Evaluation of different computational methods on 5-methylcytosine sites identification

Abstract

5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.

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

“…Moreover, we also used the area under the ROC curve (AUC) is to quantitively measure the predictive performance of the model (Yang et al, 2018;Lv et al, 2019b;Niu et al, 2019). A higher AUC represents a better predictor (Hanley and McNeil, 1982;Liu et al, 2018;Feng et al, 2019;Lai et al, 2019).…”

Characterization of Dysregulated lncRNA-Associated ceRNA Network Reveals Novel lncRNAs With ceRNA Activity as Epigenetic Diagnostic Biomarkers for Osteoporosis Risk

“…To identify potential lncRNA biomarkers with ceRNA activity, a random forest approach and leave-one-out cross-validation (LOOCV) were used to select optimal lncRNAs biomarkers using the R package "randomForest" and out-of-bag (OOB) error, which measure the performance of the model on the training set (Lv et al, 2019;Tan et al, 2019). The OOB error will produce an unbiased estimate for the classification error, while the bagging method will decrease the chance of overfitting (Toth et al, 2019).…”

m5CPred-SVM: A Novel Method for Predicting m5C Sites of RNA

“…In this study, m5C data of three species have been collected from recently published literature. For A. thaliana, same datasets constructed by Lv et al [26] were used for fair comparison. The positive RNA segments which contain m5C site in the center were collected from NCBI Gene Expression Omnibus (GEO) database with the accession number GSE94065 [29].…”

“…The pseudo K-tuple nucleotide composition (PseKNC) has been used to represent an RNA sequence with a discrete model or vector which can keep considerable sequence order information, especially the global or long-range sequence order information [20,26,38,39]. In this study, we used PseDNC (K=2 for PseKNC) to encode the RNA segments.…”

“…Li et al [25] had collected data from GEO database and developed a Web server RNAm5Cfinder based on random forest algorithm, which can be used to predict m5C sites in eight kinds of cells or tissues of mouse and human. More recently, Lv et al [26] developed a server called iRNA-m5C to predict m5C sites of four types of species.…”