Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.23.217976v1?rss=1 Authors: Bigio, B., Seeleuthner, Y., Kerner, G., Migaud, M., Rosain, J., Boisson, B., Nasca, C., Puel, A., Bustamante, J., Casanova, J.-L., Abel, L., Cobat, A. Abstract: The detection of copy number variations (CNVs) in whole-exome sequencing (WES) data is important, as CNVs may underlie a number of human genetic disorders. The recently developed HMZDelFinder algorithm can detect rare homozygous and hemizygous (HMZ) deletions in WES data more effectively than other widely used tools. Here, we present HMZDelFinder_opt, an approach that outperforms HMZDelFinder for the detection of HMZ deletions, including partial exon deletions in particular, in typical laboratory cohorts that are generated over time under different experimental conditions. We show that using an optimized reference control set of WES data, based on a PCA-derived Euclidean distance for coverage, strongly improves the detection of HMZ deletions both in real patients carrying validated disease-causing deletions and in simulated data. Furthermore, we develop a sliding window approach enabling HMZDelFinder-opt to identify HMZ partial deletions of exons that are otherwise undiscovered by HMZDelFinder. HMZDelFinder_opt is a timely and powerful approach for detecting HMZ deletions, particularly partial exon deletions, in laboratory cohorts, which are typically heterogeneous. Copy rights belong to original authors. Visit the link for more info