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

19.3K
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%...
19.3K
Variability: Analysis01:11

Variability: Analysis

645
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
645

You might also read

Related Articles

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

Sort by
Same author

A dish-to-biobank framework links β-cell nutrient-stress programs to genetic and dietary risk for Type 2 Diabetes.

bioRxiv : the preprint server for biology·2026
Same author

Clonal lineage tracing of innate immune cells in human cancer.

Cancer cell·2026
Same author

Resolving human neuronal herpesvirus reactivation via petabase-scale association studies.

bioRxiv : the preprint server for biology·2026
Same author

Ancestry-specific rewiring of BCR-MAPK signaling in sarcoidosis B cells.

bioRxiv : the preprint server for biology·2026
Same author

Scalable genotyping in fixed transcriptomes resolves clonal heterogeneity via single-cell sequencing.

bioRxiv : the preprint server for biology·2026
Same author

Single-cell multi-omic analysis of mitochondrial mutational mosaicism and dynamics.

Nature communications·2026
Same journal

Novel variants in LSS related hypotrichosis simplex 14.

Frontiers in genetics·2026
Same journal

Network-based analysis identifies shared mechanisms between ischemic stroke and myocardial infarction and therapeutic ingredients of Buyang Huanwu Decoction.

Frontiers in genetics·2026
Same journal

GWAS analysis of a depression cohort defined by an EHR-phenotyping algorithm reveals the role of immune regulations in depression risk.

Frontiers in genetics·2026
Same journal

Ferroptosis, lipid metabolism, and genetic regulation in postoperative rehabilitation of elderly hip fractures: from molecular mechanisms to clinical translation.

Frontiers in genetics·2026
Same journal

Single-cell and pseudobulk analyses reveal hidden mitochondrial expression imbalance in gastric cancer.

Frontiers in genetics·2026
Same journal

Transcriptomic profiling and experimental validation of myeloid-cell-differentiation-related key genes in osteoarthritis.

Frontiers in genetics·2026
See all related articles

Related Experiment Video

Updated: Mar 30, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K

dcVar: a method for identifying common variants that modulate differential correlation structures in gene expression

Caleb A Lareau1, Bill C White2, Courtney G Montgomery3

  • 1Tandy School of Computer Science - Department of Mathematics, University of Tulsa Tulsa, OK, USA ; Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation Oklahoma City, OK, USA.

Frontiers in Genetics
|November 6, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces dcVar, a tool to identify differential co-expression variants (dcVars) by analyzing gene expression data. It helps understand how genetic variants influence gene correlations and explains phenotypic differences.

Keywords:
RNA-Seqcommon varianteQTLgenome-wide association studymicroarray gene expressionmolecular phenotype

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

42.7K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

3.2K

Related Experiment Videos

Last Updated: Mar 30, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K
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

42.7K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

3.2K

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Differential co-expression analysis is crucial for understanding phenotypic variation.
  • Characterizing genetic variants' roles in regulating differential co-expression remains a challenge.

Purpose of the Study:

  • To develop a statistical methodology and computational tool (dcVar) for identifying differential co-expression variants (dcVars).
  • To infer the function of variants by analyzing their impact on gene co-expression patterns.

Main Methods:

  • Developed a statistical method to identify transcript pairs with differential correlation structure based on variant genotypes.
  • Created a user-friendly and efficient tool, dcVar, for analyzing eQTL and RNA-Seq data.
  • Applied dcVar to the HapMap3 eQTL dataset.

Main Results:

  • Demonstrated dcVar's utility in uncovering novel functions of genetic variants.
  • Provided examples from height genome-wide association studies and cancer drug resistance.
  • Showcased differential correlation structure as a key intermediate phenotype.

Conclusions:

  • Differential correlation structure is a valuable intermediate phenotype for variant function characterization.
  • dcVar facilitates the discovery of novel variant functions in genetic studies.
  • The methodology aids in explaining phenotypic differences through gene co-expression regulation.