BACKGROUND: Several motif detection algorithms have been developed to discover overrepresented motifs in sets of coexpressed genes. However, in a noisy gene list, the number of genes containing the motif versus the number lacking the motif might not be sufficiently high to allow detection by classical motif detection tools. To still recover motifs which are not significantly enriched but still present, we developed a procedure in which we use phylogenetic footprinting to first delineate all potential motifs in each gene. Then we mutually compare all detected motifs and identify the ones that are shared by at least a few genes in the data set as potential candidates. RESULTS: We applied our methodology to a compiled test data set containing known regulatory motifs and to two biological data sets derived from genome wide expression studies. By executing four consecutive steps of 1) identifying conserved regions in orthologous intergenic regions, 2) aligning these conserved regions, 3) clustering the conserved regions containing similar regulatory regions followed by extraction of the regulatory motifs and 4) screening the input intergenic sequences with detected regulatory motif models, our methodology proves to be a powerful tool for detecting regulatory motifs when a low signal to noise ratio is present in the input data set. Comparing our results with two other motif detection algorithms points out the robustness of our algorithm. CONCLUSION: We developed an approach that can reliably identify multiple regulatory motifs lacking a high degree of overrepresentation in a set of coexpressed genes (motifs belonging to sparsely connected hubs in the regulatory network) by exploiting the advantages of using both coexpression and phylogenetic information.

MOTIVATION: Microarray experiments generate a considerable amount of data, which analyzed properly help us gain a huge amount of biologically relevant information about the global cellular behaviour. Clustering (grouping genes with similar expression profiles) is one of the first steps in data analysis of high-throughput expression measurements. A number of clustering algorithms have proved useful to make sense of such data. These classical algorithms, though useful, suffer from several drawbacks (e.g. they require the predefinition of arbitrary parameters like the number of clusters; they force every gene into a cluster despite a low correlation with other cluster members). In the following we describe a novel adaptive quality-based clustering algorithm that tackles some of these drawbacks. RESULTS: We propose a heuristic iterative two-step algorithm: First, we find in the high-dimensional representation of the data a sphere where the "density" of expression profiles is locally maximal (based on a preliminary estimate of the radius of the cluster-quality-based approach). In a second step, we derive an optimal radius of the cluster (adaptive approach) so that only the significantly coexpressed genes are included in the cluster. This estimation is achieved by fitting a model to the data using an EM-algorithm. By inferring the radius from the data itself, the biologist is freed from finding an optimal value for this radius by trial-and-error. The computational complexity of this method is approximately linear in the number of gene expression profiles in the data set. Finally, our method is successfully validated using existing data sets. AVAILABILITY: http://www.esat.kuleuven.ac.be/~thijs/Work/Clustering.html

Abstract Summary: INCLUSive allows automatic multistep analysis of microarray data (clustering and motif finding). The clustering algorithm (adaptive quality-based clustering) groups together genes with highly similar expression profiles. The upstream sequences of the genes belonging to a cluster are automatically retrieved from GenBank and can be fed directly into Motif Sampler, a Gibbs sampling algorithm that retrieves statistically over-represented motifs in sets of sequences, in this case upstream regions of co-expressed genes. Availability: For academic purposes at http://www.esat.kuleuven.ac.be/~dna/BioI/Software.html Contact: [email protected] * To whom correspondence should be addressed. Email: [email protected].

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.

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.

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: [email protected]; [email protected]