Dataset Information

Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes.

ABSTRACT: BACKGROUND: Bacterial promoters, which increase the efficiency of gene expression, differ from other promoters by several characteristics. This difference, not yet widely exploited in bioinformatics, looks promising for the development of relevant computational tools to search for strong promoters in bacterial genomes. RESULTS: We describe a new triad pattern algorithm that predicts strong promoter candidates in annotated bacterial genomes by matching specific patterns for the group I sigma70 factors of Escherichia coli RNA polymerase. It detects promoter-specific motifs by consecutively matching three patterns, consisting of an UP-element, required for interaction with the alpha subunit, and then optimally-separated patterns of -35 and -10 boxes, required for interaction with the sigma70 subunit of RNA polymerase. Analysis of 43 bacterial genomes revealed that the frequency of candidate sequences depends on the A+T content of the DNA under examination. The accuracy of in silico prediction was experimentally validated for the genome of a hyperthermophilic bacterium, Thermotoga maritima, by applying a cell-free expression assay using the predicted strong promoters. In this organism, the strong promoters govern genes for translation, energy metabolism, transport, cell movement, and other as-yet unidentified functions. CONCLUSION: The triad pattern algorithm developed for predicting strong bacterial promoters is well suited for analyzing bacterial genomes with an A+T content of less than 62%. This computational tool opens new prospects for investigating global gene expression, and individual strong promoters in bacteria of medical and/or economic significance.

SUBMITTER: Dekhtyar M

PROVIDER: S-EPMC2412878 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes.

Dekhtyar Michael M Morin Amelie A Sakanyan Vehary V

BMC bioinformatics 20080509

<h4>Background</h4>Bacterial promoters, which increase the efficiency of gene expression, differ from other promoters by several characteristics. This difference, not yet widely exploited in bioinformatics, looks promising for the development of relevant computational tools to search for strong promoters in bacterial genomes.<h4>Results</h4>We describe a new triad pattern algorithm that predicts strong promoter candidates in annotated bacterial genomes by matching specific patterns for the group ...[more]

PMID: 18471287

Similar Datasets

Project description:BackgroundOver the last few decades, computational genomics has tremendously contributed to decipher biology from genome sequences and related data. Considerable effort has been devoted to the prediction of transcription promoter and terminator sites that represent the essential "punctuation marks" for DNA transcription. Computational prediction of promoters in prokaryotes is a problem whose solution is far from being determined in computational genomics. The majority of published bacterial promoter prediction tools are based on a consensus-sequences search and they were designed specifically for vegetative σ70 promoters and, therefore, not suitable for promoter prediction in bacteria encoding a lot of σ factors, like actinomycetes.ResultsIn this study we investigated the possibility to identify putative promoters in prokaryotes based on evolutionarily conserved motifs, and focused our attention on GC-rich bacteria in which promoter prediction with conventional, consensus-based algorithms is often not-exhaustive. Here, we introduce G4PromFinder, a novel algorithm that predicts putative promoters based on AT-rich elements and G-quadruplex DNA motifs. We tested its performances by using available genomic and transcriptomic data of the model microorganisms Streptomyces coelicolor A3(2) and Pseudomonas aeruginosa PA14. We compared our results with those obtained by three currently available promoter predicting algorithms: the σ70consensus-based PePPER, the σ factors consensus-based bTSSfinder, and PromPredict which is based on double-helix DNA stability. Our results demonstrated that G4PromFinder is more suitable than the three reference tools for both the genomes. In fact our algorithm achieved the higher accuracy (F1-scores 0.61 and 0.53 in the two genomes) as compared to the next best tool that is PromPredict (F1-scores 0.46 and 0.48). Consensus-based algorithms produced lower performances with the analyzed GC-rich genomes.ConclusionsOur analysis shows that G4PromFinder is a powerful tool for promoter search in GC-rich bacteria, especially for bacteria coding for a lot of σ factors, such as the model microorganism S. coelicolor A3(2). Moreover consensus-based tools and, in general, tools that are based on specific features of bacterial σ factors seem to be less performing for promoter prediction in these types of bacterial genomes.

Project description:Bacterial blight, caused by Xanthomonas oryzae pv. oryzae (Xoo), is the most important bacterial disease in rice (Oryza sativa L.). Our previous studies have revealed that the bacterial blight resistance gene Xa23 from wild rice O. rufipogon Griff. confers the broadest-spectrum resistance against all the naturally occurring Xoo races. As a novel executor R gene, Xa23 is transcriptionally activated by the bacterial avirulence (Avr) protein AvrXa23 via binding to a 28-bp DNA element (EBEAvrXa23) in the promoter region. So far, the evolutionary mechanism of Xa23 remains to be illustrated. Here, a rice germplasm collection of 97 accessions, including 29 rice cultivars (indica and japonica) and 68 wild relatives, was used to analyze the evolution, phylogeographic relationship and association of Xa23 alleles with bacterial blight resistance. All the ~ 473 bp DNA fragments consisting of promoter and coding regions of Xa23 alleles in the germplasm accessions were PCR-amplified and sequenced, and nine single nucleotide polymorphisms (SNPs) were detected in the promoter regions (~131 bp sequence upstream from the start codon ATG) of Xa23/xa23 alleles while only two SNPs were found in the coding regions. The SNPs in the promoter regions formed 5 haplotypes (Pro-A, B, C, D, E) which showed no significant difference in geographic distribution among these 97 rice accessions. However, haplotype association analysis indicated that Pro-A is the most favored haplotype for bacterial blight resistance. Moreover, SNP changes among the 5 haplotypes mostly located in the EBE/ebe regions (EBEAvrXa23 and corresponding ebes located in promoters of xa23 alleles), confirming that the EBE region is the key factor to confer bacterial blight resistance by altering gene expression. Polymorphism analysis and neutral test implied that Xa23 had undergone a bottleneck effect, and selection process of Xa23 was not detected in cultivated rice. In addition, the Xa23 coding region was found highly conserved in the Oryza genus but absent in other plant species by searching the plant database, suggesting that Xa23 originated along with the diversification of the Oryza genus from the grass family during evolution. This research offers a potential for flexible use of novel Xa23 alleles in rice breeding programs and provide a model for evolution analysis of other executor R genes.

Project description:BACKGROUND: The process of horizontal gene transfer (HGT) is believed to be widespread in Bacteria and Archaea, but little comparative data is available addressing its occurrence in complete microbial genomes. Collection of high-quality, automated HGT prediction data based on phylogenetic evidence has previously been impractical for large numbers of genomes at once, due to prohibitive computational demands. DarkHorse, a recently described statistical method for discovering phylogenetically atypical genes on a genome-wide basis, provides a means to solve this problem through lineage probability index (LPI) ranking scores. LPI scores inversely reflect phylogenetic distance between a test amino acid sequence and its closest available database matches. Proteins with low LPI scores are good horizontal gene transfer candidates; those with high scores are not. DESCRIPTION: The DarkHorse algorithm has been applied to 955 microbial genome sequences, and the results organized into a web-searchable relational database, called the DarkHorse HGT Candidate Resource http://darkhorse.ucsd.edu. Users can select individual genomes or groups of genomes to screen by LPI score, search for protein functions by descriptive annotation or amino acid sequence similarity, or select proteins with unusual G+C composition in their underlying coding sequences. The search engine reports LPI scores for match partners as well as query sequences, providing the opportunity to explore whether potential HGT donor sequences are phylogenetically typical or atypical within their own genomes. This information can be used to predict whether or not sufficient information is available to build a well-supported phylogenetic tree using the potential donor sequence. CONCLUSION: The DarkHorse HGT Candidate database provides a powerful, flexible set of tools for identifying phylogenetically atypical proteins, allowing researchers to explore both individual HGT events in single genomes, and large-scale HGT patterns among protein families and genome groups. Although the DarkHorse algorithm cannot, by itself, provide definitive proof of horizontal gene transfer, it is a flexible, powerful tool that can be combined with slower, more rigorous methods in situations where these other methods could not otherwise be applied.

Dataset Information

Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes.

Publications

Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets