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Updated: Aug 21, 2025

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications
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Detecting RNA modification using direct RNA sequencing: A systematic review.

Xichen Zhao1, Yuxin Zhang1,2, Daiyun Hang1,3

  • 1Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, 215123 Suzhou, Jiangsu, China.

Computational and Structural Biotechnology Journal
|November 16, 2022
PubMed
Summary
This summary is machine-generated.

This review systematically analyzes 15 RNA modification prediction tools using nanopore direct RNA sequencing data. It highlights their models, performance, and future potential for understanding gene expression regulation.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Post-transcriptional RNA modifications regulate gene expression and RNA functions.
  • Accurate identification of RNA modifications is crucial for understanding cellular processes.
  • Nanopore direct RNA sequencing offers single-molecule resolution for detecting multiple RNA modifications simultaneously.

Purpose of the Study:

  • To systematically review and evaluate 15 computational tools for predicting RNA modifications from direct RNA sequencing data.
  • To compare the computational models, input/output formats, supported modifications, and performance of these tools.
  • To discuss current challenges and future directions for nanopore-based RNA modification detection.

Main Methods:

  • Systematic literature review of 15 published RNA modification prediction tools.
  • Analysis of tool characteristics: computational models, input/output, modification types, and performance metrics.
  • Comparative assessment of tool capabilities based on direct RNA sequencing data.

Main Results:

  • Detailed overview of 15 distinct RNA modification prediction tools.
  • Comparison of the strengths and weaknesses of various computational approaches.
  • Identification of commonalities and differences in supported modification types and performance.

Conclusions:

  • Nanopore direct RNA sequencing is a powerful technology for RNA modification detection.
  • A variety of computational tools are available, each with specific applications and limitations.
  • Further development is needed to address challenges and enhance the accuracy and scope of nanopore-based RNA modification analysis.