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Updated: Aug 13, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

stAPAminer: Mining Spatial Patterns of Alternative Polyadenylation for Spatially Resolved Transcriptomic Studies.

Guoli Ji¹, Qi Tang¹, Sheng Zhu²

¹Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China; Department of Automation, Xiamen University, Xiamen 361005, China.

Genomics, Proteomics & Bioinformatics

|January 20, 2023

Summary

This study introduces stAPAminer, a toolkit for analyzing spatial alternative polyadenylation (APA) patterns in tissues using spatial transcriptomics. It reveals reproducible spatial APA dynamics across mouse olfactory bulb layers.

Keywords:

Alternative polyadenylation Imputation Single-cell RNA sequencing Spatial pattern Spatial transcriptomics

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Last Updated: Aug 13, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Area of Science:

Transcriptomics
Molecular Biology
Bioinformatics

Background:

Alternative polyadenylation (APA) drives transcriptome complexity and gene regulation, impacting cellular processes and diseases.
Single-cell RNA sequencing (scRNA-seq) profiles APA but lacks spatial context.
Spatial transcriptomics (ST) offers spatial resolution but spatial APA patterns remain largely unexplored.

Purpose of the Study:

To develop a toolkit, stAPAminer, for analyzing spatial patterns of APA from ST data.
To identify and quantify APA sites and genes with spatially varying APA usage.
To explore the spatial distribution of APA dynamics in the mouse olfactory bulb (MOB).

Main Methods:

Developed stAPAminer toolkit for mining spatial APA patterns from ST data.
Identified and quantified APA sites using ST data.
Employed a k-nearest neighbors imputation model to recover APA signals and identify spatially variable APA genes.

Main Results:

Detailed spatial APA usage across MOB morphological layers was presented.
A comprehensive list of genes with spatial APA dynamics and distinct spatial expression patterns was compiled.
Reproducible spatial APA patterns were observed across multiple MOB ST data replicates.

Conclusions:

stAPAminer leverages ST to provide high-resolution spatial insights into APA patterns.
The toolkit facilitates the exploration of the spatial transcriptomic atlas for APA dynamics.
Identified spatial APA dynamics offer new avenues for understanding tissue complexity and disease.