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

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 microarray-based...

You might also read

Related Articles

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

Sort by
Same author

Orchestrating multi-state QTL analysis with bioconductor.

BMC bioinformatics·2026
Same author

The roles of the acetyltransferase domains of the chromatin regulators KAT6A and KAT6B in vivo.

Development (Cambridge, England)·2026
Same author

Identifying Relevant Covariates in RNA-seq Analysis by Pseudo-Variable Augmentation.

Journal of agricultural, biological, and environmental statistics·2026
Same author

AutoCumulus: an automated mammographic density measure created using artificial intelligence.

BMC cancer·2026
Same author

KAT6A is essential for developmental control gene expression in neural stem and progenitor cells.

PLoS genetics·2026
Same author

AI-based BRAIx risk score for the intermediate-term prediction of breast cancer: a population cohort study.

The Lancet. Digital health·2026

Related Experiment Video

Updated: May 17, 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

Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates.

Steven P Lund1, Dan Nettleton, Davis J McCarthy

  • 1Statistical Engineering Division, National Institute of Standards and Technology.

Statistical Applications in Genetics and Molecular Biology
|October 30, 2012
PubMed
Summary

This study introduces novel quasi-likelihood methods for identifying differentially expressed (DE) genes in RNA-sequence data. These methods improve gene expression analysis by accurately handling low counts and overdispersion, outperforming existing techniques.

More Related Videos

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

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts
07:03

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Published on: January 2, 2018

Related Experiment Videos

Last Updated: May 17, 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

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

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts
07:03

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Published on: January 2, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Next-generation sequencing (NGS) provides RNA-sequence (RNA-seq) data for gene expression measurement.
  • RNA-seq data analysis faces challenges with low-count integers and overdispersion, requiring robust statistical methods.
  • Identifying differentially expressed (DE) genes is crucial for understanding biological processes and disease.

Purpose of the Study:

  • To develop and present novel quasi-likelihood methods for detecting DE genes from RNA-seq data.
  • To adapt Smyth's (2004) error variance estimation approach for RNA-seq data.
  • To evaluate the performance of the proposed methods against existing techniques.

Main Methods:

  • Quasi-likelihood framework for DE gene analysis.
  • Shrunken dispersion estimates adapted from microarray variance estimation.
  • Simulations based on real RNA-seq data to assess performance.

Main Results:

  • The proposed quasi-likelihood methods are computationally efficient.
  • Methods demonstrate favorable performance in detecting DE genes compared to competing approaches.
  • Accurate estimation of false discovery rates was achieved across simulations.

Conclusions:

  • The developed quasi-likelihood methods offer a robust and efficient solution for DE gene identification in RNA-seq.
  • Shrunken dispersion estimates enhance the reliability of DE gene detection.
  • These methods provide a valuable tool for genomic research and bioinformatics analysis.