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BridgeIRIS: BRussels big Data platform for sharing and discovery in clinical GEnomics

Partners:

Funding: Innoviris

Duration: 2014-2016

Project Overview:

Genome-wide tests are nowadays pervasive in medicine and some of them have become routine (e.g. array CGH) or will soon become (exome or whole genome). On the one hand these genome-wide tests provide an unprecedented opportunity for improving the quality and yield of diagnosis, treatment prescription and research http://storecialis.net/generic-cialis/. For example, in rare disease diagnostics, exome sequencing increases resolution of cases dramatically from ~5% five-fold to 25%. On the other hand they demand hospitals to seek up-to-date, certified and reliable (bio)informatics solutions to store, manage and analyse such a huge avalanche of data. This project arises from specific needs of the Centers for Medical Genetics of the VUB,  ULB and UCL for 1) a reliable storage and easy access to clinical and phenotypic data as well as massive amounts of high- throughput genomic data, 2) extracting more information from genetic tests in an automated and validated manner, and 3) deploying the extracted knowledge in clinical routine decisions. These needs are made more urgent by the imminent availability of a joint VUB/ULB Next-Generation Sequencing platform, and by the presumably upcoming reimbursement of genomic tests by the Belgian National Health Service. Those technologies produce masses of genomic data waiting to be stored, organised, analysed and valorised. 

Project Objectives:

  1. Design and creation of a multi-site phenomic and genomic data warehouse compliant with issues of interoperability, privacy, security, scalability and reliability: as of today, no integrated computer solution is available to store, analyse and collaborate around the large amounts of unstructured data which are already accumulating in ULB/VUB/UCL medical genetics centers.
  2. Development of automated tools (including quality checking and mapping pipelines, pre- processing, dimensionality reduction and multivariate classification) for extracting relevant information from genetic data with focus on i) integrating relevant information related to copy number variation (CNV) and single-nucleotide variants coming from array CGH and exome sequencing respectively ii) shift from a monogenic analysis of genomic data to a multigenic approach by means of feature selection and dimensionality reduction approaches at first and by re-analysis of very large numbers of array CGH and exomes samples at a later stage looking for statistical association of variants at one gene or locus with a specific phenotype iii) analysis of the incidentalome, i.e., the known variants associated with known pathologies incidentally discovered by genome-wide profiling but for which the analysis was not initially prescribed. The possibility of screening for disease before the onset of symptoms for every patient in an automated fashion provides the opportunity of a shift towards preventive genetic medicine.
  3. Use of the designed tools to extract new knowledge and transfer it to the medical setting with focus on three presumably oligogenic diseases (cardiac arrhythmia disorder Brugada Syndrome (BrS), epileptic encephalopathies and cleft lip and/or palate (CL/P)) and possible extension to other diseases (pulmonary hypertension, brain malformations, complex pediatric neurodevelopmental disorders, and mitochondrial diseases). The final goal is to provide reliable diagnostic predictor tools to the clinicians and to develop a framework for other presumably oligo/polygenic disorders.

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