Gert Thijs

Assessing computational tools for the discovery of transcription factor binding sites

The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling

MOTIVATION Transcriptome analysis allows detection and clustering of genes that are coexpressed under various biological circumstances. Under the assumption that coregulated genes share cis-acting regulatory elements, it is important to investigate the upstream sequences controlling the transcription of these genes. To improve the robustness of the Gibbs sampling algorithm to noisy data sets we propose an extension of this algorithm for motif finding with a higher-order background model. RESULTS Simulated data and real biological data sets with well-described regulatory elements are used to test the influence of the different background models on the performance of the motif detection algorithm. We show that the use of a higher-order model considerably enhances the performance of our motif finding algorithm in the presence of noisy data. For Arabidopsis thaliana, a reliable background model based on a set of carefully selected intergenic sequences was constructed. AVAILABILITY Our implementation of the Gibbs sampler called the Motif Sampler can be used through a web interface: http://www.esat.kuleuven.ac.be/~thijs/Work/MotifSampler.html. CONTACT gert.thijs@esat.kuleuven.ac.be; yves.moreau@esat.kuleuven.ac.be

A Gibbs sampling method to detect over-represented motifs in the upstream regions of co-expressed genes

Microarray experiments can reveal useful information on the transcriptional regulation. We try to find regulatory elements in the region upstream of translation start of coexpressed genes. Here we present a modification to the original Gibbs Sampling algorithm [12]. We introduce a probability distribution to estimate the number of copies of the motif in a sequence. The second modification is the incorporation of a higher-order background model. We have successfully tested our algorithm on several data sets. First we show results on two selected data set: sequences from plants containing the G-box motif and the upstream sequences from bacterial genes regulated by O2-responsive protein FNR. In both cases the motif sampler is able to find the expected motifs. Finally, the sampler is tested on 4 clusters of coexpressed genes from a wounding experiment in Arabidopsis thaliana. We find several putative motifs that are related to the pathways involved in the plant defense mechanism.

A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes

Microarray experiments can reveal important information about transcriptional regulation. In our case, we look for potential promoter regulatory elements in the upstream region of coexpressed genes. Here we present two modifications of the original Gibbs sampling algorithm for motif finding (Lawrence et al., 1993). First, we introduce the use of a probability distribution to estimate the number of copies of the motif in a sequence. Second, we describe the technical aspects of the incorporation of a higher-order background model whose application we discussed in Thijs et al. (2001). Our implementation is referred to as the Motif Sampler. We successfully validate our algorithm on several data sets. First, we show results for three sets of upstream sequences containing known motifs: 1) the G-box light-response element in plants, 2) elements involved in methionine response in Saccharomyces cerevisiae, and 3) the FNR O(2)-responsive element in bacteria. We use these data sets to explain the influence of the parameters on the performance of our algorithm. Second, we show results for upstream sequences from four clusters of coexpressed genes identified in a microarray experiment on wounding in Arabidopsis thaliana. Several motifs could be matched to regulatory elements from plant defence pathways in our database of plant cis-acting regulatory elements (PlantCARE). Some other strong motifs do not have corresponding motifs in PlantCARE but are promising candidates for further analysis.

TOUCAN 2: the all-inclusive open source workbench for regulatory sequence analysis

We present the second and improved release of the TOUCAN workbench for cis-regulatory sequence analysis. TOUCAN implements and integrates fast state-of-the-art methods and strategies in gene regulation bioinformatics, including algorithms for comparative genomics and for the detection of cis-regulatory modules. This second release of TOUCAN has become open source and thereby carries the potential to evolve rapidly. The main goal of TOUCAN is to allow a user to come to testable hypotheses regarding the regulation of a gene or of a set of co-regulated genes. TOUCAN can be launched from this location: .

Comprehensive analysis of the base composition around the transcription start site in Metazoa.

BackgroundThe transcription start site of a metazoan gene remains poorly understood, mostly because there is no clear signal present in all genes. Now that several sequenced metazoan genomes have been annotated, we have been able to compare the base composition around the transcription start site for all annotated genes across multiple genomes.ResultsThe most prominent feature in the base compositions is a significant local variation in G+C content over a large region around the transcription start site. The change is present in all animal phyla but the extent of variation is different between distinct classes of vertebrates, and the shape of the variation is completely different between vertebrates and arthropods. Furthermore, the height of the variation correlates with CpG frequencies in vertebrates but not in invertebrates and it also correlates with gene expression, especially in mammals. We also detect GC and AT skews in all clades (where %G is not equal to %C or %A is not equal to %T respectively) but these occur in a more confined region around the transcription start site and in the coding region.ConclusionsThe dramatic changes in nucleotide composition in humans are a consequence of CpG nucleotide frequencies and of gene expression, the changes in Fugu could point to primordial CpG islands, and the changes in the fly are of a totally different kind and unrelated to dinucleotide frequencies.

INCLUSive: a web portal and service registry for microarray and regulatory sequence analysis

INCLUSive is a suite of algorithms and tools for the analysis of gene expression data and the discovery of cis-regulatory sequence elements. The tools allow normalization, filtering and clustering of microarray data, functional scoring of gene clusters, sequence retrieval, and detection of known and unknown regulatory elements using probabilistic sequence models and Gibbs sampling. All tools are available via different web pages and as web services. The web pages are connected and integrated to reflect a methodology and facilitate complex analysis using different tools. The web services can be invoked using standard SOAP messaging. Example clients are available for download to invoke the services from a remote computer or to be integrated with other applications. All services are catalogued and described in a web service registry. The INCLUSive web portal is available for academic purposes at http://www.esat.kuleuven.ac.be/inclusive.

In silico identification and experimental validation of PmrAB targets in Salmonella typhimurium by regulatory motif detection

BackgroundThe PmrAB (BasSR) two-component regulatory system is required for Salmonella typhimurium virulence. PmrAB-controlled modifications of the lipopolysaccharide (LPS) layer confer resistance to cationic antibiotic polypeptides, which may allow bacteria to survive within macrophages. The PmrAB system also confers resistance to Fe3+-mediated killing. New targets of the system have recently been discovered that seem not to have a role in the well-described functions of PmrAB, suggesting that the PmrAB-dependent regulon might contain additional, unidentified targets.ResultsWe performed an in silico analysis of possible targets of the PmrAB system. Using a motif model of the PmrA binding site in DNA, genome-wide screening was carried out to detect PmrAB target genes. To increase confidence in the predictions, all putative targets were subjected to a cross-species comparison (phylogenetic footprinting) using a Gibbs sampling-based motif-detection procedure. As well as the known targets, we detected additional targets with unknown functions. Four of these were experimentally validated (yibD, aroQ, mig-13 and sseJ). Site-directed mutagenesis of the PmrA-binding site (PmrA box) in yibD revealed specific sequence requirements.ConclusionsWe demonstrated the efficiency of our procedure by recovering most of the known PmrAB-dependent targets and by identifying unknown targets that we were able to validate experimentally. We also pinpointed directions for further research that could help elucidate the S. typhimurium virulence pathway.

Functional bioinformatics of microarray data: from expression to regulation

Using microarrays is a powerful technique to monitor the expression of thousands of genes in a single experiment. From series of such experiments, it is possible to identify the mechanisms that govern the activation of genes in an organism. Short deoxyribonucleic acid patterns (called binding sites) near the genes serve as switches that control gene expression. As a result similar patterns of expression can correspond to similar binding site patterns. Here we integrate clustering of coexpressed genes with the discovery of binding motifs. We overview several important clustering techniques and present a clustering algorithm (called adaptive quality-based clustering), which we have developed to address several shortcomings of existing methods. We overview the different techniques for motif finding, in particular the technique of Gibbs sampling, and we present several extensions of this technique in our Motif Sampler. Finally, we present an integrated web tool called INCLUSive (available online at http://www.esat.kuleuven.ac.be//spl sim/dna/BioI/Software.html) that allows the easy analysis of microarray data for motif finding.

Genome-specific higher-order background models to improve motif detection.

Motif detection based on Gibbs sampling is a common procedure used to retrieve regulatory motifs in silico. Using a species-specific background model was previously shown to increase the robustness of the algorithm. Here, we demonstrate that selecting a non-species-adapted background model can have an adverse effect on the results of motif detection. The large differences in the average nucleotide composition of prokaryotic sequences exacerbate the problem of exchanging background models. Therefore, we have developed complex background models for all prokaryotic species with available genome sequences.