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

9.2K
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...
9.2K
Ribosome Profiling02:24

Ribosome Profiling

3.2K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.2K

You might also read

Related Articles

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

Sort by
Same author

MAE-UNETR++: Masked Autoencoder Pretraining for 3-D Lung Nodule Segmentation.

bioRxiv : the preprint server for biology·2026
Same author

Intermittent fasting exacerbates microplastic-induced gut inflammation via Rikenella-mediated regulation of purine metabolism and Th17/Treg balance.

NPJ biofilms and microbiomes·2026
Same author

Single-cell map of the healthy human immune system across the lifespan reveals unique infant immune signatures.

Nature communications·2026
Same author

Network Toxicology and In Vivo Studies Reveal the Toxicity and Mechanisms of Tributyl Citrate Carried by Microplastics in Promoting Colitis-to-Tumorigenesis Transformation.

Environment & health (Washington, D.C.)·2026
Same author

Gut microbiota promotes immune tolerance at the maternal-fetal interface.

Cell·2025
Same author

Single-cell RNA profiling of blood CD4<sup>+</sup> T cells identifies distinct helper and dysfunctional regulatory clusters in children with SLE.

Nature immunology·2025

Related Experiment Video

Updated: Apr 23, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

42.5K

BADGE: a novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data.

Jinghua Gu, Xiao Wang, Leena Halakivi-Clarke

    BMC Bioinformatics
    |September 26, 2014
    PubMed
    Summary

    This study introduces a novel Bayesian framework to accurately quantify mRNA abundance and identify differentially expressed genes from RNA sequencing (RNA-Seq) data by modeling biological and technical variations. The method improves gene expression analysis and accounts for complex data variability.

    More Related Videos

    Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
    05:12

    Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

    Published on: February 2, 2024

    1.6K
    Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
    05:07

    Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

    Published on: November 7, 2025

    551

    Related Experiment Videos

    Last Updated: Apr 23, 2026

    Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
    10:10

    Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

    Published on: September 18, 2021

    42.5K
    Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
    05:12

    Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

    Published on: February 2, 2024

    1.6K
    Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
    05:07

    Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

    Published on: November 7, 2025

    551

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • RNA sequencing (RNA-Seq) offers high-resolution transcriptome analysis.
    • Biological and technical variations complicate accurate mRNA quantification and differential gene expression analysis in RNA-Seq data.

    Purpose of the Study:

    • To develop a novel Bayesian framework for joint estimation of gene-level mRNA abundance and differential expression.
    • To systematically study and model both within-sample and between-sample variations in RNA-Seq count data.

    Main Methods:

    • Developed a Bayesian hierarchical approach incorporating a Poisson-Lognormal model for within-sample variation and a Gamma-Gamma model for between-sample variation.
    • Modeled over-dispersion of read counts among multiple samples to account for inter-sample variability.
    • Utilized simulation studies with synthesized sequencing counts and real data from the Sequencing Quality Control (SEQC) Project for validation.

    Main Results:

    • The proposed Bayesian method accurately quantifies mRNA abundance and identifies differentially expressed genes.
    • Demonstrated superior performance in differential analysis compared to existing RNA-Seq methods, as validated by SEQC project data with ERCC spike-in controls.
    • The model effectively estimates sources of variation, including sequencing biases, as shown in a breast cancer dataset analysis.

    Conclusions:

    • A novel Bayesian hierarchical approach was developed to address within-sample and between-sample variations in RNA-Seq data.
    • Simulation and real-world data applications confirm the method's robust performance.
    • The developed software package is publicly available for use.