CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network

Lantian Yao, Peilin Xie, Jiahui Guan, Chia Ru Chung, Yixian Huang, Yuxuan Pang, Huacong Wu, Ying Chih Chiang*, Tzong Yi Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Enhancers are a class of noncoding DNA, serving as crucial regulatory elements in governing gene expression by binding to transcription factors. The identification of enhancers holds paramount importance in the field of biology. However, traditional experimental methods for enhancer identification demand substantial human and material resources. Consequently, there is a growing interest in employing computational methods for enhancer prediction. In this study, we propose a two-stage framework based on deep learning, termed CapsEnhancer, for the identification of enhancers and their strengths. CapsEnhancer utilizes chaos game representation to encode DNA sequences into unique images and employs a capsule network to extract local and global features from sequence “images”. Experimental results demonstrate that CapsEnhancer achieves state-of-the-art performance in both stages. In the first and second stages, the accuracy surpasses the previous best methods by 8 and 3.5%, reaching accuracies of 94.5 and 95%, respectively. Notably, this study represents the pioneering application of computer vision methods to enhancer identification tasks. Our work not only contributes novel insights to enhancer identification but also provides a fresh perspective for other biological sequence analysis tasks.

Original languageEnglish
Pages (from-to)5725-5736
Number of pages12
JournalJournal of Chemical Information and Modeling
Volume64
Issue number14
DOIs
StatePublished - 22 Jul 2024

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