RT Journal Article
SR Electronic
T1 Deep learning identifies and quantifies recombination hotspot determinants
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.07.29.454133
DO 10.1101/2021.07.29.454133
A1 Li, Yu
A1 Chen, Siyuan
A1 Rapakoulia, Trisevgeni
A1 Kuwahara, Hiroyuki
A1 Yip, Kevin Y.
A1 Gao, Xin
YR 2021
UL http://biorxiv.org/content/early/2021/07/29/2021.07.29.454133.abstract
AB Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known to be related to the hotspots, their contributions to the recombination hotspots have not been quantified, and other determinants are yet to be elucidated. Here, we develop a computational method, RHSNet, based on deep learning and signal processing, to identify and quantify the hotspot determinants in a purely data-driven manner, utilizing datasets from various studies, populations, sexes, and species. In addition to being able to identify hotspot regions and the well-known determinants accurately, RHSNet is sensitive to the difference between different PRDM9 alleles and different sexes, and can generalize to PRDM9-lacking species. The cross-sex, cross-population, and cross-species studies suggest that the proposed method has the potential to identify and quantify the evolutionary determinant motifs.Teaser RHSNet can accurately identify and quantify recombination hotspot determinants across different studies, sexes, populations, and species.Competing Interest StatementThe authors have declared no competing interest.