Citation: Huang, G.; Luo, W.; Zhang, G.; Zheng, P.; Yao, Y.; Lyu, J.; Liu, Y.; Wei, D.-Q. Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition. Biomolecules 2022, 12, 995. https://doi.org/10.3390/ biom12070995 Academic Editors: Xin Lai and Le Zhang Received: 31 May 2022 Accepted: 7 July 2022 Published: 17 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). biomolecules Article Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition Guohua Huang 1, *, Wei Luo 1 , Guiyang Zhang 1 , Peijie Zheng 1 , Yuhua Yao 2 , Jianyi Lyu 1 , Yuewu Liu 3 and Dong-Qing Wei 4 1 School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; qq25822@163.com (W.L.); guiyang9542@163.com (G.Z.); zhengpeijie1997@163.com (P.Z.); ljy990309@163.com (J.L.) 2 School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China; yaoyuhua2288@163.com 3 College of Information and Intelligence, Hunan Agricultural University, Changsha 410083, China; yuewuliu@whu.edu.cn 4 State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; dqwei@sjtu.edu.cn * Correspondence: 3280@hnsyu.edu.cn Abstract: Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers. Keywords: enhancer; promoter; deep learning; feed-forward attention; convolution neural network; long-short term memory; residual neural network 1. Introduction Enhancers are short pieces of DNA sequences of 50 to 1500 bp, which can accelerate the transcription of target genes by binding the transcription factors [1,2]. Unlike the promoters, enhancers are located either in the upstream/downstream or within the genes they regulate and doesn’t have to be close to the starting sites of transcription [2–4]. Increasing evidences indicate that enhancers play a critical role in the gene regulation [4,5]. The enhancers control the expression of genes involved in cell differentiation [6,7] and are responsible for morphological changes in three spine stickleback fish [8]. The enhancers orchestrate critical cellular events such as differentiation [9,10], maintenance of cell identity [11,12], and response to stimuli [13–15] by binding to transcription factors [16]. The enhancers are closely related to inflammation and cancer [17]. Therefore, precisely detecting en- hancers from DNA sequences is critical to further investigate their functions or roles in the cellular processes. The methods or techniques used to identify enhancers are divided into two categories: high-throughput experimental technology and computational method [5,18]. The former includes chromatin immunoprecipitation followed by deep sequencing (ChIP–seq) [19,20], protein-binding microarrays (PBMs) [21], systematic evolution of ligands by exponential enrichment (SELEX) [22], yeast-one-hybrid (Y1H) [23], and bacterial-one-hybrid [24]. The Biomolecules 2022, 12, 995. https://doi.org/10.3390/biom12070995 https://www.mdpi.com/journal/biomolecules