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Each year diagnostic laboratories in the Netherlands profile thousands of individuals for heritable disease using next‐generation sequencing (NGS). This requires pathogenicity classification of millions of DNA variants on the standard 5‐tier scale. To reduce time spent on data interpretation and increase data quality and reliability, the nine Dutch labs decided to publicly share their classifications. Variant classifications of nearly 100,000 unique variants were catalogued and compared in a centralized MOLGENIS database. Variants classified by more than one center were labeled as “consensus” when classifications agreed, and shared internationally with LOVD and ClinVar. When classifications opposed (LB/B vs. LP/P), they were labeled “conflicting”, while other nonconsensus observations were labeled “no consensus”. We assessed our classifications using the InterVar software to compare to ACMG 2015 guidelines, showing 99.7% overall consistency with only 0.3% discrepancies. Differences in classifications between Dutch labs or between Dutch labs and ACMG were mainly present in genes with low penetrance or for late onset disorders and highlight limitations of the current 5‐tier classification system. The data sharing boosted the quality of DNA diagnostics in Dutch labs, an initiative we hope will be followed internationally. Recently, a positive match with a case from outside our consortium resulted in a more definite disease diagnosis.

Within the context of early drug discovery, a new pharmacophore-based tool to score and align small molecules (Pharao) is described. The tool is built on the idea to model pharmacophoric features by Gaussian 3D volumes instead of the more common point or sphere representations. The smooth nature of these continuous functions has a beneficent effect on the optimization problem introduced during alignment. The usefulness of Pharao is illustrated by means of three examples: a virtual screening of trypsin-binding ligands, a virtual screening of phosphodiesterase 5-binding ligands, and an investigation of the biological relevance of an unsupervised clustering of small ligands based on Pharao.

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.

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 Contact: frank.desmet@esat.kuleuven.ac.be * To whom correspondence should be addressed.

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.

PlantCARE is a database of plant cis-acting regulatory elements, enhancers and repressors. Regulatory elements are represented by positional matrices, consensus sequences and individual sites on particular promoter sequences. Links to the EMBL, TRANSFAC and MEDLINE databases are provided when available. Data about the transcription sites are extracted mainly from the literature, supplemented with an increasing number of in silico predicted data. Apart from a general description for specific transcription factor sites, levels of confidence for the experimental evidence, functional information and the position on the promoter are given as well. New features have been implemented to search for plant cis-acting regulatory elements in a query sequence. Furthermore, links are now provided to a new clustering and motif search method to investigate clusters of co-expressed genes. New regulatory elements can be sent automatically and will be added to the database after curation. The PlantCARE relational database is available via the World Wide Web at http://sphinx.rug.ac.be:8080/PlantCARE/.

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 * To whom correspondence should be addressed.

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: Gert.Thijs@esat.kuleuven.ac.be * To whom correspondence should be addressed. Email: Kathleen.Marchal@esat.kuleuven.ac.be.

The authors use an ensemble of multilayer perceptrons to build a model for a type of gene regulatory sequence called a G-box. A variant of the bagging method (bootstrap-and-aggregate) improves the performance of the ensemble over that of a single network. Through a decomposition of the generalization error of the ensemble into bias and variance components, the authors estimate this error from the hold-out samples of the individual networks. They test the model on putative G-boxes, on sequences upstream of light-regulated genes, and on a control group and demonstrate that the model separates these groups efficiently.

A Spectrophore™ is a one-dimensional descriptor that describes the three-dimensional molecular field surrounding a molecule generated by a given set of atomic properties. In a typical application, Spectrophores™ are calculated from the molecular shape in combination with the electrostatic, lipophilic, softness, and hardness potential surrounding the molecules. Given that molecules with similar three-dimensional properties have similar Spectrophores™ and the real-valued nature of Spectrophores™, they are very well suited to deploy in various virtual screening models. Spectrophores™ have been recently released in the open source domain and have been added as a descriptor in the Open Babel C++ API. To illustrate the capabilities of Spectrophores™ for virtual screening and clustering, a large self-organising map (SOM) was trained from 8.6 million commercially available compounds as a representation of the vendor chemical space. To train a SOM from such a large collection of compounds, a specially tailored algorithm was developed. The resulting SOM clearly shows that the vendor chemical space can be mapped in a meaningful organization based on its Spectrophores™ representation and that it handles compounds beyond their topological similarity. The SOM has been applied in three different problems, including the overlay of vendor databases, the comparison of compounds with different pharmacological profiles, and to sample drug-like screening libraries.