Paper Info
Title
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.
Year
DOI
Venue
2020
10.1093/bib/bbz048
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
m5C site,feature description,computational method,webserver,iRNA-m5C
Journal
21
Issue
ISSN
Citations 
3
1467-5463
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Hao Lv1114.65
Zi-Mei Zhang210.69
Shi-Hao Li301.01
Jiu-Xin Tan400.34
Wei Chen5578.63
Hao Lin6347.08