Deep learning identifies and quantifies recombination hotspot determinants

Yu Li*, Siyuan Chen, Trisevgeni Rapakoulia, Hiroyuki Kuwahara, Kevin Y. Yip, Xin Gao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Motivation: 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 propose 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. Results: RHSNet can significantly outperform other sequence-based methods on multiple datasets across different species, sexes and studies. In addition to being able to identify hotspot regions and the well-known determinants accurately, more importantly, RHSNet can quantify the determinants that contribute significantly to the recombination hotspot formation in the relation between PRDM9 binding motif, histone modification and GC content. Further cross-sex, cross-population and cross-species studies suggest that the proposed method has the generalization power and potential to identify and quantify the evolutionary determinant motifs.

Original languageEnglish (US)
Pages (from-to)2683-2691
Number of pages9
Issue number10
StatePublished - May 15 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


Dive into the research topics of 'Deep learning identifies and quantifies recombination hotspot determinants'. Together they form a unique fingerprint.

Cite this