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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Author Spotlight: Decoding RNA Methylation's Role in Pancreatic Cancer - A Single-Base Resolution Study

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Systematic evaluation of computational tools for multitype RNA modification detection using nanopore direct RNA

Tingting Luo¹, Moping Xu¹, Miao Wang¹

¹Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital affiliated to Tongji University, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, China.

|December 10, 2025

Summary

This summary is machine-generated.

Evaluating 86 tools for RNA modification detection via nanopore sequencing, this study finds retraining with diverse data improves accuracy. Non-m⁶A modification tools require further development for reliable biological validation.

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A Nonsequencing Approach for the Rapid Detection of RNA Editing

A Nonsequencing Approach for the Rapid Detection of RNA Editing

Published on: April 21, 2022

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Nanopore direct RNA sequencing enables single-base resolution detection of RNA modifications.
Accurate identification of diverse RNA modifications is crucial for understanding gene regulation and cellular function.

Purpose of the Study:

To systematically evaluate the performance of 86 computational tools for detecting six key RNA modifications (m⁶A, Ψ, m⁵C, A-to-I editing, m⁷G, and m¹A) using nanopore direct RNA sequencing data.
To assess the impact of retraining tools with combined in vitro transcription and real biological samples on their accuracy and generalizability.
To identify the strengths and weaknesses of current computational tools for various RNA modification detection tasks.

Main Methods:

Systematic evaluation of 86 computational tools across six RNA modifications.
Utilized direct RNA sequencing data from RNA002 and RNA004 chemistries.
Assessed tool performance before and after retraining with combined in vitro and real biological samples.

Main Results:

Retraining tools significantly enhanced accuracy and generalizability, particularly for Ψ, m⁵C, and A-to-I modifications.
m⁶A detection tools demonstrated high accuracy on real biological samples.
Non-m⁶A detection tools exhibited challenges in precision-recall balance, quantification accuracy, and biological validity.

Conclusions:

Diverse training data, including both in vitro and real biological samples, is essential for improving RNA modification detection tools.
Current non-m⁶A detection tools require further optimization to ensure reliable performance and biological relevance.
Development of tools with enhanced capability for distinguishing between modification types at single-base resolution is needed.