iEnhancer-GAN: A Deep Learning Framework in Combination with Word Embedding and Sequence Generative Adversarial Net to Identify Enhancers and Their Strength

Abstract

As critical components of DNA, enhancers can efficiently and specifically manipulate the spatial and temporal regulation of gene transcription. Malfunction or dysregulation of enhancers is implicated in a slew of human pathology. Therefore, identifying enhancers and their strength may provide insights into the molecular mechanisms of gene transcription and facilitate the discovery of candidate drug targets. In this paper, a new enhancer and its strength predictor, iEnhancer-GAN, is proposed based on a deep learning framework in combination with the word embedding and sequence generative adversarial net (Seq-GAN). Considering the relatively small training dataset, the Seq-GAN is designed to generate artificial sequences. Given that each functional element in DNA sequences is analogous to a “word” in linguistics, the word segmentation methods are proposed to divide DNA sequences into “words”, and the skip-gram model is employed to transform the “words” into digital vectors. In view of the powerful ability to extract high-level abstraction features, a convolutional neural network (CNN) architecture is constructed to perform the identification tasks, and the word vectors of DNA sequences are vertically concatenated to form the embedding matrices as the input of the CNN. Experimental results demonstrate the effectiveness of the Seq-GAN to expand the training dataset, the possibility of applying word segmentation methods to extract “words” from DNA sequences, the feasibility of implementing the skip-gram model to encode DNA sequences, and the powerful prediction ability of the CNN. Compared with other state-of-the-art methods on the training dataset and independent test dataset, the proposed method achieves a significantly improved overall performance. It is anticipated that the proposed method has a certain promotion effect on enhancer related fields.

Analysis of the landscape of human enhancer sequences in biological databases

“…On the other hand, novel software to identify enhancer sequences is being developed [173] , [174] . Comparative studies of algorithms and revisions about these tools have been previously elaborated in other works [130] , [175] , [176] , although a more recent in-depth review regarding this issue would be of interest.…”

Opportunity to improve livestock traits using 3D genomics

“…Similarly, there are increasing numbers of machine learning tools that are able to predict the role of a sequence, (e.g. whether a sequence is an enhancer) from DNA alone or a combination of DNA and epigenomic assays like ChIP-seq (Ghandi et al 2014;Min et al 2017;Chen et al 2018;Le et al 2019;Nguyen et al 2019;Oubounyt et al 2019;Ma et al 2020;Shujaat et al 2020;Sun et al 2020;Yang et al 2021). For example, iEnhancer-ECNN is able to predict whether a 200-bp sequence is a strong enhancer, weak enhancer or non-enhancer just from DNA sequence (Nguyen et al 2019).…”

“…2020; Yang et␣al . 2021). For example, iEnhancer‐ECNN is able to predict whether a 200‐bp sequence is a strong enhancer, weak enhancer or non‐enhancer just from DNA sequence (Nguyen et␣al .…”

Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition

“…Furthermore, Machine learning-based methods are capable of discovering non-linear hidden motifs of enhancers. To date, there are at least twenty machine learning-based methods for enhancer prediction [ 16 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ], such as iEnhancer-2L [ 40 ], iEnhancer-PsedeKNC [ 41 ], EnhancerPred [ 42 ], and EnhancerPred2.0 [ 43 ]. The general workflow of these methods is firstly to compute representation of sequences such as pseudo k-tuple nucleotide composition, nucleotide binary profiles, as well as accumulated nucleotide frequency, then to learn a classifier by using a machine learning algorithm such as support vector machine and random forest, and finally to predict unknown sequences.…”