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

9.9K
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...
9.9K
Real Time RT-PCR02:57

Real Time RT-PCR

57.1K
Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
57.1K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
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...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Maternal folic acid deficiency stimulates neural cell apoptosis via miR-34a associated with Bcl-2 in the rat foetal brain.

International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience·2018
Same author

Deficiency of Brummer Impaires Lipid Mobilization and JH-Mediated Vitellogenesis in the Brown Planthopper, <i>Nilaparvata lugens</i>.

Frontiers in physiology·2018
Same author

Resveratrol reduces intracellular reactive oxygen species levels by inducing autophagy through the AMPK-mTOR pathway.

Frontiers of medicine·2018
Same author

The Current Epidemiological Landscape of Ventilator-associated Pneumonia in the Intensive Care Unit: A Multicenter Prospective Observational Study in China.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2018
Same author

Layered Structure Produced Nonconcentration Quenching in a Novel Eu<sup>3+</sup>-Doped Phosphor.

ACS applied materials & interfaces·2018
Same author

Enhancement of radiotherapeutic efficacy for esophageal cancer by paclitaxel-loaded red blood cell membrane nanoparticles modified by the recombinant protein anti-EGFR-iRGD.

Journal of biomaterials applications·2018

Related Experiment Video

Updated: Jun 17, 2025

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

37.1K

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in

Jing Yan1, Qiuhong Zeng1, Xianlong Wang2,3

  • 1Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, 350122, China.

BMC Bioinformatics
|August 7, 2024
PubMed
Summary

RankCompV3 accurately identifies differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data by comparing gene expression orderings. This novel method offers improved sensitivity for weak biological signals and controls false positive rates effectively.

Keywords:
Differential expression analysisDifferentially expressed genesRelative expression orderingsSingle-cell RNA sequencing

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.5K
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

13.5K

Related Experiment Videos

Last Updated: Jun 17, 2025

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

37.1K
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.5K
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

13.5K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate identification of differentially expressed genes (DEGs) is crucial but challenging for single-cell RNA sequencing (scRNA-seq) data.
  • Existing algorithms often suffer from high false positive rates (FPRs) and miss subtle biological signals.

Purpose of the Study:

  • To introduce RankCompV3, a novel method for identifying DEGs in scRNA-seq profiles.
  • To address the limitations of existing DEG detection algorithms in terms of accuracy and sensitivity.

Main Methods:

  • RankCompV3 utilizes relative expression orderings (REOs) of gene pairs.
  • Gene expression levels are compared across single-cell profiles to determine pairwise rankings.
  • A 3x3 contingency table and McCullagh's method are employed to assess gene dysregulation.

Main Results:

  • RankCompV3 demonstrated robust control of FPR and high accuracy on both simulated and real scRNA-seq data.
  • The method outperformed 11 other common single-cell DEG detection algorithms.
  • RankCompV3 showed higher sensitivity to weak biological signals compared to existing approaches.

Conclusions:

  • The REOs-based RankCompV3 algorithm is a valuable tool for scRNA-seq data analysis.
  • It enables accurate and sensitive identification of DEGs.
  • The algorithm is implemented in Julia and callable in R, with source code available.