Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA-seq03:21

RNA-seq

10.0K
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...
10.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Neural mechanisms of cough: insights from multimodal magnetic resonance imaging-a narrative review.

Journal of thoracic disease·2026
Same author

Programmable mRNA 3'UTR engineering restores MHC-I and overcomes immune evasion in prostate cancer.

Nature biomedical engineering·2026
Same author

Optimizing Sowing Date and Nitrogen Management to Trade Off Yield and Nitrate Leaching in Maize-Soybean Intercropping Under CMIP6 Climate Scenarios in the North China Plain.

Plants (Basel, Switzerland)·2026
Same author

Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome.

Science advances·2026
Same author

Histone deacetylase enzyme activity is not the universal anticancer target of HDAC inhibitors.

Signal transduction and targeted therapy·2026
Same author

Astrocytic ACSBG1 depletion improves lipid-cytokine signaling and attenuates α-Synuclein pathology in a Parkinson's disease mouse model.

bioRxiv : the preprint server for biology·2026
Same journal

Large-scale discovery platform enables identification of peptides targeting drug-resistant candidiasis.

Cell reports methods·2026
Same journal

A computational method to design broad-spectrum T cell-inducing vaccines applied to Betacoronaviruses.

Cell reports methods·2026
Same journal

MalDeepSeq panel: A targeted ultra-deep sequencing approach to trace drug resistance markers in Plasmodium falciparum.

Cell reports methods·2026
Same journal

Induced pluripotent stem cell-derived macrophages enable broad modeling of human inflammasome signaling.

Cell reports methods·2026
Same journal

Rapid discovery of cell-surface glycosylation regulators using a lectin-based magnetic CRISPR screen.

Cell reports methods·2026
Same journal

A real-time FRET ubiquitin transfer assay for quantitative characterization of ternary complexes in targeted protein degradation.

Cell reports methods·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2025

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

5.6K

PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk

Venkata Soumith Jonnakuti1, Eric J Wagner2, Mirjana Maletić-Savatić3

  • 1Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA; Program in Quantitative and Computational Biology, Baylor College of Medicine, Houston, TX 77030, USA; Medical Scientist Training Program, Baylor College of Medicine, Houston, TX 77030, USA.

Cell Reports Methods
|February 7, 2024
PubMed
Summary
This summary is machine-generated.

Alternative polyadenylation (APA) is crucial for gene regulation but is understudied in diseases. PolyAMiner-Bulk, a new deep learning tool, accurately identifies novel APA changes from RNA sequencing data, advancing disease research.

Keywords:
CP: Systems biologyalternative polyadenylation (APA)bioinformaticscomputational biologydeep learninggene regulationlarge language model (LLM)post-transcriptional regulation

More Related Videos

Author Spotlight: Exploring the Frontier of mRNA Research with Poly A Tail Analysis Techniques
08:16

Author Spotlight: Exploring the Frontier of mRNA Research with Poly A Tail Analysis Techniques

Published on: January 12, 2024

960
An Easy Method for Plant Polysome Profiling
11:09

An Easy Method for Plant Polysome Profiling

Published on: August 28, 2016

12.7K

Related Experiment Videos

Last Updated: Jul 4, 2025

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

5.6K
Author Spotlight: Exploring the Frontier of mRNA Research with Poly A Tail Analysis Techniques
08:16

Author Spotlight: Exploring the Frontier of mRNA Research with Poly A Tail Analysis Techniques

Published on: January 12, 2024

960
An Easy Method for Plant Polysome Profiling
11:09

An Easy Method for Plant Polysome Profiling

Published on: August 28, 2016

12.7K

Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Alternative polyadenylation (APA) is a critical post-transcriptional regulation mechanism influencing gene expression.
  • Current APA detection methods using bulk RNA sequencing (RNA-seq) are limited by reliance on existing annotations and inability to detect novel or complex APA events.
  • Challenges include resolving overlapping cleavage and polyadenylation sites (C/PASs) and noisy 3' UTR data.

Purpose of the Study:

  • To develop an advanced computational tool for accurate and comprehensive APA analysis from bulk RNA-seq data.
  • To overcome the limitations of existing methods in identifying novel, tissue-, and disease-specific APA variations.
  • To provide insights into the regulatory roles and disease implications of APA.

Main Methods:

  • Introduction of PolyAMiner-Bulk, an attention-based deep learning algorithm.
  • The algorithm is designed to learn C/PAS sequence grammar and resolve overlapping C/PASs.
  • Capability to detect non-proximal-to-distal APA changes and generate visualizations of APA dynamics.

Main Results:

  • PolyAMiner-Bulk demonstrated superior performance in identifying APA changes across multiple datasets compared to existing methods.
  • The tool accurately recapitulates C/PAS sequence grammar and resolves complex C/PAS structures.
  • It successfully captures a broader range of APA variations, including non-proximal-to-distal events.

Conclusions:

  • PolyAMiner-Bulk offers a robust and accurate paradigm for APA analysis using bulk RNA-seq data.
  • The method enhances the ability to discover novel APA events relevant to human diseases.
  • This advancement is significant given the prevalence of APA and the availability of bulk RNA-seq datasets.