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

12.7K
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...
12.7K
Ribosome Profiling02:24

Ribosome Profiling

4.3K
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...
4.3K

You might also read

Related Articles

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

Sort by
Same author

AutoZyme: An Autonomous Agentic Framework to Optimize Bioinformatics Software.

bioRxiv : the preprint server for biology·2026
Same author

SIRPα ablated iPSC-derived macrophages resist hypophagia and enhance mAb-dependent and CAR-mediated cytotoxicity of solid tumors.

Molecular therapy. Oncology·2026
Same author

High-resolution genomic analysis reveals abundant mosaic outcomes of bacterial natural transformation independent of MutS-mediated mismatch repair.

mBio·2026
Same author

Human neural organoid modeling of diffuse midline glioma captures the complexity of patient tumors.

Journal of neuro-oncology·2026
Same author

Deletion detection in SARS-CoV-2 genomes from COVID-19 patients: elimination of false positives.

Virus evolution·2026
Same author

Glioblastoma immunotherapy in the context of the aging immune system: a systematic review and meta-analysis.

Journal of neuro-oncology·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Apr 15, 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

43.0K

EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments.

Ning Leng1, Yuan Li2, Brian E McIntosh3

  • 1Department of Statistics, University of Wisconsin, Madison, WI, USA, Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.

Bioinformatics (Oxford, England)
|April 8, 2015
PubMed
Summary
This summary is machine-generated.

EBSeq-HMM, a novel empirical Bayes mixture model, effectively identifies differentially expressed genes in ordered RNA-seq data by accounting for condition dependence. This method enhances analysis of time-course or spatial gene expression patterns.

More Related Videos

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

10.8K
RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord
11:13

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord

Published on: November 1, 2014

15.2K

Related Experiment Videos

Last Updated: Apr 15, 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

43.0K
A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

10.8K
RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord
11:13

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord

Published on: November 1, 2014

15.2K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) technologies enable common ordered RNA-seq experiments.
  • Identifying genes with changing expression over time or space is a key objective.
  • Existing methods for differential gene expression often assume condition exchangeability, limiting power in ordered data.

Purpose of the Study:

  • To develop a statistical method that accommodates dependence in gene expression across ordered conditions.
  • To improve the identification and characterization of differentially expressed genes in ordered RNA-seq experiments.

Main Methods:

  • An empirical Bayes mixture modeling approach.
  • Implementation of an auto-regressive hidden Markov model (HMM) to model dependencies.
  • Application in simulation studies and case studies.

Main Results:

  • EBSeq-HMM successfully identifies differentially expressed genes in ordered conditions.
  • The method is effective in specifying gene-specific expression paths.
  • EBSeq-HMM can also be applied to inference of isoform expression.

Conclusions:

  • EBSeq-HMM provides a powerful approach for analyzing ordered RNA-seq data.
  • The method addresses limitations of existing methods by accounting for condition dependence.
  • An R package is available for implementation and further research.