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m6AConquer: a consistently quantified and orthogonally validated database for the N6-methyladenosine (m6A) epitranscriptome.

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Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.

Shuhe Liu1,2,3, Xichen Zhao1,3, Zhen Wei1,2,4

  • 1Department of Biosciences and Bioinformatics, School of Science, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.

Briefings in Bioinformatics
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

This review details bioinformatics methods to link N6-methyladenosine (m6A) RNA modifications to psychiatric disorders. These strategies help understand m6A's role in conditions like major depressive disorder.

Keywords:
causal inferencem6A epitranscriptomicsm6A-QTLmendelian randomizationmulti-omics integrationpsychiatric disorders

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Area of Science:

  • Neuroscience
  • Genetics
  • Bioinformatics

Background:

  • N6-methyladenosine (m6A) is a key RNA modification regulating gene expression in the central nervous system.
  • Dysregulation of m6A is implicated in psychiatric disorders, but causal links to disease phenotypes are unclear.

Purpose of the Study:

  • To provide a comprehensive survey of bioinformatics strategies for investigating m6A's role in psychiatric disorders.
  • To establish a roadmap for developing testable hypotheses for epitranscriptome-targeted therapies.

Main Methods:

  • The review outlines four analytical themes: false-positive signal calibration, causal inference using statistical genetics, cell-type-specific functional analysis, and machine learning for biomarker prediction.
  • Strategies were validated using a case study in major depressive disorder, intersecting m6A effects with psychiatric genetic risk.

Main Results:

  • The presented bioinformatics workflows enable the disentanglement of causal links between specific m6A sites and disease phenotypes.
  • The case study demonstrates the utility of these methods in understanding m6A's contribution to major depressive disorder.

Conclusions:

  • Streamlined bioinformatics approaches are crucial for advancing our understanding of the epitranscriptome in psychiatric disorders.
  • These methods pave the way for novel therapeutic interventions targeting m6A modifications.