Touati Benoukraf, Memorial University of Newfoundland
The recent advent of third-generation sequencing technologies brings promise for better characterization of genomic structural variants by virtue of having longer reads. However, long-read applications are still constrained by their high sequencing error rates and low sequencing throughput. Here, we present NanoVar, an optimized structural variant caller utilizing low-depth (8X) whole-genome sequencing data generated by Oxford Nanopore Technologies. NanoVar exhibits higher structural variant calling accuracy when benchmarked against current tools using low-depth simulated datasets. In an acute myeloid leukemia patient cohort, we successfully validate structural variants characterized by NanoVar and uncover normal alternative sequences or alleles present in healthy individuals.
Touati Benoukraf focused his research on developing computational methods for analysing large (epi)genomic datasets with the aim of delineating novel biological mechanisms in pathophysiology. After a Ph.D. performed at the Aix-Marseille University, he went to Singapore for postdoctoral training. He then gained his scientific independence by being awarded a "Special Fellowship" at the Cancer Science Institute of Singapore, followed by a Canada Research Chair (Tier 2) in Bioinformatics for Personalized Medicine at the Memorial University of Newfoundland.