CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

Journal: Bioinformatics (Oxford, England)
PMID: 35997565

Abstract

MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although various experimental techniques have been developed to detect chromatin loops, they have been found to be time-consuming and costly. Nowadays, various sequence-based computational methods can capture significant features of 3D genome organization and help predict chromatin loops. However, these methods have low performance and poor generalization ability in predicting chromatin loops.

Authors

  • Pengyu Zhang
    School of Software, Shandong University, Jinan, Shandong 250101, China.
  • Yingfu Wu
    College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
  • Haoru Zhou
    College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
  • Bing Zhou
    Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450001, China.
  • Hongming Zhang
    College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.

Keywords

External Resources

Popular Topics
Recent Journals