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

Mutations01:39

Mutations

Overview
Mutations01:35

Mutations

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...
Mismatch Repair01:20

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...
Mismatch Repair01:36

Mismatch Repair

Overview
Point and Frameshift Mutations01:30

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

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

Sort by
Same author

Accurate detection of tumor clonality and ongoing expansion mode from genomic data.

bioRxiv : the preprint server for biology·2026
Same author

Scalable subclonal reconstruction of cancer cells in DNA sequencing data using a penalized likelihood model.

bioRxiv : the preprint server for biology·2026
Same author

NOTCH1 acts as a tumor suppressor that induces early differentiation in head and neck cancer.

JCI insight·2026
Same author

BESTish: A Diffusion-Approximation Framework for Inferring Selection and Mutation in Clonal Hematopoiesis.

bioRxiv : the preprint server for biology·2026
Same author

Subcutaneous Lenacapavir in People With Multidrug-Resistant HIV-1: 156 Week Results of the CAPELLA Study.

Open forum infectious diseases·2025
Same author

CanID: A Robust and Accurate RNA-seq Expression-based Diagnostic Classification Scheme for Pediatric Malignancies.

Genomics, proteomics & bioinformatics·2025
Same journal

COL1A1 and SERPINE1 as Potential Therapeutic Targets in Diabetic Retinopathy: A Study Incorporating RNA Transcriptomics, Single-Cell RNA Sequencing, and Proteomics.

Human mutation·2026
Same journal

Autosomal Dominant Missense <i>DAG1</i> Variant Linked to Mild-Moderate LGMD R16.

Human mutation·2026
Same journal

RETRACTION: "Differential Effects of AKT1(p.E17K) Expression on Human Mammary Luminal Epithelial and Myoepithelial Cells".

Human mutation·2026
Same journal

Diagnostic Yield of Genome Sequencing in an Iranian Exome-Negative Autosomal-Recessive Intellectual Disability Cohort.

Human mutation·2026
Same journal

Exploring the Functional Impact of Individual <i>DDX41</i> Variants With a Fast and Robust Cell-Based Method.

Human mutation·2026
Same journal

Modeling the Effects of Single Nucleotide Polymorphisms (SNPs) on the Structure and Function of the Human <i>RET</i> Gene: An In Silico Study.

Human mutation·2026
See all related articles

Related Experiment Video

Updated: Jun 2, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed.

Stephanie Hicks1, David A Wheeler, Sharon E Plon

  • 1Department of Statistics, Rice University, Houston, Texas, USA.

Human Mutation
|April 12, 2011
PubMed
Summary
This summary is machine-generated.

Predicting missense mutation effects requires careful algorithm and sequence alignment selection. Different alignments significantly impact variant predictions, highlighting the need for optimization in bioinformatics research.

More Related Videos

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

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

Related Experiment Videos

Last Updated: Jun 2, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

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

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Missense mutations can alter protein function, impacting cellular processes.
  • Several algorithms exist to predict mutation effects using sequence alignments.
  • The choice of alignment can influence prediction accuracy.

Purpose of the Study:

  • To compare the accuracy of SIFT, Align-GVGD, PolyPhen-2, and Xvar algorithms.
  • To evaluate the impact of different sequence alignments on these prediction algorithms.
  • To guide researchers in selecting optimal algorithms and alignments for missense mutation analysis.

Main Methods:

  • Compared four missense mutation prediction algorithms (SIFT, Align-GVGD, PolyPhen-2, Xvar).
  • Utilized well-characterized missense mutations (n=267) in BRCA1, MSH2, MLH1, and TP53 genes.
  • Assessed prediction accuracy using native and four distinct sequence alignments (SIFT, PolyPhen-2, Uniprot, manual).

Main Results:

  • All four algorithms achieved 78-79% accuracy (Area Under Curve) with their native alignments.
  • PolyPhen-2 predictions showed the least variation across different alignments.
  • Align-GVGD incorrectly predicted all variants as neutral when using alignments with numerous sequences.
  • Algorithm predictions varied even with identical alignments, and native alignments were not always optimal.

Conclusions:

  • Algorithm and sequence alignment choice critically affects missense mutation predictions.
  • Researchers should optimize both the prediction algorithm and the sequence alignment used.
  • This study provides insights for improving the reliability of variant effect predictions.