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Related Concept Videos

Alternative RNA Splicing02:18

Alternative RNA Splicing

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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...
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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Related Experiment Video

Updated: Jun 3, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA.

Qinglong Zhang1, Liping Kang1, Haoran Yang1

  • 1Cancer Institute, Suzhou Medical College, Soochow University, NO. 199 Ren-ai Road, SIP, Suzhou 215000, China.

Briefings in Bioinformatics
|January 11, 2025
PubMed
Summary

This study introduces spvAPA, a new supervised framework for analyzing alternative polyadenylation (APA) in single-cell and spatial transcriptomics. It helps discover cell subpopulations by integrating gene expression and APA data.

Keywords:
alternative polyadenylationsingle-cell RNA-seqspatial transcriptomicssupervised analysisvisualization

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Area of Science:

  • Genomics
  • Transcriptomics
  • Bioinformatics

Background:

  • Alternative polyadenylation (APA) generates diverse messenger RNA (mRNA) isoforms, contributing to transcriptome complexity.
  • Single-cell and spatial transcriptomics offer new avenues for APA analysis, potentially revealing hidden cell subpopulations.
  • Existing analysis tools often fall short for APA data, and unsupervised methods may ignore crucial annotations like cell types.

Purpose of the Study:

  • To develop a supervised analytical framework, spvAPA, tailored for alternative polyadenylation (APA) analysis in single-cell and spatial transcriptomics.
  • To enable the discovery of novel cell subtypes and spatial morphologies by integrating gene expression and APA data.
  • To overcome limitations of conventional tools and unsupervised methods in analyzing complex transcriptomic data.

Main Methods:

  • Developed an iterative imputation method using weighted nearest neighbors to reconstruct missing APA signatures, integrating gene expression and APA data.
  • Implemented a supervised feature selection method, sparse partial least squares discriminant analysis, to identify key APA features for cell type and spatial morphology discrimination.
  • Enhanced high-dimensional data visualization by incorporating APA features alongside gene expression and APA modalities.

Main Results:

  • spvAPA effectively recovers missing APA signatures by integrating multi-modal transcriptomic data.
  • The framework successfully identifies APA features that distinguish between cell types and spatial patterns.
  • Evaluations on nine diverse datasets confirm the effectiveness and broad applicability of spvAPA for transcriptomic analysis.

Conclusions:

  • spvAPA provides a powerful supervised approach for analyzing alternative polyadenylation in single-cell and spatial transcriptomics.
  • The framework facilitates the discovery of cell subpopulations and spatial structures previously undetectable.
  • spvAPA represents a significant advancement in leveraging APA for deeper biological insights from transcriptomic data.