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

12.6K
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...
12.6K
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

LBR and LAP2 mediate heterochromatin tethering to the nuclear periphery to preserve genome homeostasis.

Nature cell biology·2026
Same author

Inhibition of asparagine synthetase effectively retards polycystic kidney disease progression.

EMBO molecular medicine·2024
Same author

Monitoring the 5'UTR landscape reveals isoform switches to drive translational efficiencies in cancer.

Oncogene·2022
Same author

The solution structure of Dead End bound to AU-rich RNA reveals an unusual mode of tandem RRM-RNA recognition required for mRNA regulation.

Nature communications·2022
Same author

Global and precise identification of functional miRNA targets in mESCs by integrative analysis.

EMBO reports·2022
Same author

Sequestration of LINE-1 in cytosolic aggregates by MOV10 restricts retrotransposition.

EMBO reports·2022

Related Experiment Video

Updated: Apr 1, 2026

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

505

Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.

Daniel Spies1, Constance Ciaudo2

  • 1Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland ; Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich, Institute of Molecular Life Sciences, Winterthurerstrasse 190, 8057 Zurich, Switzerland.

Computational and Structural Biotechnology Journal
|October 3, 2015
PubMed
Summary

New bioinformatics tools enhance RNA-sequencing (RNA-seq) analysis for time series experiments, improving understanding of gene expression and the transcriptome. This review discusses challenges and future applications in differential expression analysis.

Keywords:
BioinformaticsClusteringDifferential gene expressionRNA-seqTime course analysisTranscriptomics

More Related Videos

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

14.2K
Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture
11:00

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Published on: August 8, 2013

28.0K

Related Experiment Videos

Last Updated: Apr 1, 2026

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

505
Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

14.2K
Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture
11:00

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Published on: August 8, 2013

28.0K

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Gene expression analysis is crucial for biological and medical research.
  • Microarrays have been widely used for gene expression profiling.
  • RNA-sequencing (RNA-seq) offers advanced insights into gene regulation and signaling networks.

Purpose of the Study:

  • To review current challenges in RNA-seq analysis.
  • To highlight new bioinformatics tools for time series RNA-seq data.
  • To discuss improvements and future applications in differential expression analysis.

Main Methods:

  • Review of existing literature and bioinformatics tools for RNA-seq.
  • Focus on computational methods applicable to time series experiments.
  • Exploration of data integration and differential expression analysis techniques.

Main Results:

  • RNA-seq provides a comprehensive view of the transcriptome.
  • New bioinformatics tools are emerging for analyzing time-dependent gene expression data.
  • Optimization of microarray analysis methods can benefit RNA-seq studies.

Conclusions:

  • RNA-seq analysis presents ongoing challenges, particularly for time series data.
  • Advancements in bioinformatics tools are essential for unlocking the full potential of RNA-seq.
  • Future research should focus on improved data integration and novel differential expression analysis methods.