The goal of the project is to develop innovative bioinformatics and computational biology strategies aimed at two key challenges. First, it addresses the technological challenge of leveraging the flood of data from next−generation sequencing (NGS) towards mapping genomic and transcriptomic variation. Second, it tackles the scientific challenge of understanding how (human) genetic variation originates and how it leads to differences in (clinical) phenotypes. On the one hand, the project aims at developing innovative computational methods for (1) the mapping and visualization of NGS data (WP1), (2) the identification of relevant noncoding variation (WP2), and (3) the fusion of multiple types of omics data using strategies based on networks and kernels. On the other hand, the project aims at demonstrating the relevance of the proposed computational methods on specific biological challenges (biological mechanisms of copy number variations (WP4), constitutional disorders (microcephaly) (WP5), and cancer (Acute Lymphoblastic Leukemia (ALL) (WP6)).
Affordable sequencing of complete genomes and transcriptomes will revolutionize systems biology. Sequencing technology is progressing at neck−breaking pace. Current estimates put the cost of sequencing a complete human genome between €10,000 and €100,000. he €1000 human genome is rapidly approaching. Beyond genome sequencing, NGS has multiple applications, including sequencing transcriptomes (RNA−seq) as an alternative to expression arraysor sequencing products of chromatine immunoprecipitation (ChIP−seq) for the study of transcriptional regulation and so on. It is critical that a center of excellence with extensive expertise in NGS data analysis emerges at K.U.Leuven. Beyond its spectacular impact on fundamental research, NGS is set to revolutionize clinical research and the Leuven University Hospitals must be at the forefront of this translation effort as they already have been for the introduction of array CGH technology in clinical genetic diagnosis. We will aim at providing an efficient infrastructure to support NGS data handling by the Genomics Core and serving as a competence center for NGS data analysis towards the university community.
Understanding the biological cascades that, starting for genomic variation at one or more loci in the genome, lead to clinically relevant phenotypic variation is a daunting challenge. While NGS data offers us an unprecedented amount of data about genomic variation, sorting through this mass of data to identify the minute fraction of truly relevant variation is essentially an unsolved problem. We then want understand which pathways and networks are affected, for example at the expression level. We want to understand how multiple mutations and pathways interact to give rise to a phenotype, and how they affect the variable penetrance or severity of a phenotype. Answering such questions will require multiple breakthroughs in computational biology and also a truly integrated approach where computational and biological experts work hand in hand in constant interaction. Our results will have a major impact on the area of biological in which we have already established leadership, but will be broadly applicable, both for other teams within the university and other researchers internationally.
Our consortium brings together a team of young, high−potential researchers who have already extensively and successfully collaborated in the past years. Our team has published over twenty joint publications in the past five years, multiple of them in top journals, including Nature Medicine and Nature Biotechnology. Our team has leading expertise in several areas of computational biology, such as NGS data analysis for the detection of structural variation, data integration for the identification of disease causing genes, and cis−regulatory sequence and network analysis. It has also leading expertise in analysis of structural variation in constitutional disorders, of chromosomal instability as a mechanism of structural variation, and of oncogenic mutation and pathways in leukemia. The unique blend of cutting−edge computational biology know−how and biological research in our consortium has put us in unique position to bridge the gap between genomic variation and phenotypic variation, and understand the molecular cascades that flow from variome to phenome in human disorders.