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

Ribosome Profiling02:24

Ribosome Profiling

3.6K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.6K
Epistasis Analysis01:09

Epistasis Analysis

5.1K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
5.1K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.8K
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%...
17.8K

You might also read

Related Articles

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

Sort by
Same author

Short-Term Oral Spermidine Supplementation Modifies Aspects of Neurodegenerative Disease in Flies and Mice With MPS III.

Journal of inherited metabolic disease·2026
Same author

DNAJC6 Parkinson's disease: Endolysosomal dysfunction and emerging roles for oligodendrocytes.

NPJ Parkinson's disease·2026
Same author

Multimodal bHLH-PAS DNA binding controls specificity and drives obesity.

Nucleic acids research·2026
Same author

The chromosome-level genome sequences of <i>Danio rerio</i> strains AB, Nadia and Cooch Behar.

Wellcome open research·2026
Same author

Tissue tropism of toxic metals in northern quolls (Dasyurus hallucatus) and northern brown bandicoots (Isoodon macrourus) on Groote Eylandt, Australia.

PloS one·2025
Same author

Housing and husbandry factors affecting zebrafish novel tank test responses: a global multi-laboratory study.

Lab animal·2025

Related Experiment Video

Updated: Aug 6, 2025

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

22.9K

Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis.

Lachlan Baer1, Karissa Barthelson1,2, John Postlethwait3

  • 1School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia.

Biorxiv : the Preprint Server for Biology
|March 22, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a new metric to identify true mutation effects in gene expression data. This method distinguishes genuine biological responses from technical artifacts caused by expression quantitative trait loci (eQTLs).

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

38.0K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

13.0K

Related Experiment Videos

Last Updated: Aug 6, 2025

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

22.9K
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

38.0K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

13.0K

Area of Science:

  • Genomics
  • Transcriptomics
  • Bioinformatics

Background:

  • Transcriptome analysis comparing mutant and wild-type genotypes reveals mutation impacts and biological responses.
  • Genes near mutations can appear differentially expressed due to expression quantitative trait loci (eQTLs), not true biological effects.
  • This eQTL artifact complicates functional enrichment studies and leads to incorrect inferences.

Approach:

  • Introduced "chromosomally co-located differentially expressed genes" (CC-DEGs) observed even with dominant mutations.
  • Defined a metric for "differential allelic representation" (DAR) in RNA-sequencing data to quantify localized expression differences.
  • Demonstrated DAR's ability to predict eQTL-driven expression regions and improve functional enrichment analysis.

Key Points:

  • The DAR metric helps distinguish true mutation effects from eQTL artifacts.
  • Improved functional enrichment by excluding or weighting genes based on DAR.
  • Identified CC-DEGs likely related to mutant phenotypes, supporting linkage disequilibrium evolution theories.
  • Observed potential chromosomal aggregation of CC-DEGs in zebrafish evolution.

Conclusions:

  • The DAR metric offers a robust method for addressing eQTL issues in RNA-sequencing data.
  • This approach enhances the accuracy of mutation effect analysis and functional genomics.
  • Provides insights into evolutionary processes influenced by linkage disequilibrium.