CiteULike: msuarezdiez operon

A novel method for accurate operon predictions in all sequenced prokaryotes

Morgan Price — 2005-03-05T12:30:19-00:00

Nucleic Acids Research, Vol. 33, No. 3. (2005), pp. 880-892.

A multi-approaches-guided genetic algorithm with application to operon prediction

Shuqin Wang — 2008-03-24T12:01:12-00:00

Artificial Intelligence in Medicine, Vol. 41, No. 2. (October 2007), pp. 151-159.

SummaryObjective The prediction of operons is critical to the reconstruction of regulatory networks at the whole genome level. Multiple genome features have been used for predicting operons. However, multiple genome features are usually dealt with using only single method in the literatures. The aim of this paper is to develop a combined method for operon prediction by using different methods to preprocess different genome features in order for exerting their unique characteristics.Methods A novel multi-approach-guided genetic algorithm for operon prediction is presented. We exploit different methods for intergenic distance, cluster of orthologous groups (COG) gene functions, metabolic pathway and microarray expression data. A novel local-entropy-minimization method is proposed to partition intergenic distance. Our program can be used for other newly sequenced genomes by transferring the knowledge that has been obtained from Escherichia coli data. We calculate the log-likelihood for COG gene functions and Pearson correlation coefficient for microarray expression data. The genetic algorithm is used for integrating the four types of data.Results The proposed method is examined on E. coli K12 genome, Bacillus subtilis genome, and Pseudomonas aeruginosa PAO1 genome. The accuracies of prediction for these three genomes are 85.9987%, 88.296%, and 81.2384%, respectively.Conclusion Simulated experimental results demonstrate that in the genetic algorithm the preprocessing for genome data using multiple approaches ensures the effective utilization of different biological characteristics. Experimental results also show that the proposed method is applicable for predicting operons in prokaryote.

Operon prediction for sequenced bacterial genomes without experimental information.

NH Bergman — 2008-03-24T11:58:25-00:00

Appl Environ Microbiol, Vol. 73, No. 3. (February 2007), pp. 846-854.

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.

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

X Chen — 2005-05-09T20:47:13-00:00

Nucleic Acids Res, Vol. 32, No. 7. (2004), pp. 2147-2157.

We present a computational method for operon prediction based on a comparative genomics approach. A group of consecutive genes is considered as a candidate operon if both their gene sequences and functions are conserved across several phylogenetically related genomes. In addition, various supporting data for operons are also collected through the application of public domain computer programs, and used in our prediction method. These include the prediction of conserved gene functions, promoter motifs and terminators. An apparent advantage of our approach over other operon prediction methods is that it does not require many experimental data (such as gene expression data and pathway data) as input. This feature makes it applicable to many newly sequenced genomes that do not have extensive experimental information. In order to validate our prediction, we have tested the method on Escherichia coli K12, in which operon structures have been extensively studied, through a comparative analysis against Haemophilus influenzae Rd and Salmonella typhimurium LT2. Our method successfully predicted most of the 237 known operons. After this initial validation, we then applied the method to a newly sequenced and annotated microbial genome, Synechococcus sp. WH8102, through a comparative genome analysis with two other cyanobacterial genomes, Prochlorococcus marinus sp. MED4 and P.marinus sp. MIT9313. Our results are consistent with previously reported results and statistics on operons in the literature.

Operons in Escherichia coli: Genomic analyses and predictions

Heladia Salgado — 2008-03-24T11:50:07-00:00

Proceedings of the National Academy of Sciences, Vol. 97, No. 12. (June 2000), pp. 6652-6657.

The rich knowledge of operon organization in Escherichia coli, together with the completed chromosomal sequence of this bacterium, enabled us to perform an analysis of distances between genes and of functional relationships of adjacent genes in the same operon, as opposed to adjacent genes in different transcription units. We measured and demonstrated the expected tendencies of genes within operons to have much shorter intergenic distances than genes at the borders of transcription units. A clear peak at short distances between genes in the same operon contrasts with a flat frequency distribution of genes at the borders of transcription units. Also, genes in the same operon tend to have the same physiological functional class. The results of these analyses were used to implement a method to predict the genomic organization of genes into transcription units. The method has a maximum accuracy of 88% correct identification of pairs of adjacent genes to be in an operon, or at the borders of transcription units, and correctly identifies around 75% of the known transcription units when used to predict the transcription unit organization of the E. coli genome. Based on the frequency distance distributions, we estimated a total of 630 to 700 operons in E. coli. This step opens the possibility of predicting operon organization in other bacteria whose genome sequences have been finished.