Pairwise interactions between RNA molecules are fundamental to many biological processes, including translation, splicing, and gene regulation. Traditionally, RNA-RNA interactions have been mapped experimentally one at a time, or predicted computationally. To facilitate the study of RNA interactomes, we have recently developed CLASH (Cross-linking, Ligation and Sequencing of Hybrids), an experimental technique that captures RNA-RNA interactions by physically joining two RNA molecules into a single chimeric RNA molecule, followed by next-generation sequencing. I will discuss the applications of this approach in the study of ribosome biogenesis, gene regulation by microRNAs, and splicing. I will also present a new experimental approach that will allow comprehensive mapping of genetic interactions within and between genes, at a single nucleotide resolution.

Within the next few years, as sequencing technologies get faster and cheaper, tenths of thousands of newly sequenced genomes will become available (see for example [1, 2]). In contrast, experimental characterisation of genomes’ functional elements, which is expected to lead to breakthroughs in understanding molecular and cellular processes [3], remains in most cases an expensive and painstakingly slow process. In this scenario, computational biology appears as one scalable way to make sense of the vast amount of sequence data that are being deposited (daily) in the public domain. While computational predictions cannot substitute for experiments, they can generate hypotheses to guide targeted experimental validation. In this seminar, I will illustrate ongoing work that aims to shed some light on the “Dark Matter” of the protein sequence universe [4]. Additionally, I will discuss ideas to improve annotation of pathways, protein complexes and organelles in fully sequenced organisms.

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With the increasing availability of whole genome sequencing data, one has now access to a wealth of somatic mutation events specific to patients carrying a cancer. Classically, researchers focus on the events lying within protein-coding regions in order to extract the ones disruptive to protein structure and/or function. One can use whole-genome sequencing information to extract mutations lying within cis-regulatory regions with potential impact on gene regulation. We will present our study of 84 matched tumor-normal whole genomes of B-cell lymphomas where we specifically focused on mutations overlapping transcription factor binding sites (TFBSs) for their potential impact on gene expression regulation. We highlight the importance of moving beyond the protein-coding subset of the genome to analyze the cis-regulatory space. TFBSs representing the core of gene cis-regulation, we will finally introduce our transcription factor flexible models representing the next generation of TFBS prediction.

Reverse engineering of gene regulatory relationships from genomics datais a crucial task to dissect the complex underlying regulatory mechanismoccurring in a cell. From a computational point of view thereconstruction of gene regulatory networks is an undetermined problem asthe large number of possible solutions is typically high in contrast tothe number of available independent data points. Many possible solutionscan fit the available data, explaining the data equally well, but onlyone of them can be the biologically true solution. Several strategieshave been proposed in literature to reduce the search space and/orextend the amount of independent information. Moreover, several methodshave been developed to infer gene networks from steady-state data, muchless literature is produced about time-course data, so the developmentof algorithms to infer gene networks from time-series measurements is acurrent challenge into bioinformatics research area. Here I will present two recently proposed algorithms, the first termedTimeDelay-ARACNE tries to extract dependencies between pairs of genesat different time…

DNA microarrays have been intensively used in the past years for gene expression studies and for the analysis of full genomes. These devices are high throughput and rely on the hybridization between target sequences in solution and sequences covalently linked at a solid surface. During this talk I will review some results of the analysis of dedicated experiments to characterize the thermodynamics of the hybridization process in microarrays. I will show that accurate nearest-neighbor model parameters can be obtained from the analysis of hybridizations with sequences carrying one or more mismatches. These inputs can be used to design accurate diagnostic tools for the analysis of samples containing mutations. As an example the case of HIV will be discussed. References: J. Hooyberghs, M. Baiesi, A. Ferrantini and E. Carlon "Breakdown of thermodynamic equilibrium for DNA hybridization in microarrays" Phys. Rev. E 81, 012901 (2010) W. Hadiwikarta, J.-C.Walter, J. Hooyberghs and E. Carlon ,…

Polarisation in yeast cells consists of an aggregation of proteins (here CdC42) on the membrane which becomes asymmetric. The two main mechanisms involved in the breaking of symmetry in this process seem to be : -reaction-diffusion mechanism -actin mediated transport Experiments have shown that the polar cap size depends on the local curvature of the affected cell and more precisely that it is the actin mediated transport which is crucial to obtain this correlation. The aim of this talk is to give some hypotheses which may explain the mechanisms underlying this correlation. To this, we in particular use a mathematical model in order to test different components in reaction diffusions and actin transport mechanisms which may influence the cap size of cDc42. This work is made in collaboration with D. Bonazzi and N. Minc from the Institut Jacques Monod, part of Université Paris Diderot.