10,346 research outputs found

    Recognition of prokaryotic promoters based on a novel variable-window Z-curve method

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    Transcription is the first step in gene expression, and it is the step at which most of the regulation of expression occurs. Although sequenced prokaryotic genomes provide a wealth of information, transcriptional regulatory networks are still poorly understood using the available genomic information, largely because accurate prediction of promoters is difficult. To improve promoter recognition performance, a novel variable-window Z-curve method is developed to extract general features of prokaryotic promoters. The features are used for further classification by the partial least squares technique. To verify the prediction performance, the proposed method is applied to predict promoter fragments of two representative prokaryotic model organisms (Escherichia coli and Bacillus subtilis). Depending on the feature extraction and selection power of the proposed method, the promoter prediction accuracies are improved markedly over most existing approaches: for E. coli, the accuracies are 96.05% (σ70 promoters, coding negative samples), 90.44% (σ70 promoters, non-coding negative samples), 92.13% (known sigma-factor promoters, coding negative samples), 92.50% (known sigma-factor promoters, non-coding negative samples), respectively; for B. subtilis, the accuracies are 95.83% (known sigma-factor promoters, coding negative samples) and 99.09% (known sigma-factor promoters, non-coding negative samples). Additionally, being a linear technique, the computational simplicity of the proposed method makes it easy to run in a matter of minutes on ordinary personal computers or even laptops. More importantly, there is no need to optimize parameters, so it is very practical for predicting other species promoters without any prior knowledge or prior information of the statistical properties of the samples

    SIGffRid: A tool to search for sigma factor binding sites in bacterial genomes using comparative approach and biologically driven statistics

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    <p>Abstract</p> <p>Background</p> <p>Many programs have been developed to identify transcription factor binding sites. However, most of them are not able to infer two-word motifs with variable spacer lengths. This case is encountered for RNA polymerase Sigma (<it>σ</it>) Factor Binding Sites (SFBSs) usually composed of two boxes, called -35 and -10 in reference to the transcription initiation point. Our goal is to design an algorithm detecting SFBS by using combinational and statistical constraints deduced from biological observations.</p> <p>Results</p> <p>We describe a new approach to identify SFBSs by comparing two related bacterial genomes. The method, named SIGffRid (SIGma Factor binding sites Finder using R'MES to select Input Data), performs a simultaneous analysis of pairs of promoter regions of orthologous genes. SIGffRid uses a prior identification of over-represented patterns in whole genomes as selection criteria for potential -35 and -10 boxes. These patterns are then grouped using pairs of short seeds (of which one is possibly gapped), allowing a variable-length spacer between them. Next, the motifs are extended guided by statistical considerations, a feature that ensures a selection of motifs with statistically relevant properties. We applied our method to the pair of related bacterial genomes of <it>Streptomyces coelicolor </it>and <it>Streptomyces avermitilis</it>. Cross-check with the well-defined SFBSs of the SigR regulon in <it>S. coelicolor </it>is detailed, validating the algorithm. SFBSs for HrdB and BldN were also found; and the results suggested some new targets for these <it>σ </it>factors. In addition, consensus motifs for BldD and new SFBSs binding sites were defined, overlapping previously proposed consensuses. Relevant tests were carried out also on bacteria with moderate GC content (i.e. <it>Escherichia coli</it>/<it>Salmonella typhimurium </it>and <it>Bacillus subtilis</it>/<it>Bacillus licheniformis </it>pairs). Motifs of house-keeping <it>σ </it>factors were found as well as other SFBSs such as that of SigW in <it>Bacillus </it>strains.</p> <p>Conclusion</p> <p>We demonstrate that our approach combining statistical and biological criteria was successful to predict SFBSs. The method versatility autorizes the recognition of other kinds of two-box regulatory sites.</p

    N-gram analysis of 970 microbial organisms reveals presence of biological language models

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    <p>Abstract</p> <p>Background</p> <p>It has been suggested previously that genome and proteome sequences show characteristics typical of natural-language texts such as "signature-style" word usage indicative of authors or topics, and that the algorithms originally developed for natural language processing may therefore be applied to genome sequences to draw biologically relevant conclusions. Following this approach of 'biological language modeling', statistical n-gram analysis has been applied for comparative analysis of whole proteome sequences of 44 organisms. It has been shown that a few particular amino acid n-grams are found in abundance in one organism but occurring very rarely in other organisms, thereby serving as genome signatures. At that time proteomes of only 44 organisms were available, thereby limiting the generalization of this hypothesis. Today nearly 1,000 genome sequences and corresponding translated sequences are available, making it feasible to test the existence of biological language models over the evolutionary tree.</p> <p>Results</p> <p>We studied whole proteome sequences of 970 microbial organisms using n-gram frequencies and cross-perplexity employing the Biological Language Modeling Toolkit and Patternix Revelio toolkit. Genus-specific signatures were observed even in a simple unigram distribution. By taking statistical n-gram model of one organism as reference and computing cross-perplexity of all other microbial proteomes with it, cross-perplexity was found to be predictive of branch distance of the phylogenetic tree. For example, a 4-gram model from proteome of <it>Shigellae flexneri 2a</it>, which belongs to the <it>Gammaproteobacteria </it>class showed a self-perplexity of 15.34 while the cross-perplexity of other organisms was in the range of 15.59 to 29.5 and was proportional to their branching distance in the evolutionary tree from <it>S. flexneri</it>. The organisms of this genus, which happen to be pathotypes of <it>E.coli</it>, also have the closest perplexity values with <it>E. coli.</it></p> <p>Conclusion</p> <p>Whole proteome sequences of microbial organisms have been shown to contain particular n-gram sequences in abundance in one organism but occurring very rarely in other organisms, thereby serving as proteome signatures. Further it has also been shown that perplexity, a statistical measure of similarity of n-gram composition, can be used to predict evolutionary distance within a genus in the phylogenetic tree.</p

    SIGffRid: A tool to search for sigma factor binding sites in bacterial genomes using comparative approach and biologically driven statistics

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    <p>Abstract</p> <p>Background</p> <p>Many programs have been developed to identify transcription factor binding sites. However, most of them are not able to infer two-word motifs with variable spacer lengths. This case is encountered for RNA polymerase Sigma (<it>σ</it>) Factor Binding Sites (SFBSs) usually composed of two boxes, called -35 and -10 in reference to the transcription initiation point. Our goal is to design an algorithm detecting SFBS by using combinational and statistical constraints deduced from biological observations.</p> <p>Results</p> <p>We describe a new approach to identify SFBSs by comparing two related bacterial genomes. The method, named SIGffRid (SIGma Factor binding sites Finder using R'MES to select Input Data), performs a simultaneous analysis of pairs of promoter regions of orthologous genes. SIGffRid uses a prior identification of over-represented patterns in whole genomes as selection criteria for potential -35 and -10 boxes. These patterns are then grouped using pairs of short seeds (of which one is possibly gapped), allowing a variable-length spacer between them. Next, the motifs are extended guided by statistical considerations, a feature that ensures a selection of motifs with statistically relevant properties. We applied our method to the pair of related bacterial genomes of <it>Streptomyces coelicolor </it>and <it>Streptomyces avermitilis</it>. Cross-check with the well-defined SFBSs of the SigR regulon in <it>S. coelicolor </it>is detailed, validating the algorithm. SFBSs for HrdB and BldN were also found; and the results suggested some new targets for these <it>σ </it>factors. In addition, consensus motifs for BldD and new SFBSs binding sites were defined, overlapping previously proposed consensuses. Relevant tests were carried out also on bacteria with moderate GC content (i.e. <it>Escherichia coli</it>/<it>Salmonella typhimurium </it>and <it>Bacillus subtilis</it>/<it>Bacillus licheniformis </it>pairs). Motifs of house-keeping <it>σ </it>factors were found as well as other SFBSs such as that of SigW in <it>Bacillus </it>strains.</p> <p>Conclusion</p> <p>We demonstrate that our approach combining statistical and biological criteria was successful to predict SFBSs. The method versatility autorizes the recognition of other kinds of two-box regulatory sites.</p

    Synthetic biology approaches for protein production optimization in bacterial cell factories

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    A survey of DNA motif finding algorithms

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    Background: Unraveling the mechanisms that regulate gene expression is a major challenge in biology. An important task in this challenge is to identify regulatory elements, especially the binding sites in deoxyribonucleic acid (DNA) for transcription factors. These binding sites are short DNA segments that are called motifs. Recent advances in genome sequence availability and in high-throughput gene expression analysis technologies have allowed for the development of computational methods for motif finding. As a result, a large number of motif finding algorithms have been implemented and applied to various motif models over the past decade. This survey reviews the latest developments in DNA motif finding algorithms.Results: Earlier algorithms use promoter sequences of coregulated genes from single genome and search for statistically overrepresented motifs. Recent algorithms are designed to use phylogenetic footprinting or orthologous sequences and also an integrated approach where promoter sequences of coregulated genes and phylogenetic footprinting are used. All the algorithms studied have been reported to correctly detect the motifs that have been previously detected by laboratory experimental approaches, and some algorithms were able to find novel motifs. However, most of these motif finding algorithms have been shown to work successfully in yeast and other lower organisms, but perform significantly worse in higher organisms.Conclusion: Despite considerable efforts to date, DNA motif finding remains a complex challenge for biologists and computer scientists. Researchers have taken many different approaches in developing motif discovery tools and the progress made in this area of research is very encouraging. Performance comparison of different motif finding tools and identification of the best tools have proven to be a difficult task because tools are designed based on algorithms and motif models that are diverse and complex and our incomplete understanding of the biology of regulatory mechanism does not always provide adequate evaluation of underlying algorithms over motif models.Peer reviewedComputer Scienc

    Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes

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    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 σ70 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 α subunit, and then optimally-separated patterns of -35 and -10 boxes, required for interaction with the σ70 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.</p
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