Paper Info
Title
Mixed-Weight Neural Bagging for Detecting <inline-formula><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> Modifications in SARS-CoV-2 RNA Sequencing
Abstract
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> modifications in DRS precisely. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> We present a methodology for identifying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> modifications that incorporated mapping and extracted features from DRS data. To detect <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> modifications, we introduce an ensemble method called mixed-weight neural bagging (MWNB), trained with 5-base RNA synthetic DRS containing modified and unmodified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Our strategy enables the prediction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> modifications using DRS data and completes the identification of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> modifications on the SARS-CoV-2. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance:</i> The Corona Virus Disease 2019 (COVID-19) outbreak has significantly influence, caused by the SARS-CoV-2. An RNA modification called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> is connected with viral infections. The appearance of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> modifications related to several essential proteins affects proteins’ structure and function. Therefore, finding the location and number of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^6A$</tex-math></inline-formula> RNA modifications is crucial for subsequent analysis of the protein expression profile.
Year
DOI
Venue
2022
10.1109/TBME.2022.3150420
IEEE Transactions on Biomedical Engineering
Keywords
DocType
Volume
COVID-19,Humans,RNA, Viral,SARS-CoV-2,Sequence Analysis, RNA
Journal
69
Issue
ISSN
Citations 
8
0018-9294
0
PageRank 
References 
Authors
0.34
10
13
Name
Order
Citations
PageRank
Ruhan Liu100.34
Liang Ou200.34
Bin Sheng336861.19
Pei Hao41098.08
Li Ping5104698.88
Xiaokang Yang63581238.09
Guangtao Xue745652.52
Lei Zhu818220.07
Yuyang Luo900.34
Ping Zhang1000.34
Po Yang1100.34
Huating Li12225.14
David Dagan Feng133329413.76