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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Related Experiment Video

Updated: Jun 26, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.

Jialu Liang1, Qing Wang1, Sen Guo1

  • 1Department of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Rd, Suite 7000, Gainesville, FL 32611, United States.

Briefings in Bioinformatics
|June 24, 2026
PubMed
Summary
This summary is machine-generated.

scDeepAPA is a new deep learning tool for analyzing alternative polyadenylation (APA) in single-cell RNA sequencing data. It accurately identifies polyadenylation sites and quantifies APA events, revealing cell-type-specific changes in diseases like Alzheimer's and cancer.

Keywords:
alternative polyadenylation (APA)bidirectional LSTMdeep learningimmunogenomicsmamba architectureneoantigen discoverypolyadenylation site (PAS) predictionpost-transcriptional regulationsingle-cell RNA sequencing

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Last Updated: Jun 26, 2026

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Published on: June 24, 2021

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Alternative polyadenylation (APA) is a key post-transcriptional regulator impacting transcript diversity and function.
  • Single-cell RNA sequencing (scRNA-seq) offers potential for studying cell-type-specific APA dynamics.
  • Existing computational tools for APA are often ill-suited for scRNA-seq data due to limitations with gene annotations and bulk data focus.

Purpose of the Study:

  • To develop a deep learning framework, scDeepAPA, optimized for accurate polyadenylation site (PAS) detection and APA event quantification in scRNA-seq data.
  • To enable functional interpretation of APA dynamics at single-cell resolution.
  • To overcome limitations of existing tools in single-cell transcriptomic analyses.

Main Methods:

  • scDeepAPA integrates convolutional feature extraction with Mamba-based state-space modeling and bidirectional LSTM layers.
  • The framework is trained on high-confidence annotations from PolyASite v3.0 for robust PAS prediction.
  • Performance is benchmarked against state-of-the-art PAS prediction models using human and mouse datasets.

Main Results:

  • scDeepAPA demonstrates superior performance in accuracy, F1 score, and ROC metrics compared to existing models.
  • Application to Alzheimer's disease data reveals cell-type-specific APA remodeling in immune and glial cells, with shifts towards proximal PAS usage.
  • Analysis of KRAS-mutant lung cancer data identifies global proximal PAS activation and tumor-specific intronic APA events generating potential neoantigens.

Conclusions:

  • scDeepAPA provides an accurate and efficient method for PAS identification and APA profiling in single-cell transcriptomics.
  • The tool facilitates in-depth analysis of regulatory mechanisms and immunogenic consequences of APA.
  • scDeepAPA advances the understanding of post-transcriptional regulation in neurodegeneration and cancer by enabling single-cell resolution studies.