4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction

He, Wenying; Jia, Cangzhi; Zou, Quan

doi:10.1093/bioinformatics/bty668

Cited by 151 publications

(84 citation statements)

References 55 publications

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“…For the ROC curve, 1-specificity was plotted on the horizontal axis, and sensitivity on the vertical axis. LOO, K-Fold cross-validation, and independent testing are the most widely used methods for predictor evaluation (Mrozek et al, 2015;Cao and Cheng, 2016;Chen et al, 2017Chen et al, , 2018aChen et al, , 2019bPan et al, 2017;He et al, 2018He et al, , 2019Jiang et al, 2018;Xiong et al, 2018;Yu et al, 2018;Zhang et al, 2018;Ding et al, 2019;Feng et al, 2019;Kong and Zhang, 2019;Li and Liu, 2019;Lv et al, 2019a;Manavalan et al, 2019;Shan et al, 2019;Wang et al, 2019a;Wei et al, 2019a,b;Xu et al, 2019;Yu and Dai, 2019). That is the general machine learning evaluation methods (training, validation and testing) are used for optimized model evaluation.…”

Section: Model Evaluation Metrics and Methodsmentioning

confidence: 99%

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

Zhang

Ding

et al. 2020

Front. Bioeng. Biotechnol.

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One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial for various cellular biological and physiological processes. To gain more insight into the functional mechanisms involved, it is of fundamental importance to precisely identify pseudouridine sites in RNA. Several useful machine learning approaches have become available recently, with the increasing progress of next-generation sequencing technology; however, existing methods cannot predict sites with high accuracy. Thus, a more accurate predictor is required. In this study, a random forest-based predictor named RF-PseU is proposed for prediction of pseudouridylation sites. To optimize feature representation and obtain a better model, the light gradient boosting machine algorithm and incremental feature selection strategy were used to select the optimum feature space vector for training the random forest model RF-PseU. Compared with previous state-of-the-art predictors, the results on the same benchmark data sets of three species demonstrate that RF-PseU performs better overall. The integrated average leave-one-out cross-validation and independent testing accuracy scores were 71.4% and 74.7%, respectively, representing increments of 3.63% and 4.77% versus the best existing predictor. Moreover, the final RF-PseU model for prediction was built on leave-one-out cross-validation and provides a reliable and robust tool for identifying pseudouridine sites. A web server with a user-friendly interface is accessible at http://148.70.81.170:10228/rfpseu.

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Section: Model Evaluation Metrics and Methodsmentioning

confidence: 99%

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

Zhang

Ding

et al. 2020

Front. Bioeng. Biotechnol.

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“…The most widespread DNA methylation modifications are N6-methyladenine (6mA), 5methylcytosine (5mC) and N4-methylcytosine (4mC) that have been detected in both prokaryotic and eukaryotic genomes (Fu et al, 2015;Blow et al, 2016;Chen et al, 2017). These modifications are catalyzed by specific DNA methyltransferases (DNMTs) that transfer a methyl group to specific exocyclic amino groups (He et al, 2018). In eukaryotes, 5mC is the most common DNA modification, which is essential for gene regulation, transposon suppression and gene imprinting (Suzuki and Bird, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…However, the low predictive power is the main drawback of iDNA4mC. 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.…”

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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“…On the basis of the latest experimental results obtained from the large-scale profiling which included the whole exome and RNA sequencing, it can be learnt that genetic and epigenetic mechanisms are involved in the occurrence and progress of glioma cells (Cancer Genome Atlas Research, 2008;Brennan et al, 2013), especially the aberrant epigenetic silencing of genes caused by histone deacetylation (Vaissiere et al, 2008;Cartron et al, 2013). A large number of researches have proven that significant nuclear expression of histone deacetylase 1 (HDAC1) occurred in GBM cells during the process of tumor progression, recurrence, and metastasis (Bhat et al, 2008;Kim et al, 2008;Campos et al, 2011;Li et al, 2016Li et al, , 2018aZhang et al, 2016;Staberg et al, 2017;He et al, 2019;Natsume et al, 2019). In addition, the invasive and proliferative phenotype of GBM cells was found to be related to the overexpression of HDAC1 level (Han et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Selective Inhibition of HDAC1 by Macrocyclic Polypeptide for the Treatment of Glioblastoma: A Binding Mechanistic Analysis Based on Molecular Dynamics

Wang

Ren

et al. 2020

Front. Mol. Biosci.

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Glioblastoma (GBM) is the most common and aggressive intracranial malignant brain tumor, and the abnormal expression of HDAC1 is closely correlated to the progression, recurrence and metastasis of GBM cells, making selective inhibition of HDAC1 a promising strategy for GBM treatments. Among all available selective HDAC1 inhibitors, the macrocyclic peptides have gained great attention due to their remarkable inhibitory selectivity on HDAC1. However, the binding mechanism underlying this selectivity is still elusive, which increases the difficulty of designing and synthesizing the macrocyclic peptide-based anti-GBM drug. Herein, multiple computational approaches were employed to explore the binding behaviors of a typical macrocyclic peptide FK228 in both HDAC1 and HDAC6. Starting from the docking conformations of FK228 in the binding pockets of HDAC1&6, relatively long MD simulation (500 ns) shown that the hydrophobic interaction and hydrogen bonding of E91 and D92 in the Loop2 of HDAC1 with the Cap had a certain traction effect on FK228, and the sub-pocket formed by Loop1 and Loop2 in HDAC1 could better accommodate the Cap group, which had a positive effect on maintaining the active conformation of FK228. While the weakening of the interactions between FK228 and the residues in the Loop2 of HDAC6 during the MD simulation led to the large deflection of FK228 in the binding site, which also resulted in the decrease in the interactions between the Linker region of FK228 and the previously identified key amino acids (H134, F143, H174, and F203). Therefore, the residues located in Loop1 and Loop2 contributed in maintaining the active conformation of FK228, which would provide valuable hints for the discovery and design of novel macrocyclic polypeptide HDAC inhibitors.

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4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction

Abstract: The web-server 4mCPred, is accessible at http://server.malab.cn/4mCPred/index.jsp.

Cited by 151 publications

References 55 publications

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

Selective Inhibition of HDAC1 by Macrocyclic Polypeptide for the Treatment of Glioblastoma: A Binding Mechanistic Analysis Based on Molecular Dynamics

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