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Updated: Dec 15, 2025
DNAzyme-dependent Analysis of rRNA 2’-O-Methylation
Published on: September 16, 2019
Extremely-randomized-tree-based Prediction of N6-Methyladenosine Sites in Saccharomyces cerevisiae
Rajiv G Govindaraj1, Sathiyamoorthy Subramaniyam1, Balachandran Manavalan1
11HotSpot Therapeutics, 50 Milk Street, 16 Floor, Boston, MA02109, USA; 2Research and Development Center, In-silicogen Inc., Yongin-si 16954, Gyeonggi-do, Republic of Korea; 3Department of Biotechnology, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu641048, India; 4Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.
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.
