3 10

Cited 0 times in

SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome

Authors
Basith, S; Manavalan, B; Shin, TH; Lee, G
Citation
Molecular therapy. Nucleic acids, 18. : 131-141, 2019
Journal Title
Molecular therapy. Nucleic acids
ISSN
2162-2531
Abstract
DNA N(6)-adenine methylation (6mA) is an epigenetic modification in prokaryotes and eukaryotes. Identifying 6mA sites in rice genome is important in rice epigenetics and breeding, but non-random distribution and biological functions of these sites remain unclear. Several machine-learning tools can identify 6mA sites but show limited prediction accuracy, which limits their usability in epigenetic research. Here, we developed a novel computational predictor, called the Sequence-based DNA N(6)-methyladenine predictor (SDM6A), which is a two-layer ensemble approach for identifying 6mA sites in the rice genome. Unlike existing methods, which are based on single models with basic features, SDM6A explores various features, and five encoding methods were identified as appropriate for this problem. Subsequently, an optimal feature set was identified from encodings, and corresponding models were developed individually using support vector machine and extremely randomized tree. First, all five single models were integrated via ensemble approach to define the class for each classifier. Second, two classifiers were integrated to generate a final prediction. SDM6A achieved robust performance on cross-validation and independent evaluation, with average accuracy and Matthews correlation coefficient (MCC) of 88.2% and 0.764, respectively. Corresponding metrics were 4.7%-11.0% and 2.3%-5.5% higher than those of existing methods, respectively. A user-friendly, publicly accessible web server (http://thegleelab.org/SDM6A) was implemented to predict novel putative 6mA sites in rice genome.
Keywords
DNA N(6)-adenine methylationextremely randomized treemachine learningrice genomesupport vector machine
DOI
10.1016/j.omtn.2019.08.011
PMID
31542696
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Physiology
AJOU Authors
Basith, ShaherinBalachandran, Manavalan신, 태환이, 광
Full Text Link
Files in This Item:
31542696.pdfDownload
Export
RIS (EndNote)
XLS (Excel)
XML

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse