Jove
Visualize
Contact Us

Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

5.6K
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...
5.6K
Epistasis01:39

Epistasis

49.9K
In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
49.9K
Exon Recombination02:32

Exon Recombination

4.0K
The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon...
4.0K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.9K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.9K
Position-effect Variegation02:32

Position-effect Variegation

6.9K
In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
6.9K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.8K
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...
6.8K

You might also read

Related Articles

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

Sort by
Same author

On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.

Journal of mathematical biology·2026
Same author

Genetic background shapes AI-predicted variant effects.

bioRxiv : the preprint server for biology·2026
Same author

Bacterial proteome foundation model enhances functional prediction from enzymes to ecological interactions.

bioRxiv : the preprint server for biology·2026
Same author

Evolution of the rate, molecular spectrum, and fitness effects of mutation under minimal selection in Caenorhabditis elegans.

Genetics·2026
Same author

Evolution of the rate, molecular spectrum, and fitness effects of mutation under minimal selection in <i>Caenorhabditis elegans</i>.

bioRxiv : the preprint server for biology·2026
Same author

Inference and Visualization of Complex Genotype-Phenotype Maps.

Molecular biology and evolution·2026
Same journal

Demonstration of a quantum C-NOT gate in a time-multiplexed fully reconfigurable photonic processor.

Nature communications·2026
Same journal

Nonlinear quantum light source with van der Waals ferroelectric NbOX<sub>2</sub> (X = Br, I).

Nature communications·2026
Same journal

Antagonistic histone H2A variants and autonomous heterochromatin formation shape epigenomic patterns in Arabidopsis.

Nature communications·2026
Same journal

The long tail of nitrate pollution in groundwater challenges governance of global water quality.

Nature communications·2026
Same journal

Select microbial metabolites promote tau aggregation in a murine tauopathy model.

Nature communications·2026
Same journal

Warming climate has lengthened global intense tropical cyclone seasons.

Nature communications·2026
See all related articles
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 Experiment Video

Updated: Dec 24, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.6K

Minimum epistasis interpolation for sequence-function relationships.

Juannan Zhou1, David M McCandlish2

  • 1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.

Nature Communications
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to predict biological function for unassayed genotypes using sequence-function relationships. This approach models genetic interactions effectively, even with limited data, aiding in understanding complex biological systems.

More Related Videos

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe
07:55

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

Published on: March 7, 2019

8.4K
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.9K

Related Experiment Videos

Last Updated: Dec 24, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.6K
A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe
07:55

A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

Published on: March 7, 2019

8.4K
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.9K

Area of Science:

  • Genomics and Systems Biology
  • Computational Biology and Bioinformatics

Background:

  • Massively parallel phenotyping assays generate vast amounts of genotype-phenotype data, offering insights into mutation combinations.
  • These assays are not exhaustive, creating a need for methods to impute phenotypes for unmeasured genotypes.
  • Understanding genetic interactions is crucial for predicting biological function.

Purpose of the Study:

  • To develop an imputation method for predicting phenotypes of unassayed genotypes.
  • To infer the least epistatic sequence-function relationship from experimental data.
  • To model complex genetic interactions and explore biological fitness landscapes.

Main Methods:

  • Developed an imputation technique based on inferring minimal epistasis in sequence-function relationships.
  • The method reconstructs genetic backgrounds to minimize changes in mutational effects.
  • Applied the imputation model to high-throughput transcription factor binding assays.

Main Results:

  • The developed models accurately capture complex, higher-order genetic interactions near existing data points.
  • Models approximate additivity in regions with sparse or absent data, ensuring robustness.
  • Successfully explored the fitness landscape of Protein G using imputed data.

Conclusions:

  • The proposed imputation method effectively predicts unassayed genotype-phenotype relationships.
  • This approach provides a powerful tool for analyzing complex genetic architectures and fitness landscapes.
  • Enables deeper understanding of biological function from limited high-throughput experimental data.