The Gibbs Centroid Sampler

The Gibbs Centroid Sampler is a software package designed for locating conserved elements in biopolymer sequences. The Gibbs Centroid Sampler reports a centroid alignment, i.e. an alignment that has the minimum total distance to the set of samples chosen from the a posteriori probability distribution of transcription factor binding-site alignments. In so doing, it garners information from the full ensemble of solutions, rather than only the single most probable point that is the target of many motif-finding algorithms, including its predecessor, the Gibbs Recursive Sampler. Centroid estimators have been shown to yield substantial improvements, in both sensitivity and positive predictive values, to the prediction of RNA secondary structure and motif finding. The Gibbs Centroid Sampler, along with interactive tutorials, an online user manual, and information on downloading the software, is available at: http://bayesweb.wadsworth.org/gibbs/gibbs.html

Bayesian Centroid Estimation for Motif Discovery

Biological sequences may contain patterns that are signal important biomolecular functions; a classical example is regulation of gene expression by transcription factors that bind to specific patterns in genomic promoter regions. In motif discovery we are given a set of sequences that share a common motif and aim to identify not only the motif composition, but also the binding sites in each sequence of the set. We present a Bayesian model that is an extended version of the model adopted by the Gibbs motif sampler, and propose a new centroid estimator that arises from a refined and meaningful loss function for binding site inference. We discuss the main advantages of centroid estimation for motif discovery, including computational convenience, and how its principled derivation offers further insights about the posterior distribution of binding site configurations. We also illustrate, using simulated and real datasets, that the centroid estimator can differ from the maximum a posteriori estimator.Comment: 24 pages, 9 figure

PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling

PhyloGibbs, our recent Gibbs-sampling motif-finder, takes phylogeny into account in detecting binding sites for transcription factors in DNA and assigns posterior probabilities to its predictions obtained by sampling the entire configuration space. Here, in an extension called PhyloGibbs-MP, we widen the scope of the program, addressing two major problems in computational regulatory genomics. First, PhyloGibbs-MP can localise predictions to small, undetermined regions of a large input sequence, thus effectively predicting cis-regulatory modules (CRMs) ab initio while simultaneously predicting binding sites in those modules—tasks that are usually done by two separate programs. PhyloGibbs-MP's performance at such ab initio CRM prediction is comparable with or superior to dedicated module-prediction software that use prior knowledge of previously characterised transcription factors. Second, PhyloGibbs-MP can predict motifs that differentiate between two (or more) different groups of regulatory regions, that is, motifs that occur preferentially in one group over the others. While other “discriminative motif-finders” have been published in the literature, PhyloGibbs-MP's implementation has some unique features and flexibility. Benchmarks on synthetic and actual genomic data show that this algorithm is successful at enhancing predictions of differentiating sites and suppressing predictions of common sites and compares with or outperforms other discriminative motif-finders on actual genomic data. Additional enhancements include significant performance and speed improvements, the ability to use “informative priors” on known transcription factors, and the ability to output annotations in a format that can be visualised with the Generic Genome Browser. In stand-alone motif-finding, PhyloGibbs-MP remains competitive, outperforming PhyloGibbs-1.0 and other programs on benchmark data

The Effect of Orthology and Coregulation on Detecting Regulatory Motifs

Background: Computational de novo discovery of transcription factor binding sites is still a challenging problem. The growing number of sequenced genomes allows integrating orthology evidence with coregulation information when searching for motifs. Moreover, the more advanced motif detection algorithms explicitly model the phylogenetic relatedness between the orthologous input sequences and thus should be well adapted towards using orthologous information. In this study, we evaluated the conditions under which complementing coregulation with orthologous information improves motif detection for the class of probabilistic motif detection algorithms with an explicit evolutionary model. Methodology: We designed datasets (real and synthetic) covering different degrees of coregulation and orthologous information to test how well Phylogibbs and Phylogenetic sampler, as representatives of the motif detection algorithms with evolutionary model performed as compared to MEME, a more classical motif detection algorithm that treats orthologs independently. Results and Conclusions: Under certain conditions detecting motifs in the combined coregulation-orthology space is indeed more efficient than using each space separately, but this is not always the case. Moreover, the difference in success rate between the advanced algorithms and MEME is still marginal. The success rate of motif detection depends on the complex interplay between the added information and the specificities of the applied algorithms. Insights in this relation provide information useful to both developers and users. All benchmark datasets are available at http://homes.esat.kuleuven.be/,kmarchal/Supplementary_Storms_Valerie_PlosONE

Finding regulatory DNA motifs using alignment-free evolutionary conservation information

As an increasing number of eukaryotic genomes are being sequenced, comparative studies aimed at detecting regulatory elements in intergenic sequences are becoming more prevalent. Most comparative methods for transcription factor (TF) binding site discovery make use of global or local alignments of orthologous regulatory regions to assess whether a particular DNA site is conserved across related organisms, and thus more likely to be functional. Since binding sites are usually short, sometimes degenerate, and often independent of orientation, alignment algorithms may not align them correctly. Here, we present a novel, alignment-free approach for using conservation information for TF binding site discovery. We relax the definition of conserved sites: we consider a DNA site within a regulatory region to be conserved in an orthologous sequence if it occurs anywhere in that sequence, irrespective of orientation. We use this definition to derive informative priors over DNA sequence positions, and incorporate these priors into a Gibbs sampling algorithm for motif discovery. Our approach is simple and fast. It requires neither sequence alignments nor the phylogenetic relationships between the orthologous sequences, yet it is more effective on real biological data than methods that do

Modeling an Evolutionary Conserved Circadian Cis-Element

Circadian oscillator networks rely on a transcriptional activator called CLOCK/CYCLE (CLK/CYC) in insects and CLOCK/BMAL1 or NPAS2/BMAL1 in mammals. Identifying the targets of this heterodimeric basic-helix-loop-helix (bHLH) transcription factor poses challenges and it has been difficult to decipher its specific sequence affinity beyond a canonical E-box motif, except perhaps for some flanking bases contributing weakly to the binding energy. Thus, no good computational model presently exists for predicting CLK/CYC, CLOCK/BMAL1, or NPAS2/BMAL1 targets. Here, we use a comparative genomics approach and first study the conservation properties of the best-known circadian enhancer: a 69-bp element upstream of the Drosophila melanogaster period gene. This fragment shows a signal involving the presence of two closely spaced E-box–like motifs, a configuration that we can also detect in the other four prominent CLK/CYC target genes in flies: timeless, vrille, Pdp1, and cwo. This allows for the training of a probabilistic sequence model that we test using functional genomics datasets. We find that the predicted sequences are overrepresented in promoters of genes induced in a recent study by a glucocorticoid receptor-CLK fusion protein. We then scanned the mouse genome with the fly model and found that many known CLOCK/BMAL1 targets harbor sequences matching our consensus. Moreover, the phase of predicted cyclers in liver agreed with known CLOCK/BMAL1 regulation. Taken together, we built a predictive model for CLK/CYC or CLOCK/BMAL1-bound cis-enhancers through the integration of comparative and functional genomics data. Finally, a deeper phylogenetic analysis reveals that the link between the CLOCK/BMAL1 complex and the circadian cis-element dates back to before insects and vertebrates diverged

Phyloscan: locating transcription-regulating binding sites in mixed aligned and unaligned sequence data

The transcription of a gene from its DNA template into an mRNA molecule is the first, and most heavily regulated, step in gene expression. Especially in bacteria, regulation is typically achieved via the binding of a transcription factor (protein) or small RNA molecule to the chromosomal region upstream of a regulated gene. The protein or RNA molecule recognizes a short, approximately conserved sequence within a gene's promoter region and, by binding to it, either enhances or represses expression of the nearby gene. Since the sought-for motif (pattern) is short and accommodating to variation, computational approaches that scan for binding sites have trouble distinguishing functional sites from look-alikes. Many computational approaches are unable to find the majority of experimentally verified binding sites without also finding many false positives. Phyloscan overcomes this difficulty by exploiting two key features of functional binding sites: (i) these sites are typically more conserved evolutionarily than are non-functional DNA sequences; and (ii) these sites often occur two or more times in the promoter region of a regulated gene. The website is free and open to all users, and there is no login requirement. Address: (http://bayesweb.wadsworth.org/phyloscan/)

De Novo Transcription Factor Binding Site Discovery: A Machine Learning And Model Selection Approach

Computational methods have been widely applied to the problem of predicting regulatory elements. Many tools have been proposed. Each has taken a different approach and has been based on different underlying sets of assumptions, frequently similar to those of other tools. To date, the accuracy of each individual tool has been relatively poor. Noting that different tools often report different results, common practice is to analyze a given set of regulatory regions using more than one tool and to manually compare the results. Recently, ensemble approaches have been proposed that automate the execution of a set of tools and aggregate the results. This has been seen to provide some improvement but is still handled in an ad hoc manner since tool outputs are often in dissimilar formats. Another approach to improve accuracy has been to investigate the objective functions currently in use and identify additional informational statistics to incorporate into them. As a result of this investigation, one statistical measure of positional specificity has been demonstrated to be informative. In this context, this thesis explores the application of three simple models for the positional distribution of transcription factor binding sites (TFBS) to the problem of TFBS discovery. As alternate measures of positional specificity, log-likelihood ratios for the three models are calculated and treated as features to classify TFBSs as biologically relevant or irrelevant. As a verification step, randomly generated positional distributions are analyzed to demonstrate the robustness and accuracy of the log-likelihood ratios at classifying data from known distributions using a simple classifier. To improve classification accuracy, a support vector machine (SVM) approach is used. Subsequently, randomly generated sequences seeded with TFBSs at positions chosen to conform to one of the three models are analyzed as an additional verification step. Finally, two types of sets of real regulatory region sequences are analyzed. First, results consistent with the literature are obtained in three cases for genes experimentally determined to be co-expressed during mouse thymocyte maturation, and a novel role is predicted for three families of TFBSs in single positive (SP) T-cells. Second, the mouse and human ―real‖ sets from Tompa et al’s ―Assessment of Computational Motif Discovery Tools‖ are analyzed, and the results are reported

Bacterial regulon modeling and prediction based on systematic \u3ci\u3ecis\u3c/i\u3e regulatory motif analyses

Regulons are the basic units of the response system in a bacterial cell, and each consists of a set of transcriptionally co-regulated operons. Regulon elucidation is the basis for studying the bacterial global transcriptional regulation network. In this study, we designed a novel co-regulation score between a pair of operons based on accurate operon identification and cis regulatory motif analyses, which can capture their co-regulation relationship much better than other scores. Taking full advantage of this discovery, we developed a new computational framework and built a novel graph model for regulon prediction. This model integrates the motif comparison and clustering and makes the regulon prediction problem substantially more solvable and accurate. To evaluate our prediction, a regulon coverage score was designed based on the documented regulons and their overlap with our prediction; and a modified Fisher Exact test was implemented to measure how well our predictions match the co-expressed modules derived from E. coli microarray gene-expression datasets collected under 466 conditions. The results indicate that our program consistently performed better than others in terms of the prediction accuracy. This suggests that our algorithms substantially improve the state-of-the-art, leading to a computational capability to reliably predict regulons for any bacteria