Transcription factors (TFs) recognize and bind to specific DNA sequences, thereby altering the chromatin structure and regulating transcription. TFs aid in the formation of a guide genome that facilitates the expression of genes under complex regulation. Understanding the underlying mechanism that mediates the TF-led regulation of gene expression is a popular topic in current genomic research. However, identifying the precise TF binding site (TFBS) and the specific role of the TFs in transcriptional regulation is challenging. This article summarizes the status of research concerning the prediction of TFBS. First, the experimental methods for identifying TFBS have been summarized by accessing related databases. Second, the machine learning methods for predicting TFBS, especially deep learning, have been summarized. Finally, the study elaborates on the main challenges faced in TFBS prediction. The purpose of this article is to provide researchers with a comprehensively understand the prediction of TFBS and to promote further development in this field.

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A Review About Transcription Factor Binding Sites Prediction Based on Deep Learning

The binding of transcription factors (TFs) to transcription factor binding sites (TFBS) plays a pivotal role in regulating gene expression and evolution Accurately modeling the specificity of DNA and searching for TFBS helps understand the genome’s function and evolution In recent years, computational identification of TFBS has become an active field of research Here, we propose DeepARC, an attention-based hybrid approach combining convolutional neural network (CNN) and recurrent neural network (RNN) for predicting TFBS We employ a position-based embedding strategy to embed a DNA sequence into a matrix with distributed representation contenting the position information and then feed the distributed representations of the sequence into a CNN-BiLSTM-Attention-based framework to classify whether there is a TFBS in a sequence Take the advantage of the attention mechanism, DeepARC can obtain more valuable information about TFBS and add interpretability to the TFBS search process Moreover, sufficient experiments prove that DeepARC has better performance than existing predictors The DeepARC web server is available at http://deeparcdenglaborg

DeepARC: An Attention-based Hybrid Model for Predicting Transcription Factor Binding Sites from Positional Embedded DNA Sequence

Transcription factors are important cellular components of the process of gene expression control. Transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators to fine-tune spatiotemporal gene regulation. As the common proteins, transcription factors play a meaningful role in life-related activities. In the face of the increase in the protein sequence, it is urgent how to predict the structure and function of the protein effectively. At present, protein-DNA-binding site prediction methods are based on traditional machine learning algorithms and deep learning algorithms. In the early stage, we usually used the development method based on traditional machine learning algorithm to predict protein-DNA-binding sites. In recent years, methods based on deep learning to predict protein-DNA-binding sites from sequence data have achieved remarkable success. Various statistical and machine learning methods used to predict the function of DNA-binding proteins have been proposed and continuously improved. Existing deep learning methods for predicting protein-DNA-binding sites can be roughly divided into three categories: convolutional neural network (CNN), recursive neural network (RNN) and hybrid neural network based on CNN-RNN. The purpose of this review is to provide an overview of the computational and experimental methods applied in the field of protein-DNA-binding site prediction today. This paper introduces the methods of traditional machine learning and deep learning in protein-DNA-binding site prediction from the aspects of data processing characteristics of existing learning frameworks and differences between basic learning model frameworks. Our existing methods are relatively simple compared with natural language processing, computational vision, computer graphics and other fields. Therefore, the summary of existing protein-DNA-binding site prediction methods will help researchers better understand this field.

A survey on protein-DNA-binding sites in computational biology.

DeepnsSNPs: Accurate prediction of non-synonymous single-nucleotide polymorphisms by combining multi-scale convolutional neural network and residue environment information

The transforming growth factor beta-1 (TGF β-1) cytokine exerts both pro-tumor and anti-tumor effects in carcinogenesis. An increasing body of literature suggests that TGF β-1 signaling outcome is partially dependent on the regulatory targets of downstream receptor-regulated Smad (R-Smad) proteins Smad2 and Smad3. However, the lack of Smad-specific antibodies for ChIP-seq hinders convenient identification of Smad-specific binding sites.In this study, we use localization and affinity purification (LAP) tags to identify Smad-specific binding sites in a cancer cell line. Using ChIP-seq data obtained from LAP-tagged Smad proteins, we develop a convolutional neural network with long-short term memory (CNN-LSTM) as a deep learning approach to classify a pool of Smad-bound sites as being Smad2- or Smad3-bound. Our data showed that this approach is able to accurately classify Smad2- versus Smad3-bound sites. We use our model to dissect the role of each R-Smad in the progression of breast cancer using a previously published dataset.Our results suggests that deep learning approaches can be used to dissect binding site specificity of closely related transcription factors. 

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Deep learning for de-convolution of Smad2 versus Smad3 binding sites

Transcription factor binding site (TFBS), one of the DNA-protein binding sites, plays important roles in understanding regulation of gene expression and drug design. Recently, deep-learning based methods have been widely used in the prediction of TFBS. In this work, we propose a novel deep-learning model, called Combination of Multi-Scale Convolutional Network and Long Short-Term Memory Network (MCNN-LSTM), which utilizes multi-scale convolution for feature processing, and the long short-term memory network to recognize TFBS in DNA sequences. Moreover, we design a new encoding method, called multi-nucleotide one-hot (MNOH), which considers the correlation between nucleotides in adjacent positions, to further improve the prediction performance of TFBS. Stringent cross-validation and independent tests on benchmark datasets demonstrated the efficacy of MNOH and MCNN-LSTM. Based on the proposed methods, we further implement a new TFBS predictor, called DeepTF. The computational experimental results show that our predictor outperformed several existing TFBS predictors.

Xiao-Rong Bao

Papers

DeepTF: Accurate Prediction of Transcription Factor Binding Sites by Combining Multi-scale Convolution and Long Short-Term Memory Neural Network