Primary sequence-assisted prediction of m6A RNA methylation sites from Oxford nanopore direct RNA sequencing data

Yuxin Zhang, Daiyun Huang, Zhen Wei, Kunqi Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)62-69
Number of pages8
JournalMethods
Volume203
DOIs
Publication statusPublished - Jul 2022

Keywords

  • Machine learning
  • Oxford nanopore technique
  • RNA modification

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