Mixed-Weight Neural Bagging for Detecting $m^6A$ Modifications in SARS-CoV-2 RNA Sequencing

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 $m^6A$ 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 $m^6A$ modifications in DRS precisely. Methods: We present a methodology for identifying $m^6A$ modifications that incorporated mapping and extracted features from DRS data. To detect $m^6A$ modifications, we introduce an ensemble method called mixed-weight neural bagging (MWNB), trained with 5-base RNA synthetic DRS containing modified and unmodified $m^6A$. 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 $m^6A$ modifications using DRS data and completes the identification of $m^6A$ 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 $m^6A$ is connected with viral infections. The appearance of $m^6A$ modifications related to several essential proteins affects proteins’ structure and function. Therefore, finding the location and number of $m^6A$ RNA modifications is crucial for subsequent analysis of the protein expression profile.