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Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package .

The chemical universe containing organic molecules within a reasonable molecular weight is vast and largely unexplored. Estimations of possible numbers of unique molecules range from 10(13) to 10(180). These numbers have to be compared with the few tens of millions of compounds currently known. Design of libraries that populate the medicinally relevant chemical subspace and tools that help to maximise the chance of identifying leads are necessary. This review describes various molecular representations that lead to the definition of chemical space, drug space or activity space. Strategies for compound selection in such spaces are discussed, as well as potential sources of diversity that could be used to explore the medicinal space in quest of new drugs.

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Natural products are universally recognized to contribute valuable chemical diversity to the design of molecular screening libraries. The analysis undertaken in this work, provides a foundation for the generation of fragment screening libraries that capture the diverse range of molecular recognition building blocks embedded within natural products. Physicochemical properties were used to select fragment-sized natural products from a database of known natural products (Dictionary of Natural Products). PCA analysis was used to illustrate the positioning of the fragment subset within the property space of the non-fragment sized natural products in the dataset. Structural diversity was analysed by three distinct methods: atom function analysis, using pharmacophore fingerprints, atom type analysis, using radial fingerprints, and scaffold analysis. Small pharmacophore triplets, representing the range of chemical features present in natural products that are capable of engaging in molecular interactions with small, contiguous areas of protein binding surfaces, were analysed. We demonstrate that fragment-sized natural products capture more than half of the small pharmacophore triplet diversity observed in non fragment-sized natural product datasets. Atom type analysis using radial fingerprints was represented by a self-organizing map. We examined the structural diversity of non-flat fragment-sized natural product scaffolds, rich in sp3 configured centres. From these results we demonstrate that 2-ring fragment-sized natural products effectively balance the opposing characteristics of minimal complexity and broad structural diversity when compared to the larger, more complex fragment-like natural products. These naturally-derived fragments could be used as the starting point for the generation of a highly diverse library with the scope for further medicinal chemistry elaboration due to their minimal structural complexity. This study highlights the possibility to capture a high proportion of the individual molecular interaction motifs embedded within natural products using a fragment screening library spanning 422 structural clusters and comprised of approximately 2800 natural products.