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

Point and Frameshift Mutations01:30

Point and Frameshift Mutations

742
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...
742
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

61.6K
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).
61.6K
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
Spontaneous and Induced Mutations01:30

Spontaneous and Induced Mutations

2.0K
Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).
2.0K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

11.5K
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
11.5K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

3.9K
3.9K

You might also read

Related Articles

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

Sort by
Same author

Stochastic Phylogenetic Models of Shape.

Systematic biology·2026
Same author

Food-Preparation Skills of Adults With ADHD and the Mediating Effect of Executive Functions.

OTJR : occupation, participation and health·2026
Same author

Centromeric satellite expansion drives genome evolution in the snowy owl.

Genome biology·2026
Same author

Genome Scanning Reveals the Genetic Basis of a Color Pattern Morphotype in an Island Population of the European Adder (Vipera berus).

Genome biology and evolution·2026
Same author

Educator as Coach: Teaching Clinical Performance Using Wearable Data.

AEM education and training·2026
Same author

Multicomponent Behavior Change Technique Intervention for Caregivers of People With Alzheimer Disease and Related Dementias: Protocol for a Single-Arm, Personalized Behavioral Trial to Disrupt Sedentary Time.

JMIR research protocols·2026

Related Experiment Video

Updated: Jan 5, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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

Published on: December 10, 2012

11.6K

A Bayesian Framework for Inferring the Influence of Sequence Context on Point Mutations.

Guy Ling1, Danielle Miller1, Rasmus Nielsen2,3,4

  • 1School of Molecular Cell Biology and Biotechnology, Tel-Aviv University, Tel-Aviv, Israel.

Molecular Biology and Evolution
|October 26, 2019
PubMed
Summary

This study introduces a Bayesian statistical model to analyze how surrounding DNA sequences affect mutation rates. The method accurately identifies mutation patterns in poliovirus and HIV-1, revealing enzyme-specific sequence contexts.

Keywords:
MCMCevolutionary modelmutation ratespopulation geneticssequence context

More Related Videos

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.3K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

36.0K

Related Experiment Videos

Last Updated: Jan 5, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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

Published on: December 10, 2012

11.6K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.3K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

36.0K

Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Mutation rates are influenced by surrounding DNA sequences (sequence context).
  • Enzyme activity, which causes mutations, is often specific to particular sequence contexts.
  • Understanding these contexts is crucial for interpreting genetic variation.

Purpose of the Study:

  • To develop a statistical model for detecting and evaluating sequence context effects on mutation rates using deep population sequencing data.
  • To address the computational challenges of analyzing complex sequence contexts.
  • To identify specific sequence contexts linked to biological processes like deamination.

Main Methods:

  • Developed a novel Bayesian statistical method incorporating sparse model selection.
  • Assumed that only a small number of sequence contexts significantly impact mutation rates.
  • Validated the model's accuracy on simulated pentanucleotide (5-nucleotide) contexts with noisy data.

Main Results:

  • The method accurately detected sequence context effects on mutation rates in simulated data.
  • Analysis of poliovirus data revealed sequence contexts associated with ADAR 1/2 deamination.
  • Analysis of HIV-1 data identified sequence contexts linked to APOBEC3G deamination.

Conclusions:

  • The developed Bayesian approach effectively identifies context-dependent mutation patterns in population sequencing data.
  • This method can be applied broadly to discover novel mutable or editing sites.
  • It provides a powerful tool for analyzing large-scale genomic data in the era of next-generation sequencing.