DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: The importance of chromatin loops in gene regulation is broadly accepted. There are mainly two approaches to predict chromatin loops: transcription factor (TF) binding-dependent approach and genomic variation-based approach. However, neither of these approaches provides an adequate understanding of gene regulation in human tissues. To address this issue, we developed a deep learning-based chromatin loop prediction model called Deep Learning-based Universal Chromatin Interaction Annotator (DeepLUCIA).

Authors

  • Dongchan Yang
    Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea.
  • Taesu Chung
    Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Dongsup Kim
    Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea. kds@kaist.ac.kr.