Author:
Xu Sheng,Wei Junkang,Sun Siqi,Zhang Jizhou,Chan Ting-Fung,Li Yu
Abstract
AbstractSingle-strand breaks are the major DNA damage in the genome and serve a crucial role in various biological processes. To reveal the significance of single-strand breaks, multiple sequencing-based single-strand break detection methods have been developed, which are costly and unfeasible for large-scale analysis. Hence, we propose SSBlazer, an explainable and scalable deep learning framework for single-strand break site prediction at the nucleotide level. SSBlazer is a lightweight model with robust generalization capabilities across various species and is capable of numerous unexplored SSB-related applications.
Funder
Chinese University of Hong Kong
Research Grants Council of the Hong Kong Special Administrative Region
Innovation and Technology Commission of the Hong Kong Special Administrative Region
Publisher
Springer Science and Business Media LLC