Abstract
OBJECTIVE: The m6A modification is the most common ribonucleic acid (RNA) modification, playing a role in prompting the virus's gene mutation and protein structure changes in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Nanopore single-molecule direct RNA sequencing (DRS) provides data support for RNA modification detection, which can preserve the potential m(6)A signature compared to second-generation sequencing. However, due to insufficient DRS data, there is a lack of methods to find m6A RNA modifications in DRS. Our purpose is to identify m(6)A modifications in DRS precisely. METHODS: We present a methodology for identifying m(6)A modifications that incorporated mapping and extracted features from DRS data. To detect m(6)A modifications, we introduce an ensemble method called mixed-weight neural bagging (MWNB), trained with 5-base RNA synthetic DRS containing modified and unmodified m(6)A. RESULTS: Our MWNB model achieved the highest classification accuracy of 97.85% and AUC of 0.9968. Additionally, we applied the MWNB model to the COVID-19 dataset; the experiment results reveal a strong association with biomedical experiments. CONCLUSION: Our strategy enables the prediction of m(6)A modifications using DRS data and completes the identification of m(6)A modifications on the SARS-CoV-2. SIGNIFICANCE: The Corona Virus Disease 2019 (COVID-19) outbreak has significantly influence, caused by the SARS-CoV-2. An RNA modification called m(6)A is connected with viral infections. The appearance of m(6)A modifications related to several essential proteins affects proteins' structure and function. Therefore, finding the location and number of m(6)A RNA modifications is crucial for subsequent analysis of the protein expression profile.