SVhound: detection of regions that harbor yet undetected structural variation

Abstract

Background: Recent population studies are ever growing in number of samples to investigate the diversity of a population or species. These studies reveal new polymorphism that lead to important insights into the mechanisms of evolution, but are also important for the interpretation of these variations. Nevertheless, while the full catalog of variations across entire species remains unknown, we can predict which regions harbor additional not yet detected variations and investigate their properties, thereby enhancing the analysis for potentially missed variants. Results: To achieve this we developed SVhound (https://github.com/lfpaulin/SVhound), which based on a population level SVs dataset can predict regions that harbor unseen SV alleles. We tested SVhound using subsets of the 1000 genomes project data and showed that its correlation (average correlation of 2800 tests r = 0.7136) is high to the full data set. Next, we utilized SVhound to investigate potentially missed or understudied regions across 1KGP and CCDG. Lastly we also apply SVhound on a small and novel SV call set for rhesus macaque (Macaca mulatta) and discuss the impact and choice of parameters for SVhound. Conclusions: SVhound is a unique method to identify potential regions that harbor hidden diversity in model and non model organisms and can also be potentially used to ensure high quality of SV call sets.