TY - JOUR
T1 - Primary sequence-assisted prediction of m6A RNA methylation sites from Oxford nanopore direct RNA sequencing data
AU - Zhang, Yuxin
AU - Huang, Daiyun
AU - Wei, Zhen
AU - Chen, Kunqi
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant No. 32100519); Scientific Research Foundation for Advanced Talents of Fujian Medical University (Grant No. XRCZX202109).
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/7
Y1 - 2022/7
N2 - Traditional epitranscriptome profiling approach relies on specific antibodies or chemical treatments to capture modified RNA molecules and then applies high throughput sequencing to identify their transcriptomic locations. However, due to the lack of suitable or high-quality antibodies, only a small proportion of the 170 documented RNA modifications were profiled with those approaches. Direct sequencing of native RNA molecules using Oxford Nanopore Technologies (ONT) enabled straight inspection of RNA modifications and offered a promising alternative solution. N6-methyladenosine (m6A) is known to cause characteristic changes and increased base call errors of ONT signals compared with non-modified adenosines, based on which, the m6A sites can be identified directly from ONT signals. Meanwhile, a number of studies have shown that it is possible to predict m6A sites from RNA primary sequences. Using the m6A sites revealed by Illumina technology as benchmark, we showed that, the accuracy of ONT-based m6A site prediction can be further increased by integrating additional information from the primary sequences of RNA (AUROC of 0.918), compared with using ONT signals only (AUROC 0.878 using Base call error features, and 0.804 using Tombo features), providing a new perspective for more reliable mining of the relatively noisy ONT signals.
AB - Traditional epitranscriptome profiling approach relies on specific antibodies or chemical treatments to capture modified RNA molecules and then applies high throughput sequencing to identify their transcriptomic locations. However, due to the lack of suitable or high-quality antibodies, only a small proportion of the 170 documented RNA modifications were profiled with those approaches. Direct sequencing of native RNA molecules using Oxford Nanopore Technologies (ONT) enabled straight inspection of RNA modifications and offered a promising alternative solution. N6-methyladenosine (m6A) is known to cause characteristic changes and increased base call errors of ONT signals compared with non-modified adenosines, based on which, the m6A sites can be identified directly from ONT signals. Meanwhile, a number of studies have shown that it is possible to predict m6A sites from RNA primary sequences. Using the m6A sites revealed by Illumina technology as benchmark, we showed that, the accuracy of ONT-based m6A site prediction can be further increased by integrating additional information from the primary sequences of RNA (AUROC of 0.918), compared with using ONT signals only (AUROC 0.878 using Base call error features, and 0.804 using Tombo features), providing a new perspective for more reliable mining of the relatively noisy ONT signals.
KW - Machine learning
KW - Oxford nanopore technique
KW - RNA modification
UR - http://www.scopus.com/inward/record.url?scp=85128336334&partnerID=8YFLogxK
U2 - 10.1016/j.ymeth.2022.04.003
DO - 10.1016/j.ymeth.2022.04.003
M3 - Article
C2 - 35429629
AN - SCOPUS:85128336334
SN - 1046-2023
VL - 203
SP - 62
EP - 69
JO - Methods
JF - Methods
ER -