M5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques

Abstract

ABSTRACT5‐Methylcytosine (m5C) is a widely recognized epigenetic modification in ribonucleic acid (RNA), catalyzed by methyltransferases. This modification is crucial for various biological functions. While the role of m5C in deoxyribonucleic acid (DNA) has been extensively studied, its role in RNA is still in its early stages of exploration. Accurate and systematic detection and classification of m5C sites in RNA remain challenging tasks. Machine learning techniques offer an efficient alternative to traditional laboratory methods for identifying m5C sites in Homo sapiens. This study introduces a novel computational model m5C‐TNKmer, which utilizes k‐mer feature extraction to enhance the identification of m5C sites in RNA sequences. Four sub‐datasets derived from the primary dataset Di‐nucleotide (DNC), Tri‐nucleotide (TNC), Tetra‐nucleotide (Tetra‐NC), and Penta‐nucleotide (Penta‐NC) were used to train the model. The results demonstrated that m5C‐TNKmer achieved an impressive accuracy of 96.15%. This model provides a powerful tool for scientists to accurately identify RNA m5C sites, contributing to a deeper understanding of genetic functions and regulatory mechanisms.