deepTAD: an approach for identifying topologically associated domains based on convolutional neural network and transformer model.

Journal: Briefings in bioinformatics
PMID: 40131313

Abstract

MOTIVATION: Topologically associated domains (TADs) play a key role in the 3D organization and function of genomes, and accurate detection of TADs is essential for revealing the relationship between genomic structure and function. Most current methods are developed to extract features in Hi-C interaction matrix to identify TADs. However, due to complexities in Hi-C contact matrices, it is difficult to directly extract features associated with TADs, which prevents current methods from identifying accurate TADs.

Authors

  • Xiaoyan Wang
    Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
  • Junwei Luo
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Lili Wu
    Research Center for Integrative Medicine of Guangzhou University of Chinese Medicine, Guangzhou, 510006, P. R. China.
  • Huimin Luo
  • Fei Guo
    School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China. Electronic address: gfjy001@yahoo.com.

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