Detection of m6A RNA Methylation in Nanopore Sequencing Data Using Support Vector Machine

N6-methyladenosine (m6A) is a prevalent internal modification in RNA which plays an important role in epitranscriptomics. The detection of m6A may be carried out by utilizing the Oxford Nanopore Technology (ONT) and machine learning. In this research, following a previous study by Liu et al, we hypothesize that the current intensitychange of the modification of the RNA(N6-methyladenosine) is the result of base-calling errors(mismatch frequency, deletion frequency, per-base quality and current intensity). We apply the Curlake, EpiNano software to divide the raw data into 5-mer sequences and extract features from the RNA sequence. The SVM classifier is used to verify this assumption. Our results confirmed the finding of a previous study by Liu et al, suggesting that the base-calling ‘errors'may be usedto identify the N6-methyladenesine(m⁶A), and the consideration of the neighbourhood nucleotides of the 5mer will improve the accuracy of our prediction.