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

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes02:16

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

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

Sort by
Same author

Pervasive genome structure heterogeneity in <i>Mycobacterium tuberculosis</i> constitutively generates subpopulations with distinct clinical phenotypes.

iScience·2026
Same author

Quantifying Hierarchical Conflicts in Homology Statements.

Journal of molecular evolution·2025
Same author

Updated Erdman reveals tandem repeat copy number is phase-variable and impacts <i>M. tuberculosis</i> adaptation across evolutionary timescales.

mSystems·2025
Same author

Intercellular mosaic methylation in fast-growing <i>Mycobacterium tuberculosis</i> clinical isolates.

NAR molecular medicine·2025
Same author

Interred mechanisms of resistance and host immune evasion revealed through network-connectivity analysis of <i>M. tuberculosis</i> complex graph pangenome.

mSystems·2025
Same author

Widespread loss-of-function mutations implicating preexisting resistance to new or repurposed anti-tuberculosis drugs.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy·2024

Related Experiment Video

Updated: May 26, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Empirical comparison of cross-platform normalization methods for gene expression data.

Jason Rudy1, Faramarz Valafar

  • 1Biomedical Informatics Research Center, San Diego State University, 5500 Campanile Dr, San Diego, CA, USA.

BMC Bioinformatics
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

Comparing gene expression data across different platforms is challenging due to measurement inconsistencies. Four cross-platform normalization methods (DWD, EB, GQ, XPN) effectively address these issues, with XPN and DWD showing specific strengths.

More Related Videos

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

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Related Experiment Videos

Last Updated: May 26, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput gene expression assays generate large datasets.
  • Data heterogeneity across measurement platforms (microarrays, sequencing) complicates data integration.
  • Cross-platform normalization is crucial for combining and comparing datasets from different sources.

Purpose of the Study:

  • To rigorously compare existing cross-platform normalization methods.
  • To identify the most effective methods for harmonizing gene expression data.
  • To provide researchers with guidance on selecting appropriate normalization techniques.

Main Methods:

  • Evaluation of nine available cross-platform normalization methods.
  • Utilized two public cross-platform gene expression datasets for testing.
  • Assessed methods based on inter-platform concordance and gene list consistency.
  • Introduced new statistics and bootstrapping for robust method evaluation.

Main Results:

  • Four methods (DWD, EB, GQ, XPN) demonstrated general effectiveness in correcting platform effects.
  • XPN achieved high inter-platform concordance with equally sized treatment groups.
  • DWD showed robustness with unequal group sizes and minimal gene detection loss.
  • Other methods failed to adequately correct for platform variations.

Conclusions:

  • DWD, EB, GQ, and XPN are recommended for cross-platform normalization.
  • Method selection depends on specific experimental design (e.g., group sizes).
  • An R package, CONOR, is provided for implementing these nine normalization methods.