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

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

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

Sort by
Same author

Identification of Soybean <i>E1</i>-<i>E4</i> Gene Orthologs in the Guar Genome Using Comprehensive Transcriptome Assembly and Annotation.

Frontiers in bioscience (Scholar edition)·2025
Same author

Dissection of figured wood trait in curly birch (Betula pendula Roth var. carelica (Mercklin) Hämet-Ahti) using high-throughput genotyping.

Scientific reports·2024
Same author

Development of a High-Density Genetic Map for Muscadine Grape Using a Mapping Population from Selfing of the Perfect-Flowered Vine 'Dixie'.

Plants (Basel, Switzerland)·2022
Same author

Development of SNP Set for the Marker-Assisted Selection of Guar (<i>Cyamopsis tetragonoloba</i> (L.) Taub.) Based on a Custom Reference Genome Assembly.

Plants (Basel, Switzerland)·2021
Same author

The Assessment of Agrobiological and Disease Resistance Traits of Grapevine Hybrid Populations (<i>Vitis vinifera</i> L. × <i>Muscadinia rotundifolia</i> Michx.) in the Climatic Conditions of Crimea.

Plants (Basel, Switzerland)·2021
Same author

Identification of Key Metabolic Pathways and Biomarkers Underlying Flowering Time of Guar (<i>Cyamopsis tetragonoloba</i> (L.) Taub.) via Integrated Transcriptome-Metabolome Analysis.

Genes·2021
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2026

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform
13:14

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform

Published on: August 10, 2009

Methods for evaluating gene expression from Affymetrix microarray datasets.

Ning Jiang1, Lindsey J Leach, Xiaohua Hu

  • 1School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk

BMC Bioinformatics
|June 19, 2008
PubMed
Summary
This summary is machine-generated.

Comparing seven gene expression analysis methods on barley Affymetrix microarray data, the PDNN method showed superior performance in detecting differentially expressed genes, outperforming the default Affymetrix MAS5.0. This aids researchers in selecting optimal analysis techniques.

More Related Videos

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Related Experiment Videos

Last Updated: Jul 4, 2026

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform
13:14

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform

Published on: August 10, 2009

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Affymetrix microarrays are crucial for genome-wide gene expression measurement.
  • Selecting appropriate statistical methods for processing microarray data is challenging.
  • The performance of various gene expression analysis methods remains debated.

Purpose of the Study:

  • To comprehensively evaluate seven common methods for gene expression analysis using Affymetrix microarray data.
  • To compare method performance based on sensitivity, reproducibility, and consistency.
  • To identify the most effective method for analyzing real biological datasets.

Main Methods:

  • Utilized a well-designed Affymetrix microarray experiment with eight diverse barley cultivars and three replicates.
  • Assessed methods on sensitivity in detecting differentially expressed genes and reproducibility across replicates.
  • Employed single feature polymorphisms (SFPs) as an empirical test for differential expression detection.

Main Results:

  • Gene detection varied by over twofold between methods at a given false discovery rate (FDR).
  • The PDNN method demonstrated superior performance across all evaluation metrics.
  • The default Affymetrix MAS5.0 method exhibited inferior performance compared to others.

Conclusions:

  • The PDNN method significantly outperforms other tested methods for differential gene expression detection.
  • This study provides empirical evidence for selecting optimal gene expression analysis tools.
  • Findings aid researchers in maximizing the utility of Affymetrix microarray data.