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Journal Article

Deep learning identifies and quantifies recombination hotspot determinants


Rapakoulia,  Trisevgeni
Transcriptional Regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Li, Y., Chen, S., Rapakoulia, T., Kuwahara, H., Yip, K. Y., & Gao, X. (2022). Deep learning identifies and quantifies recombination hotspot determinants. Bioinformatics, 2022: btac234. doi:10.1093/bioinformatics/btac234.

Cite as: http://hdl.handle.net/21.11116/0000-000A-751B-5
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 outperforms 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.