Journal Article Ppublish
PubMed: 40524426 DOI: 10.1093/bib/bbaf282
2OM-Pred: prediction of 2-O-methylation sites in ribonucleic acid using diverse classifiers.
Brief Bioinform 26(3)
Authors: Anas Bilal, Muhammad Taseer Suleman, Khalid Almohammadi, Abdulkareem Alzahrani, Xiaowen Liu
Affiliations
- Department of Information Science and Technology, Hainan Normal University, No. 99 Long Kun South Road, Haikou 571158, China.
- Department of Computer Science, Bahria University Lahore Campus, 47-Civic Centre, Johar Town, Lahore, Punjab 54782, Pakistan.
- Computer Science Department, Applied College, University of Tabuk, King Faisal Road, Tabuk City 71491, Tabuk Region, Saudi Arabia.
- Computer Science Department, Faculty of Computing and Information, Al-Baha University, King Fahad Road, Alaqiq 65779, Al-Baha Region, Saudi Arabia.
Abstract
2-O-methylation (2OM) is a vital post-transcriptional modification which is formed by a functional group through the attachment of a methyl (-CH3) group to the second position of an aromatic ring hydroxyl group (-OH). It plays an active part in RNA physical configuration stability and the way different RNA molecules interrelate. Further, this modification plays a pivotal role in changing the epigenetic regulation of cellular processes. Previous approaches like mass spectrometry could not fully enhance the identification of RNA-modified sites. Sequence data were useful in the development of measures that meant the use of computationally intelligent system to identify 2OM sites quickly. This research proposed a new novel method of feature extraction and generation from the available sequences, and the feature dimensionality reduction has been done through the incorporation of statistical moments. The final feature vectors were developed and used to train prediction models. The assessment of prediction models was carried out through independent set tests and k-fold cross-validation. Through rigorous testing, the bagging ensemble model outperformed and revealed optimal accuracy scores. A publicly accessible web-based application has been developed which can be accessed via https://2om-pred-webapp.streamlit.app/.
MeSH
Genomics, Rna