Promoter prediction and annotation of microbial genomes based on DNA sequence and structural responses to superhelical stress

BACKGROUND: In our previous studies, we found that the sites in prokaryotic genomes which are most susceptible to duplex destabilization under the negative superhelical stresses that occur in vivo are statistically highly significantly associated with intergenic regions that are known or inferred to contain promoters. In this report we investigate how this structural property, either alone or together with other structural and sequence attributes, may be used to search prokaryotic genomes for promoters. RESULTS: We show that the propensity for stress-induced DNA duplex destabilization (SIDD) is closely associated with specific promoter regions. The extent of destabilization in promoter-containing regions is found to be bimodally distributed. When compared with DNA curvature, deformability, thermostability or sequence motif scores within the -10 region, SIDD is found to be the most informative DNA property regarding promoter locations in the E. coli K12 genome. SIDD properties alone perform better at detecting promoter regions than other programs trained on this genome. Because this approach has a very low false positive rate, it can be used to predict with high confidence the subset of promoters that are strongly destabilized. When SIDD properties are combined with -10 motif scores in a linear classification function, they predict promoter regions with better than 80% accuracy. When these methods were tested with promoter and non-promoter sequences from Bacillus subtilis, they achieved similar or higher accuracies. We also present a strictly SIDD-based predictor for annotating promoter sequences in complete microbial genomes. CONCLUSION: In this report we show that the propensity to undergo stress-induced duplex destabilization (SIDD) is a distinctive structural attribute of many prokaryotic promoter sequences. We have developed methods to identify promoter sequences in prokaryotic genomes that use SIDD either as a sole predictor or in combination with other DNA structural and sequence properties. Although these methods cannot predict all the promoter-containing regions in a genome, they do find large sets of potential regions that have high probabilities of being true positives. This approach could be especially valuable for annotating those genomes about which there is limited experimental data

Superhelical Destabilization in Regulatory Regions of Stress Response Genes

Stress-induced DNA duplex destabilization (SIDD) analysis exploits the known structural and energetic properties of DNA to predict sites that are susceptible to strand separation under negative superhelical stress. When this approach was used to calculate the SIDD profile of the entire Escherichia coli K12 genome, it was found that strongly destabilized sites occur preferentially in intergenic regions that are either known or inferred to contain promoters, but rarely occur in coding regions. Here, we investigate whether the genes grouped in different functional categories have characteristic SIDD properties in their upstream flanks. We report that strong SIDD sites in the E. coli K12 genome are statistically significantly overrepresented in the upstream regions of genes encoding transcriptional regulators. In particular, the upstream regions of genes that directly respond to physiological and environmental stimuli are more destabilized than are those regions of genes that are not involved in these responses. Moreover, if a pathway is controlled by a transcriptional regulator whose gene has a destabilized 5′ flank, then the genes (operons) in that pathway also usually contain strongly destabilized SIDD sites in their 5′ flanks. We observe this statistically significant association of SIDD sites with upstream regions of genes functioning in transcription in 38 of 43 genomes of free-living bacteria, but in only four of 18 genomes of endosymbionts or obligate parasitic bacteria. These results suggest that strong SIDD sites 5′ to participating genes may be involved in transcriptional responses to environmental changes, which are known to transiently alter superhelicity. We propose that these SIDD sites are active and necessary participants in superhelically mediated regulatory mechanisms governing changes in the global pattern of gene expression in prokaryotes in response to physiological or environmental changes

PromBase: a web resource for various genomic features and predicted promoters in prokaryotic genomes

<p>Abstract</p> <p>Background</p> <p>As more and more genomes are being sequenced, an overview of their genomic features and annotation of their functional elements, which control the expression of each gene or transcription unit of the genome, is a fundamental challenge in genomics and bioinformatics.</p> <p>Findings</p> <p>Relative stability of DNA sequence has been used to predict promoter regions in 913 microbial genomic sequences with GC-content ranging from 16.6% to 74.9%. Irrespective of the genome GC-content the relative stability based promoter prediction method has already been proven to be robust in terms of recall and precision. The predicted promoter regions for the 913 microbial genomes have been accumulated in a database called PromBase. Promoter search can be carried out in PromBase either by specifying the gene name or the genomic position. Each predicted promoter region has been assigned to a reliability class (low, medium, high, very high and highest) based on the difference between its average free energy and the downstream region. The recall and precision values for each class are shown graphically in PromBase. In addition, PromBase provides detailed information about base composition, CDS and CG/TA skews for each genome and various DNA sequence dependent structural properties (average free energy, curvature and bendability) in the vicinity of all annotated translation start sites (TLS).</p> <p>Conclusion</p> <p>PromBase is a database, which contains predicted promoter regions and detailed analysis of various genomic features for 913 microbial genomes. PromBase can serve as a valuable resource for comparative genomics study and help the experimentalist to rapidly access detailed information on various genomic features and putative promoter regions in any given genome. This database is freely accessible for academic and non- academic users via the worldwide web <url>http://nucleix.mbu.iisc.ernet.in/prombase/</url>.</p

Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks

<p>Abstract</p> <p>Background</p> <p>One of the major challenges in biology is the correct identification of promoter regions. Computational methods based on motif searching have been the traditional approach taken. Recent studies have shown that DNA structural properties, such as curvature, stacking energy, and stress-induced duplex destabilization (SIDD) are useful in promoter prediction, as well. In this paper, the currently used SIDD energy threshold method is compared to the proposed artificial neural network (ANN) approach for finding promoters based on SIDD profile data.</p> <p>Results</p> <p>When compared to the SIDD threshold prediction method, artificial neural networks showed noticeable improvements for precision, recall, and <it>F</it>-score over a range of values. The maximal <it>F</it>-score for the ANN classifier was 62.3 and 56.8 for the threshold-based classifier.</p> <p>Conclusions</p> <p>Artificial neural networks were used to predict promoters based on SIDD profile data. Results using this technique were an improvement over the previous SIDD threshold approach. Over a wide range of precision-recall values, artificial neural networks were more capable of identifying distinctive characteristics of promoter regions than threshold based methods.</p

The Human Genomic Melting Map

In a living cell, the antiparallel double-stranded helix of DNA is a dynamically changing structure. The structure relates to interactions between and within the DNA strands, and the array of other macromolecules that constitutes functional chromatin. It is only through its changing conformations that DNA can organize and structure a large number of cellular functions. In particular, DNA must locally uncoil, or melt, and become single-stranded for DNA replication, repair, recombination, and transcription to occur. It has previously been shown that this melting occurs cooperatively, whereby several base pairs act in concert to generate melting bubbles, and in this way constitute a domain that behaves as a unit with respect to local DNA single-strandedness. We have applied a melting map calculation to the complete human genome, which provides information about the propensities of forming local bubbles determined from the whole sequence, and present a first report on its basic features, the extent of cooperativity, and correlations to various physical and biological features of the human genome. Globally, the melting map covaries very strongly with GC content. Most importantly, however, cooperativity of DNA denaturation causes this correlation to be weaker at resolutions fewer than 500 bps. This is also the resolution level at which most structural and biological processes occur, signifying the importance of the informational content inherent in the genomic melting map. The human DNA melting map may be further explored at http://meltmap.uio.no

nocoRNAc: Characterization of non-coding RNAs in prokaryotes

<p>Abstract</p> <p>Background</p> <p>The interest in non-coding RNAs (ncRNAs) constantly rose during the past few years because of the wide spectrum of biological processes in which they are involved. This led to the discovery of numerous ncRNA genes across many species. However, for most organisms the non-coding transcriptome still remains unexplored to a great extent. Various experimental techniques for the identification of ncRNA transcripts are available, but as these methods are costly and time-consuming, there is a need for computational methods that allow the detection of functional RNAs in complete genomes in order to suggest elements for further experiments. Several programs for the genome-wide prediction of functional RNAs have been developed but most of them predict a genomic locus with no indication whether the element is transcribed or not.</p> <p>Results</p> <p>We present <smcaps>NOCO</smcaps>RNAc, a program for the genome-wide prediction of ncRNA transcripts in bacteria. <smcaps>NOCO</smcaps>RNAc incorporates various procedures for the detection of transcriptional features which are then integrated with functional ncRNA loci to determine the transcript coordinates. We applied RNAz and <smcaps>NOCO</smcaps>RNAc to the genome of <it>Streptomyces coelicolor </it>and detected more than 800 putative ncRNA transcripts most of them located antisense to protein-coding regions. Using a custom design microarray we profiled the expression of about 400 of these elements and found more than 300 to be transcribed, 38 of them are predicted novel ncRNA genes in intergenic regions. The expression patterns of many ncRNAs are similarly complex as those of the protein-coding genes, in particular many antisense ncRNAs show a high expression correlation with their protein-coding partner.</p> <p>Conclusions</p> <p>We have developed <smcaps>NOCO</smcaps>RNAc, a framework that facilitates the automated characterization of functional ncRNAs. <smcaps>NOCO</smcaps>RNAc increases the confidence of predicted ncRNA loci, especially if they contain transcribed ncRNAs. <smcaps>NOCO</smcaps>RNAc is not restricted to intergenic regions, but it is applicable to the prediction of ncRNA transcripts in whole microbial genomes. The software as well as a user guide and example data is available at <url>http://www.zbit.uni-tuebingen.de/pas/nocornac.htm</url>.</p

An iterative strategy combining biophysical criteria and duration hidden Markov) models for structural predictions of Chlamydia trachomatis s66 promoters

Background: Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from Escherichia coli. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between Escherichia coli and Chlamydia trachomatis are large enough to recommend an organism-specific modeling effort. Results: Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model Chlamydia trachomatis σ66 promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for Chlamydia trachomatis RNA polymerase σ66/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability. Conclusion: This strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequence\u27s ability to promote transcription. This work provides a baseline model that can evolve as new Chlamydia trachomatis σ66 promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes

Comparative Analysis of Mycoplasma gallisepticum vlhA Promoters

Mycoplasma gallisepticum is an intracellular parasite affecting respiratory tract of poultry that belongs to class Mollicutes. M. gallisepticum features numerous variable lipoprotein hemagglutinin genes (vlhA) that play a role in immune escape. The vlhA promoters have a set of distinct properties in comparison to promoters of the other genes. The vlhA promoters carry a variable GAA repeats region at approximately 40 nts upstream of transcription start site. The promoters have been considered active only in the presence of exactly 12 GAA repeats. The mechanisms of vlhA expression regulation and GAA number variation are not described. Here we tried to understand these mechanisms using different computational methods. We conducted a comparative analysis among several M. gallisepticum strains. Nucleotide sequences analysis showed the presence of highly conserved regions flanking repeated trinucleotides that are not linked to GAA number variation. VlhA genes with 12 GAA repeats and their orthologs in 12 M. gallisepticum strains are more conserved than other vlhA genes and have narrower GAA number distribution. We conducted comparative analysis of physicochemical profiles of M. gallisepticum vlhA and sigma-70 promoters. Stress-induced duplex destabilization (SIDD) profiles showed that sigma-70 group is characterized by the common to prokaryotic promoters sharp maxima while vlhA promoters are hardly destabilized with the region between GAA repeats and transcription start site having zero opening probability. Electrostatic potential profiles of vlhA promoters indicate the presence of the distinct patterns that appear to govern initial stages of specific DNA-protein recognition. Open state dynamics profiles of vlhA demonstrate the pattern that might facilitate transcription bubble formation. Obtained data could be the basis for experimental identification of mechanisms of phase variation in M. gallisepticum