Deep learning identifies and quantifies recombination hotspot determinants.
Bioinformatics
; 38(10): 2683-2691, 2022 05 13.
Article
en En
| MEDLINE
| ID: mdl-35561158
ABSTRACT
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. AVAILABILITY AND IMPLEMENTATION https//github.com/frankchen121212/RHSNet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Recombinación Genética
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
China