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Related Experiment Video

Updated: May 7, 2026

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

scPASU: A computational protocol for quantifying polyadenylation site usage and alternative polyadenylation from 3'

Alexandra Krylova¹, Ninh B Le¹, Angela H Ting¹

¹Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.

|May 5, 2026

Summary

This study introduces scPASU, a workflow analyzing 3' single-cell RNA sequencing data to quantify poly(A) site usage and alternative polyadenylation (APA) across cells.

Keywords:

Bioinformatics RNAseq Sequence analysis

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

3' single-cell RNA sequencing (scRNA-seq) provides polyadenylation (poly(A)) site information.
Quantifying poly(A) site usage and alternative polyadenylation (APA) is crucial for understanding gene regulation.
Existing methods may lack comprehensive analysis for scRNA-seq data.

Purpose of the Study:

To present scPASU, a Snakemake workflow for analyzing poly(A) site usage and APA from 3' scRNA-seq data.
To enable quantification of poly(A) site usage per gene and cell.
To facilitate the discovery of novel poly(A) sites and APA events.

Main Methods:

Development of the scPASU Snakemake workflow.
Building a poly(A) site reference database.
Generating a site-by-cell matrix for quantitative analysis.
Implementing statistical tests for APA detection between cell groups.

Main Results:

The scPASU workflow enables accurate quantification of poly(A) site usage from 3' scRNA-seq data.
It facilitates the identification of alternative polyadenylation (APA) events across different cell populations.
The protocol is adaptable to various organisms and sample types.

Conclusions:

scPASU provides a robust and configurable solution for analyzing poly(A) site dynamics in single cells.
This workflow enhances the utility of 3' scRNA-seq data for studying gene expression regulation.
It supports the discovery of novel biological insights through poly(A) site analysis.