Search research articles

Related Concept Videos

Mismatch Repair

Mismatch Repair

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...

You might also read

Related Articles

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

Sort by

Same author

Linkage-aware inference of fitness from short-read time-series genomic data.

Virus evolution·2026

Same author

Dual role of glycosylation in resistance to CD4-binding site broadly neutralizing antibodies.

Journal of virology·2026

Same author

Chronic infections can generate SARS-CoV-2-like bursts of viral evolution without epistasis.

Virus evolution·2026

Same author

Predicting viral sensitivity to antibodies using genetic sequences and antibody similarities.

PLoS computational biology·2026

Same author

Linkage-aware inference of fitness from short-read time-series genomic data.

bioRxiv : the preprint server for biology·2026

Same author

Parallel HIV-1 fitness landscapes shape viral dynamics in humans and macaques that develop broadly neutralizing antibodies.

eLife·2025

Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026

Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026

Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026

Same journal

When does additional information improve accuracy of RNA secondary structure prediction?

bioRxiv : the preprint server for biology·2026

Same journal

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026

Same journal

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

bioRxiv : the preprint server for biology·2026

See all related 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

Search research articles

Related Experiment Video

Updated: Jul 3, 2025

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

popDMS infers mutation effects from deep mutational scanning data.

Zhenchen Hong¹, John P Barton^1,2,3

¹Department of Physics and Astronomy, University of California, Riverside, USA.

Biorxiv : the Preprint Server for Biology

|February 14, 2024

Summary

This summary is machine-generated.

Deep mutational scanning (DMS) experiments reveal mutation effects but face analysis challenges. Our new popDMS method, using population genetics, accurately infers mutation effects and epistasis, showing high consistency across replicates.

More Related Videos

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

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

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

Published on: August 21, 2016

Related Experiment Videos

Last Updated: Jul 3, 2025

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

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

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

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

Published on: August 21, 2016

Area of Science:

Genomics and Bioinformatics
Molecular Biology
Computational Biology

Background:

Deep mutational scanning (DMS) enables large-scale functional analysis of genetic mutations.
DMS data often exhibits significant variability between experimental replicates, complicating analysis.
Accurate inference of mutation effects and interactions is crucial for understanding genotype-phenotype relationships.

Approach:

Developed popDMS, a novel computational method grounded in population genetics theory.
popDMS is designed to infer functional effects of mutations from DMS data.
The method was rigorously tested for its consistency and accuracy.

Key Points:

popDMS demonstrates high consistency in inferring single mutation effects and epistasis across experimental replicates.
The method's performance compares favorably against existing computational approaches for DMS data analysis.
popDMS provides robust and reliable functional effect measurements, reducing noise from experimental variation.

Conclusions:

popDMS offers a reliable computational solution for analyzing challenging DMS data.
The method's flexibility allows application to diverse DMS datasets, including those with multiple time points, replicates, and conditions.
popDMS enhances the utility of DMS experiments for large-scale functional genomics studies.