CGLoop: a neural network framework for chromatin loop prediction.

Journal: BMC genomics
PMID: 40186170

Abstract

BACKGROUND: Chromosomes of species exhibit a variety of high-dimensional organizational features, and chromatin loops, which are fundamental structures in the three-dimensional (3D) structure of the genome. Chromatin loops are visible speckled patterns on Hi-C contact matrix generated by chromosome conformation capture methods. The chromatin loops play an important role in gene expression, and predicting the chromatin loops generated during whole genome interactions is crucial for a deeper understanding of the 3D genome structure and function.

Authors

  • Junfeng Wang
    Department of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China.
  • Lili Wu
    Research Center for Integrative Medicine of Guangzhou University of Chinese Medicine, Guangzhou, 510006, P. R. China.
  • Jingjing Wei
    Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center; State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200438, China.
  • Chaokun Yan
    School of Computer Science and Information Engineering, Henan University, Kaifeng, 475001, China.
  • Huimin Luo
  • Junwei Luo
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Fei Guo
    School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China. Electronic address: gfjy001@yahoo.com.

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