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

Mismatch Repair01:20

Mismatch Repair

6.3K
Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
6.3K
Mismatch Repair01:36

Mismatch Repair

43.5K
Overview
43.5K
Probability Laws01:49

Probability Laws

43.9K
Overview
43.9K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

62.0K
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).
62.0K
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

867
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...
867
Mutations01:35

Mutations

42.9K
Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...
42.9K

You might also read

Related Articles

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

Sort by
Same author

Behavior and Physiology Outpace Form When Linking Traits to Ecological Responses within Populations: A Meta-Analysis.

The American naturalist·2026
Same author

Benchmarking reliability and calibration of LLMs for multi-cancer early detection test communication.

JAMIA open·2026
Same author

Pan-Cancer Genomic Scars of Alternative End Joining and Single-Strand Annealing.

bioRxiv : the preprint server for biology·2026
Same author

Multivariate causal effects: a Bayesian causal regression factor model.

Biometrics·2026
Same author

A Longitudinal Comprehensive Biospecimen and Clinical Data Repository for Cancer Early Detection: The InAdvance Study.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

A Gene Expression Tumor Signature Optimizing Partial Area-Under-the-Curve (pAUC) to Improve Specificity for Indolent Prostate Cancer.

The Prostate·2026

Related Experiment Video

Updated: Jan 17, 2026

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

Bayesian Non-Negative Matrix Factorization with Correlated Mutation Type Probabilities for Mutational Signatures.

Iris Lang, Jenna Landy, Giovanni Parmigiani

    Arxiv
    |September 22, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces new Bayesian Nonnegative Matrix Factorization (NMF) methods for cancer mutational signature analysis. These novel approaches account for dependencies between mutation types, improving accuracy and understanding of biological interactions.

    More Related Videos

    In Vivo Modeling of the Morbid Human Genome using Danio rerio
    12:31

    In Vivo Modeling of the Morbid Human Genome using Danio rerio

    Published on: August 24, 2013

    21.2K
    Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells
    11:06

    Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

    Published on: February 24, 2014

    13.5K

    Related Experiment Videos

    Last Updated: Jan 17, 2026

    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
    In Vivo Modeling of the Morbid Human Genome using Danio rerio
    12:31

    In Vivo Modeling of the Morbid Human Genome using Danio rerio

    Published on: August 24, 2013

    21.2K
    Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells
    11:06

    Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

    Published on: February 24, 2014

    13.5K

    Area of Science:

    • Genomics
    • Computational Biology
    • Cancer Research

    Background:

    • Somatic mutations are key cancer markers.
    • Mutational signature analysis, often using Nonnegative Matrix Factorization (NMF), is crucial in cancer research.
    • Current NMF methods assume independence between mutation types, limiting biological insights.

    Purpose of the Study:

    • To develop novel Bayesian NMF methods that model dependencies between mutation types in cancer signatures.
    • To improve the accuracy and efficiency of mutational signature analysis.
    • To provide a more flexible framework for understanding biological interactions in cancer genomics.

    Main Methods:

    • Implemented a Bayesian NMF with a Multivariate Truncated Normal prior on the signatures matrix, incorporating external data (COSMIC signatures).
    • Developed a hierarchical Bayesian NMF model to allow discovery of the covariance structure.
    • Utilized Markov Chain Monte Carlo (MCMC) for model convergence.

    Main Results:

    • The proposed models converge faster and show improved accuracy, particularly with small sample sizes, compared to models with independent priors.
    • The hierarchical model offers greater flexibility by learning the dependence structure.
    • The methods enhance the understanding of biological interactions and their variations across cancer types.

    Conclusions:

    • Novel Bayesian NMF methods effectively model dependencies between mutation types in cancer signatures.
    • These advancements improve accuracy and provide deeper biological insights into cancer development.
    • The developed methods and open-source code contribute to future research in mutational signature analysis and NMF applications.