Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome

Chen, X.; Su, Zhengchang; Dam, Phuongan; Palenik, Brian; Xu, Ying; Jiang, Tao

doi:10.1093/nar/gkh510

Cited by 61 publications

(55 citation statements)

References 22 publications

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“…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%

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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confidence: 99%

Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Bergman

Passalacqua

Hanna

et al. 2007

Appl Environ Microbiol

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“…Given are a template pathway consisting of n genes along with operons and regulons covering the whole target genome, predicted by the JPOP program (42,43) (available at http:͞͞csbl.bmb.uga.edu͞downloads͞ #jpop). Also given are a set of homologous gene pairs between the template pathway genes and genes in the target genome, predicted by BLAST (44).…”

Section: Methodsmentioning

confidence: 99%

“…We recently developed an effective method called JPOP (43) for operon prediction. The program uses a neural-network approach to find boundaries between operons on the basis of intergenic distances, COG gene functions, and phylogenetic profiles.…”

Section: Prediction Of Operons: the Prerequisite For Application Of Pmentioning

confidence: 99%

Mapping of orthologous genes in the context of biological pathways: An application of integer programming

Mao¹,

Su²,

Olman³

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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Mapping biological pathways across microbial genomes is a highly important technique in functional studies of biological systems. Existing methods mainly rely on sequence-based orthologous gene mapping, which often leads to suboptimal mapping results because sequence-similarity information alone does not contain sufficient information for accurate identification of orthology relationship. Here we present an algorithm for pathway mapping across microbial genomes. The algorithm takes into account both sequence similarity and genomic structure information such as operons and regulons. One basic premise of our approach is that a microbial pathway could generally be decomposed into a few operons or regulons. We formulated the pathway-mapping problem to map genes across genomes to maximize their sequence similarity under the constraint that the mapped genes be grouped into a few operons, preferably coregulated in the target genome. We have developed an integer-programming algorithm for solving this constrained optimization problem and implemented the algorithm as a computer software program, P-MAP. We have tested P-MAP on a number of known homologous pathways. We conclude that using genomic structure information as constraints could greatly improve the pathway-mapping accuracy over methods that use sequence-similarity information alone.genomic structure ͉ operon ͉ regulon ͉ ortholog ͉ pathway mapping C omparative genome analysis represents a powerful technique for functional inference of genes. Its foundation is the ability to identify homologous (or more specifically, orthologous) genes. Here orthologous genes refer to isofunctional and heterospecic genes (1-3) for practical purposes. Several methods have been developed for mapping orthologous genes through sequence comparison. A popular approach is based on reciprocal BLAST searches, the so-called bidirectional best-hit (BDBH) approach (4). Based on this strategy, Wall et al. (5) recently designed a more sophisticated scheme for finding orthologous genes through BLAST searching followed by a more accurate sequence-alignment scheme (e.g., CLUSTALW and PAML). Koonin and co-workers (6) developed a popular method for orthologous gene identification based on the idea of clusters of orthologs groups (COG). Whereas all these approaches have provided useful practical tools for prediction of orthologous genes, their prediction accuracy has been less than optimal, as discussed below. One of the key issues with all these methods is their underlying assumption that sequence similarity alone contains sufficient information for prediction of orthologous gene relationship, which is probably far from being true.One important piece of information for orthologous gene identification across microbial genomes could come from the genomic structures such as operons (7) and regulons (8). Using such information has proven to be very helpful in orthology mapping in our previous studies (9, 10). Based on this observation, we have developed an algorithm, called P-MAP, for mapping orthologous...

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“…Finally, spatial comparative genomics can also help predict protein function and regulation. In bacteria, conserved gene order and content have been used for prediction of operons, horizontal transfers, and more generally to help understand the relationship between spatial organization and functional selection [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

The Incompatible Desiderata of Gene Cluster Properties

Hoberman

Durand

2005

Comparative Genomics

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Abstract. There is widespread interest in comparative genomics in determining if historically and/or functionally related genes are spatially clustered in the genome, and whether the same sets of genes reappear in clusters in two or more genomes. We formalize and analyze the desirable properties of gene clusters and cluster definitions. Through detailed analysis of two commonly applied types of cluster, r-windows and maxgap, we investigate the extent to which a single definition can embody all of these properties simultaneously. We show that many of the most important properties are difficult to satisfy within the same definition. We also examine whether one commonly assumed property, which we call nestedness, is satisfied by the structures present in real genomic data.

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Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome

Cited by 61 publications

References 22 publications

Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Operon Prediction for Sequenced Bacterial Genomes without Experimental Information

Mapping of orthologous genes in the context of biological pathways: An application of integer programming

The Incompatible Desiderata of Gene Cluster Properties

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