Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

Abstract

DNA N4-methylcytosine modification (4mC) plays an essential role in a variety of biological processes. Therefore, accurate identification the 4mC distribution in genome-scale is important for systematically understanding its biological functions. In this study, we present Deep4mcPred, a multi-layer deep learning based predictive model to identify DNA N4-methylcytosine modifications. In this predictor, we for the first time integrate residual network and recurrent neural network to build a multi-layer deep learning predictive system. As compared to existing predictors using traditional machine learning, our proposed method has two advantages. First, our deep learning framework does not need to specify the features when training the predictive model. It can automatically learn the high-level features and capture the characteristic specificity of 4mC sites, benefiting to distinguish true 4mC sites from non-4mC sites. On the other hand, our deep learning method outperforms the traditional machine learning predictors in performance by benchmarking comparison, demonstrating that the proposed Deep4mcPred is more effective in the DNA 4mC site prediction. Moreover, via experimental comparison, we found that attention mechanism introduced into the deep learning framework is useful to capture the critical features. Additionally, we develop a webserver implementing the proposed method for the academic use of research community, which is now available at http://server.malab.cn/Deep4mcPred.

DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species

“…The bold values in the table shows high performance achieved for the particular database. Species Dataset Model Sensitivity Specificity Accuracy MCC AUC Caenorhabditis elegans ( C. elegans ) Liu et al [24] DeepTorrent 0.930 0.910 0.920 0.840 0.976 DCNN-4mC 0.971 0.968 0.969 0.938 0.992 Zeng et al [27] 4mcDeep-CBI 0.949 0.894 0.930 0.850 0.924 DCNN-4mC 0.970 0.942 0.959 0.913 0.986 Rao et al [26] Deep4mCPred 0.915 0.872 0.893 0.787 – DCNN-4mC 0.955 0.951 0.953 0.906 0.982 Drosophila melanogaster ( D. melanogaster ) Liu et al …”

“…Some other deep learning based tools like 4mCCNN [25] were also proposed which provide improvement in performance for identification of 4mC site in these species. Further Rao et al contributed an additional datatset for C.elegans , D.melanogaster and A.thaliana [26] . Subsequently Zeng et al collected an additional dataset for C.elegans [27] .…”

4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism

“…To demonstrate the effectiveness of the proposed method, we compared its performance with four other existing single-task state-of-the-art methods on the benchmark dataset, including 4mcPred-IFL ( Wei et al, 2019b ), 4mcPred_SVM ( Wei et al, 2019a ), and Deep4mcPred ( Zeng and Liao, 2020 ). It is worth noting that among the three competing methods, except the method Deep4mcPred using deep learning technique, other methods all use traditional machine learning to train the respective models by hand-made features extracted from original DNA sequences.…”

“…Previous studies have demonstrated that a stringent dataset is essential for building a robust predictive model ( Liang et al, 2017 ; Zeng and Liao, 2020 ; Su et al, 2021 ). In our previous work ( Zeng and Liao, 2020 ), we constructed large-scale datasets for three species, including Arabidopsis thaliana ( A. thaliana ), Caenorhabditis elegans ( C. elegans ), and Drosophila melanogaster ( D. melanogaster ). As for the positive samples, there are 20,000 positive samples, and each sample is a 41-bp-long sequence centered with true 4mC sites.…”

“…The average area under the ROC (receiver operating characteristic) curve (AUC) values of its prediction for multiple species were greater than 0.9 in multiple cross-validations. In our previous work, we proposed a two-layer deep learning model called Deep4mcPred, which utilizes a hybrid network of ResNet and long short-term memory (LSTM) ( Zeng and Liao, 2020 ).…”