Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species

Wei, Leyi; Luan, Shasha; Nagai, Luís Augusto Eijy; Su, Ran; Zou, Quan

doi:10.1093/bioinformatics/bty824

Cited by 158 publications

(115 citation statements)

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“…In the above context, computational tools were developed for detecting different modification sites, including protein methylation (Wei et al 2018d), protein phosphorylation ) and dephosphorylation (Jia et al 2017), protein O-GlcNAcylation , histone crotonylation (Qiu et al 2017), DNA N 4 -methylcytosine (Chen et al 2017c;Wei et al 2018b), RNA pseudouridine (Chen et al 2016b), and various RNA adenosine modifications (Chen et al 2018). However, it has been proven that sequence alignment (e.g., PSI-BLAST) cannot accurately identify the modification sites.…”

Section: Introductionmentioning

confidence: 99%

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Zou

Xing

Wei

et al. 2018

RNA

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438

214

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N 6 -Methyladenosine (m 6 A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N 6 -methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m 6 A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m 6 A sequences using deep learning techniques. To the best of our knowledge, our work is the first attempt to use word embedding and deep neural networks for m 6 A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m 6 A accurately and robustly. Four prediction schemes were built with various RNA sequence representations and optimized convolutional neural networks. The soft voting results from the four deep networks were shown to outperform all of the stateof-the-art methods. We evaluated these predictors mentioned above on a rigorous independent test data set and proved that our proposed method outperforms the state-of-the-art predictors. The training, independent, and cross-species testing data sets are much larger than in previous studies, which could help to avoid the problem of overfitting. Furthermore, an online prediction web server implementing the four proposed predictors has been built and is available at http://server. malab.cn/Gene2vec/.

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Section: Introductionmentioning

confidence: 99%

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Zou

Xing

Wei

et al. 2018

RNA

Self Cite

438

214

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“…While 6mA and 4mC are very small, they can only be detected in eukaryotes by high sensitivity techniques. In prokaryotes, 6mA and 4mC are the majority, mainly used to distinguish host DNA from exogenous pathogenic DNA (Heyn and Esteller, 2015), and 4mc controls DNA replication and corrects DNA replication errors (Cheng et al, 1995;Wei et al, 2018). Moreover, 4mC as part of a restriction-modification (R-M) system prevents restriction enzymes from degrading host DNA (Schweizer et al, 2008;Wei et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The second 4mC site predictor, called 4mCPred (He et al, 2018), proposes a new feature coding algorithm by combining positionspecific trinucleotide propensity and electron-ion interaction pseudopotentials, which improves the accuracy of prediction. The third 4mC site predictor, called 4mcPred-SVM (Wei et al, 2018), proposes more useful sequence features in the predictor and improves the feature representation capability through a two-step feature selection method. However, the performance of the experiment did not improve much.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

2020

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Motivation: N4-methylcytosine (4mC) plays an important role in host defense and transcriptional regulation. Accurate identification of 4mc sites provides a more comprehensive understanding of its biological effects. At present, the traditional machine learning algorithms are used in the research on 4mC sites prediction, but the complexity of the algorithms is relatively high, which is not suitable for the processing of large data sets, and the accuracy of prediction needs to be improved. Therefore, it is necessary to develop a new and effective method to accurately identify 4mC sites.Results: In this work, we found a large number of 4mC sites and non 4mC sites of Caenorhabditis elegans (C. elegans) from the latest MethSMRT website, which greatly expanded the dataset of C. elegans, and developed a hybrid deep neural network framework named 4mcDeep-CBI, aiming to identify 4mC sites. In order to obtain the high latitude information of the feature, we input the preliminary extracted features into the Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory network (BLSTM) to generate advanced features. Taking the advanced features as algorithm input, we have proposed an integrated algorithm to improve feature representation. Experimental results on large new dataset show that the proposed predictor is able to achieve generally better performance in identifying 4mC sites as compared to the state-of-art predictor. Notably, this is the first study of identifying 4mC sites using deep neural network. Moreover, our model runs much faster than the state-of-art predictor.

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“…Although the prediction of 4mC sites by this sequencing technique has been improved to some extent, recent studies focus more on the recognition of 4mC sites using machine learning, which is capable of predicting 4mC sites based on genome sequences, without any prior experimental knowledge. There are currently four methods available in literature to identify 4mC sites, including iDNA4mC (Chen et al, 2017), 4mCPred (Su et al, 2018), 4mcPred-SVM (Wei et al, 2018a), and 4mcPred-IFL (Wei et al, 2019a). iDNA4mC, as the first machine learning predictor, encodes sequences by nucleotide chemical properties and nucleotide frequency to features and trains support vector machine (SVM) models for prediction (Liang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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

Zeng

Liao

2020

Front. Bioeng. Biotechnol.

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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.

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Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 158 publications

References 43 publications

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

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

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Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 158 publications

References 43 publications

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

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

Contact Info

Product

Resources

About

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA

Gene2vec: gene subsequence embedding for prediction of mammalian N⁶-methyladenosine sites from mRNA