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Updated: Mar 12, 2026
2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications
Published on: July 10, 2020
Nanopore direct RNA sequencing for RNA modification analysis: workflow assessment and computational tool benchmarking
Zhixing Wu1,2,3, Jiayi Li1,4, Rong Xia1,2,3
1Department of Biosciences and Bioinformatics, Center for Intelligent RNA Therapeutics, Suzhou Key Laboratory of Cancer Biology and Chronic Disease, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China.
Oxford Nanopore Technologies (ONT) offers real-time, long-read sequencing for RNA. Detecting RNA modifications using ONT data presents challenges due to significant tool variability and inherent difficulties.
Area of Science:
- Genomics
- Transcriptomics
- Epitranscriptomics
Background:
- Recent advancements in sequencing technologies have revolutionized genomic and transcriptomic analysis.
- Oxford Nanopore Technologies (ONT) provides unique capabilities for real-time, long-read, and direct RNA sequencing.
Purpose of the Study:
- To provide a comprehensive review of ONT sequencing for RNA analysis.
- To summarize computational tools for ONT data processing and RNA modification detection.
- To assess the performance of different RNA modification detection methods using ONT data.
Main Methods:
- Overview of the ONT analytical workflow: base calling, alignment, re-squiggling, and quality control.
- Illustration of various RNA modification detection techniques, including statistical models, machine learning, deep learning, and large language models.
- Benchmark analysis of m6A and pseudouridine (Ψ) detection across two public datasets.
Main Results:
- Substantial variability observed across different computational tools for RNA modification detection.
- Inherent difficulties in reliably detecting RNA modifications from ONT sequencing signals were highlighted.
- Comparison of ONT-based approaches with conventional RNA modification detection technologies.
Conclusions:
- ONT sequencing offers powerful capabilities for epitranscriptomic research.
- Current computational tools show significant variability in detecting RNA modifications from ONT data.
- Future directions should focus on improving accuracy, robustness, and biological interpretability for ONT-based epitranscriptomic studies.
