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Related Concept Videos

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Related Experiment Video

Updated: Mar 29, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Single-Cell Gene Module Inference Reveals Alternative Polyadenylation Dynamics Associated with Autism.

Fei Liu1, Haoran Yang1, Xiaohui Wu1,2,3,4

  • 1Cancer Institute, Suzhou Medical College, Soochow University, Suzhou 215000, China.

International Journal of Molecular Sciences
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

Alternative polyadenylation (APA) impacts autism spectrum disorder (ASD) pathogenesis. Our study reveals cell-type-specific APA patterns in the brain, improving ASD cell identification and offering new therapeutic targets.

Keywords:
alternative polyadenylationautism spectrum disordergene modulesingle-nucleus RNA sequencing

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Area of Science:

  • Neuroscience
  • Genetics
  • Computational Biology

Background:

  • Autism spectrum disorder (ASD) exhibits significant genetic heterogeneity.
  • Post-transcriptional regulation, specifically alternative polyadenylation (APA), is crucial in ASD pathogenesis.
  • Current ASD research often overlooks the collective impact of APA on gene modules within specific cell types.

Purpose of the Study:

  • To develop an integrative computational framework to identify ASD-associated gene modules driven by APA.
  • To predict cell-type-specific ASD-related cells using APA profiles.
  • To explore the role of APA in ASD at a high resolution across cell types, brain regions, and sex.

Main Methods:

  • Combined matrix factorization and machine learning techniques.
  • Applied the framework to human brain single-nucleus RNA sequencing (snRNA-seq) data.
  • Integrated APA data with gene expression for enhanced predictive modeling.

Main Results:

  • Systematically uncovered cell type, brain region, and sex-specific APA regulatory patterns in ASD.
  • Identified APA modules enriched in synaptic function, neurodevelopment, and immune response pathways, particularly in prefrontal cortex excitatory neurons.
  • Developed a classification model using APA genes that effectively distinguishes ASD cells from normal cells.
  • Demonstrated that integrating APA with gene expression significantly improves prediction accuracy.

Conclusions:

  • APA represents an independent and biologically informative regulatory layer in ASD.
  • The study delineates a high-resolution APA regulatory landscape in ASD.
  • Findings offer novel insights and potential therapeutic avenues beyond transcriptional abundance.