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Updated: Jun 23, 2025
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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
Published on: June 24, 2021
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Multiplexed single-cell characterization of alternative polyadenylation regulators.
Madeline H Kowalski1, Hans-Hermann Wessels2, Johannes Linder3
1New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York University Grossman School of Medicine, New York, NY, USA.
Cell
|June 26, 2024
Summary
This study introduces CPA-Perturb-seq to map how cleavage and polyadenylation (CPA) proteins control gene expression diversity. The findings reveal coordinated regulation of polyA sites by RNA processing factors, advancing our understanding of post-transcriptional regulation.
Area of Science:
- Molecular Biology
- Genomics
- Computational Biology
Background:
- Mammalian genes utilize multiple polyadenylation (polyA) sites, generating significant transcript diversity.
- The cleavage and polyadenylation (CPA) machinery precisely regulates polyA site selection.
- Understanding the mechanisms governing polyA site choice is crucial for deciphering gene expression regulation.
Purpose of the Study:
- To investigate how CPA proteins influence polyA site usage and transcript diversity.
- To develop and apply a novel screening method for analyzing polyA site regulation.
- To identify regulatory modules and cis-regulatory codes governing polyA site selection.
Main Methods:
- CPA-Perturb-seq: A multiplexed perturbation screen using 3' single-cell RNA sequencing (scRNA-seq).
- Development of a computational framework to detect perturbation-induced changes in polyadenylation.
- Training and validation of a deep neural network (APARENT-Perturb) for predicting tandem polyA site usage.
Main Results:
- Identification of co-regulated polyA site modules linked to distinct nuclear RNA processing events (elongation, splicing, termination, surveillance).
- Characterization of a cis-regulatory code that predicts responses to CPA perturbations.
- Discovery of interactions between different regulatory complexes influencing polyA site choice.
Conclusions:
- Multiplexed single-cell perturbation screens are powerful tools for studying post-transcriptional regulation.
- CPA regulators coordinate polyA site usage through diverse RNA processing pathways.
- The developed deep learning model accurately predicts polyA site usage and regulatory interactions.
