Extremely-randomized-tree-based Prediction of N6-methyladenosine Sites in Saccharomyces cerevisiae

Author(s): Rajiv G. Govindaraj, Sathiyamoorthy Subramaniyam, Balachandran Manavalan*.

Journal Name: Current Genomics

Volume 21 , Issue 1 , 2020

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Introduction: N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved.

Methodology: In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set.

Results: Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors.

Conclusion: In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.

Keywords: Extremely randomized tree, feature optimization, N6-methyladenosine sites, cross-validation, RNA sequences, Saccharomyces cerevisiae.

Rights & PermissionsPrintExport Cite as


Article Details

VOLUME: 21
ISSUE: 1
Year: 2020
Page: [26 - 33]
Pages: 8
DOI: 10.2174/1389202921666200219125625
Price: $65

Article Metrics

PDF: 9