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Extremely-randomized-tree-based Prediction of N(6)-Methyladenosine Sites in Saccharomyces cerevisiae

Authors
Govindaraj, RG | Subramaniyam, S | Manavalan, B
Citation
Current genomics, 21(1). : 26-33, 2020
Journal Title
Current genomics
ISSN
1389-20291875-5488
Abstract
INTRODUCTION: N(6)-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

DOI
10.2174/1389202921666200219125625
PMID
32655295
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Physiology
Ajou Authors
Balachandran, Manavalan
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