Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network

Abstract

Enhancers are noncoding fragments in DNA sequences, which play an important role in gene transcription and translation. However, due to their high free scattering and positional variability, the identification and classification of enhancers have a higher level of complexity than those of coding genes. In order to solve this problem, many computer studies have been carried out in this field, but there are still some deficiencies in these prediction models. In this paper, we use various feature extraction strategies, dimension reduction technology, and a comprehensive application of machine model and recurrent neural network model to achieve an accurate prediction of enhancer identification and classification with the accuracy of was 76.7% and 84.9%, respectively. The model proposed in this paper is superior to the previous methods in performance index or feature dimension, which provides inspiration for the prediction of enhancers by computer technology in the future.

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. In the supplementary material we have included the main algorithms that have been used to identify the enhancers provided in each repository.…”

Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm

“…Therefore, there are only 10 kinds of 2-mer in this method: "AA," "CC," "AC," "CA," "GA," "AG," "CG," "GC," "AT," and "TA." The frequency of each k-mer is calculated in turn [37].…”

Enhancers as potential targets for engineering salinity stress tolerance in crop plants

“…For example, a supervised hidden Markov model (HMM) based method, enhancer HMM (eHMM), have been developed that can distinguish between enhancers and promoters too, despite a substantial overlap between their features (Zehnder et al 2019). In addition, methods for identification and classification of enhancers via dimension reduction technology and/or recurrent/ convolutional neural networks have been proposed (Li et al 2020a(Li et al , 2020b(Li et al , 2020cNguyen et al 2019). Further, a probabilistic enhancer prediction tool (PREPRINT) based on the characteristic coverage patterns of chromatin features at enhancers has also been developed (Osmala and Lähdesmäki 2020).…”

“…Several genetic and epigenetic variations in the noncoding regulatory sequences have been associated with phenotypic plasticity and domestication in crop plants. Although CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR-associated protein) technology has been used most commonly to target coding sequences, few studies have been conducted to engineer cisregulatory elements too to modulate gene expression programs (Kumar & Jain 2015;Duan et al 2016;Shi et al 2017;Li et al 2020aLi et al , 2020bLi et al , 2020c. Further, engineering of cis-regulatory elements was found to produce less detrimental pleiotropic effects.…”

“…Likewise, insertion of a 35S promoter upstream of ANT1 enhanced anthocyanin accumulation in tomato (Čerm ak et al 2015). The deletion of a regulatory fragment in the Xa13 promoter resulted in transgene-free rice with enhanced disease resistance without affecting fertility, which proved better than the generation of a knockout mutant that exhibited sterility (Li et al 2020a(Li et al , 2020b(Li et al , 2020c. Xu et al 2019).…”