Master of Science in Bio-Engineering: Cell and Gene Biotechnology KULeuven 2012
2015-2016, 2016-2017, 2017-2018
2017-2018: "Bourse de la Loterie Nationale"
Development of a precision de novo assembly algorithm for targeted regions (NOVOLoci)
The project will be realized in collaboration with HUDERF, IB2 and the ULB. The main work will be executed at the IB2.
The goal of the project is to develop an algorithm that is capable of assembling regions of interest de novo from short Illumina reads (NOVOLoci). When we can achieve a high quality assembly, it will be possible to detect new variants (impossible to call with a classic mapping + variant calling alignment) that could help in the diagnosis of individual patients. We will also develop a customized version of NOVOPlasty  for the human mitochondrial genome that can be integrated in to the Highlander platform . This module will not only assemble the mitochondrial genome, but also detect the variance and heteroplasmy in the sample.
We will test NOVOLoci on two datasets, the human genome and the genome of a leaf beetle (Gonioctena intermedia). For the human genome, we will focus on a linkage region, which is associated to congenital nongoitrous hypothyroidism. The beetle dataset is useful to test the algorithm for genomes without reference and as a benchmark, since we already have assemblies from DISCOVAR De Novo and Platanus. With the experience gained on these subjects, we would like to develop a bioinformatic pipeline to assemble regions of interest de novo, followed by the best possible variance calling. With the continuous progress in sequencing technologies, whole genome sequencing and de novo alignment will become the central genomic tool in clinics and research. Being able to produce the best alignment and calling possible and to characterize variants that were not distinguishable with mapping, de novo assembly will strongly contribute to the melioration of the comprehension and diagnosis of rare genetic disorders, a disease category very important for pediatrics