally verified operons from within the Escherichia coli genome, and so other sources of information must be added to bring the total accuracy to a more acceptable level (28).…”

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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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Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Bergman

Passalacqua

Hanna

et al. 2007

Appl Environ Microbiol

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“…For the Escherichia coli genome, operon pair predictions using the intergenic distance information were 82% accurate. 1,2 An alternative method of operon prediction is based on gene expression measurements. Using cDNA microarray technology, the expression levels can be measured simultaneously for all genes in the genome by measuring the corresponding mRNA concentrations.…”

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confidence: 99%

Predicting the Operon Structure of Bacillus Subtilis Using Operon Length, Intergene Distance, and Gene Expression Information

et al. 2003

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We predict the operon structure of the Bacillus subtilis genome using the average operon length, the distance between genes in base pairs, and the similarity in gene expression measured in time course and gene disruptant experiments. By expressing the operon prediction for each method as a Bayesian probability, we are able to combine the four prediction methods into a Bayesian classifier in a statistically rigorous manner. The discriminant value for the Bayesian classifier can be chosen by considering the associated cost of misclassifying an operon or a nonoperon gene pair. For equal costs, an overall accuracy of 88.7% was found in a leaveone-out analysis for the joint Bayesian classifier, whereas the individual information sources yielded accuracies of 58.1%, 83.1%, 77.3%, and 71.8% respectively. The predicted operon structure based on the joint Bayesian classifier is available from the DBTBS database (http://dbtbs.hgc.jp).

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“…Thus, many previous studies have suggested that constraints on gene expression and function have shaped the organization of genes in the genome. Due to lack of appropriate data related to the three-dimensional genomic organization, earlier studies considered the one-dimensional (linear) organization of genomes, and many of them emphasized the higher levels of genomic organization in prokaryotes compared with eukaryotes; for example, it is known that prokaryotes (unlike most eukaryotes) tend to contain operons of co-transcribed genes with related function 9 .…”

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Three-dimensional eukaryotic genomic organization is strongly correlated with codon usage expression and function

2014

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It has been shown that the distribution of genes in eukaryotic genomes is not random; however, formerly reported relations between gene function and genomic organization were relatively weak. Previous studies have demonstrated that codon usage bias is related to all stages of gene expression and to protein function. Here we apply a novel tool for assessing functional relatedness, codon usage frequency similarity (CUFS), which measures similarity between genes in terms of codon and amino acid usage. By analyzing chromosome conformation capture data, describing the three-dimensional (3D) conformation of the DNA, we show that the functional similarity between genes captured by CUFS is directly and very strongly correlated with their 3D distance in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, mouse and human. This emphasizes the importance of threedimensional genomic localization in eukaryotes and indicates that codon usage is tightly linked to genome architecture.

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Operons in Escherichia coli : Genomic analyses and predictions

Cited by 317 publications

References 24 publications

Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Predicting the Operon Structure of Bacillus Subtilis Using Operon Length, Intergene Distance, and Gene Expression Information

Three-dimensional eukaryotic genomic organization is strongly correlated with codon usage expression and function

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