Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Point and Frameshift Mutations

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

Position-effect Variegation

Position-effect Variegation

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.

Conservative Site-specific Recombination and Phase Variation

Conservative Site-specific Recombination and Phase Variation

Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...

You might also read

Related Articles

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

Sort by

Same author

Treatment of Severe Periodontitis using Exosome-Mediated Combination Therapies: A Retrospective Cohort Study.

Oral health & preventive dentistry·2026

Same author

Taming the reference genome jungle: the refget sequence collection standard.

bioRxiv : the preprint server for biology·2025

Same author

Atacformer: A transformer-based foundation model for analysis and interpretation of ATAC-seq data.

bioRxiv : the preprint server for biology·2025

Same author

ConceptDrift: leveraging spatial, temporal and semantic evolution of biomedical concepts for hypothesis generation.

Bioinformatics (Oxford, England)·2025

Same author

Bioconductor's Computational Ecosystem for Genomic Data Science in Cancer.

Methods in molecular biology (Clifton, N.J.)·2025

Same author

AI-readiness Criteria for Biomedical Data.

bioRxiv : the preprint server for biology·2024

Search research articles

Related Experiment Video

Updated: Oct 23, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Bedshift: perturbation of genomic interval sets.

Aaron Gu^1,2, Hyun Jae Cho^1,2, Nathan C Sheffield^3,4,5,6

¹Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.

|August 21, 2021

Summary

We introduce Bedshift, a tool to benchmark region set similarity metrics used in functional genomics. Bedshift perturbs genomic region data, revealing how different metrics respond to changes like added or shifted regions.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Related Experiment Videos

Last Updated: Oct 23, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Functional genomics experiments (e.g., ChIP-Seq, ATAC-Seq) generate genomic region sets.
Objective evaluation of similarity metrics for these region sets is currently lacking.

Purpose of the Study:

To present Bedshift, a novel tool for perturbing BED files.
To enable benchmarking of region set similarity metrics.

Main Methods:

Bedshift randomly shifts, adds, and drops regions from reference BED files.
Generated perturbed files are used to test and compare similarity metrics.

Main Results:

Bedshift facilitates the objective evaluation of region set similarity metrics.
Jaccard score is sensitive to region additions/deletions; coverage score is sensitive to region shifts.

Conclusions:

Bedshift provides a robust method for benchmarking genomic region similarity metrics.
Understanding metric behavior is crucial for accurate functional genomics data analysis.