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

9.3K
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...
9.3K

You might also read

Related Articles

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

Sort by
Same author

PU-GRAIL: residue-level graph learning for identifying protective bacterial antigens under positive-unlabeled supervision.

Bioinformatics (Oxford, England)Ā·2026
Same author

Development of an Arterial Carbon Dioxide Estimation Model Using End-Tidal Carbon Dioxide Levels during Surgery in the Pediatric Population.

AnesthesiologyĀ·2026
Same author

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biologyĀ·2026
Same author

Ethnicity-specific molecular subtypes and a machine-learning risk model in Asian patients with non-muscle-invasive bladder cancer.

Scientific reportsĀ·2026
Same author

Development of an Airflow-Based Automated Pipeline for Constructing Common Data Model Integrating Structured and Unstructured Medical Data.

Studies in health technology and informaticsĀ·2026
Same author

Deep Learning outperforms physicians in myopathy and neuropathy classification based on Needle Electromyography Signal.

PloS oneĀ·2026

Related Experiment Video

Updated: Apr 27, 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

42.6K

DegPack: a web package using a non-parametric and information theoretic algorithm to identify differentially

Jaehyun An1, Kwangsoo Kim2, Heejoon Chae3

  • 1Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.

Methods (San Diego, Calif.)
|July 2, 2014
PubMed
Summary

This study introduces a new non-parametric, information-theoretic method for identifying differentially expressed genes (DEGs) across multiple phenotypes. The approach is robust to outliers and improves phenotype characterization accuracy compared to existing tools.

Keywords:
Differentially expressed genesInformation theoretic algorithmMulticlassNon-parametric algorithmRNA-seq

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

1.1K

Related Experiment Videos

Last Updated: Apr 27, 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

42.6K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

1.1K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression analysis is crucial for understanding biological differences between phenotypes.
  • Existing methods for identifying differentially expressed genes (DEGs) are often limited to two groups or have drawbacks in multi-class scenarios.

Purpose of the Study:

  • To develop a novel non-parametric and information-theoretic approach for identifying DEGs in multi-class data.
  • To address limitations of existing DEG detection methods, particularly their sensitivity to outliers and inability to capture distinct expression patterns.

Main Methods:

  • Proposed a non-parametric, information-theoretic method for DEG identification.
  • Evaluated the method on simulated and real-world datasets, including drought-resistant rice and breast cancer data.
  • Compared performance against existing DEG analysis tools.

Main Results:

  • The proposed method effectively identified DEGs in multi-class datasets.
  • Demonstrated reduced sensitivity to outliers compared to methods like Kruskal-Wallis.
  • Achieved higher accuracy in characterizing phenotypes using identified DEGs.

Conclusions:

  • The novel non-parametric, information-theoretic approach offers a robust and accurate solution for identifying DEGs in multi-class gene expression data.
  • The method outperforms existing tools, providing better phenotype characterization.
  • A web service is available for practical application of the method and other DEG analysis tools.