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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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Related Experiment Video

Updated: Jun 9, 2025

A Method for Measuring RNA N6-methyladenosine Modifications in Cells and Tissues
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m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.

Boyi Yu1, Genta Nagae2, Yutaka Midorikawa3

  • 1Advanced Data Science Division, Research Center of Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku 153-8904, Tokyo, Japan.

Briefings in Bioinformatics
|October 22, 2024
PubMed
Summary

N6-methyladenosine (m6A) mapping in messenger RNAs is crucial for understanding its role in diseases. A new tool, m6ATM, uses deep learning with Direct RNA Sequencing data to accurately detect m6A sites, advancing epitranscriptomic research.

Keywords:
deep neural networksepitranscriptomicslong-read RNA sequencingm6A

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Area of Science:

  • Epitranscriptomics
  • Computational Biology
  • Genomics

Background:

  • N6-methyladenosine (m6A) is a key RNA modification involved in biological processes and diseases.
  • Accurate transcriptome-wide m6A profiling is essential for research.
  • Direct RNA Sequencing (DRS) shows potential for RNA modification detection but data interpretation is challenging.

Purpose of the Study:

  • To develop a computational pipeline for precise m6A site detection using DRS data.
  • To characterize the m6A epitranscriptome at single-base resolution.

Main Methods:

  • Introduction of m6A Transcriptome-wide Mapper (m6ATM), a Python-based pipeline.
  • Application of deep neural networks, including a WaveNet encoder and dual-stream multiple-instance learning.
  • Utilized Oxford Nanopore Technology Direct RNA Sequencing (DRS) data.

Main Results:

  • m6ATM achieved high accuracy (80%-98%) on in vitro datasets.
  • Outperformed existing tools in benchmarking with human cell line data.
  • Demonstrated ability to provide stoichiometric information and identified PEG10 as a potential m6A target in liver cancer.

Conclusions:

  • m6ATM is a high-performance tool for m6A detection.
  • The tool facilitates advancements in epitranscriptomic research.
  • Enables precise mapping of m6A modifications for disease-related studies.