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Project

SymBioSys: from variome to phenome.

The goal of the project is to develop innovative bioinformatics and computational biology strategies aimed at two key challenges. First, itaddresses the technological challenge of leve raging 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 one hand, the project aims at developing innova tive computational methods for (1) the mapping and visualization of NGS data (W P1), (2) the identification of relevant noncoding variation (WP2) and (3) the fusion of multiple types of omics data using strateg based on networks and kernels. On the other han d, the project aims at demonstrating the relevance of the proposed computationa l methods on specific biological challenges (biological mechanisms of copy numb er 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. Cu rrent estimates put the cost of sequencing acomplete human genome between 10,000 and 100,000. The human genome is rapidly ap proaching. Beyond genome sequencing, NGS has multiple applications, including s equencing transcriptomes (RNA-seq) as an alternative to expression arrays or sequencing products of chromatine immunoprecipitation (ChIP-seq) for the stu dy of transcriptional regulation and so on. It is critical that a center of exc ellence with extensive expertise in NGS data analysis emerges at K.U.Leuven. Be yond its spectacular impact on fundamental research, NGS is set to revolutioniz e clinical research and the Leuven University Hospitals must be at the forefron t 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 Cor e and serving as a competence center for NGS data analysis towards the universi ty community.Understanding the biological cascades t hat, starting for genomic variation at one or more loci in the genome, lea d to clinically relevant phenotypic variation is a daunting challenge. While NG S data offers us an unprecedented amount of data about genomic variation, sorti ng through this mass of data to identify the minute fraction of truly relevant variation is essentially an unsolved problem. We then want understand which pat hways and networks are affected, for example at the expression level. We want t o understand how multiple mutations and pathways interact to give rise toa phe notype, and how they affect the variable penetrance or severity ofa 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 maj or impact on the area of biological in which we have already established leader ship, butwill be broadly applicable, both for other teams within the universit y and other researchers internationally. Our consorti um brings together a team of young, high-potential researchwho have already extensively and successfully collaborated in the past years. Our team has publ ished over twenty joint publications in the past five years, multiple of them i n top journals, including NatureMedicine and Biotechnology. Our team has leading expertise in several areas of computational biolog y, such as NGS data analysis for the detection of structuravariation, data in tegration for the identification of disease causing genes, and regulatory sequence and network analys is. It has also leading expertise in analysis of structural variation in constitut ional disorders, of chromosomal instability as a mechanism of structural v ariation, 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 a unique position to bridge the gap between genomi c variation and phenotypic variation, and understand the molecular cascades tha t flow from to in human disorders.
Date:1 Nov 2010 →  31 Oct 2018
Keywords:Bioinformatics
Disciplines:Scientific computing, Bioinformatics and computational biology, Public health care, Public health services