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Mutations

Mutations

Mutations

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 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...

Mismatch Repair

Mismatch Repair

Mutations in Microorganisms

Mutations in Microorganisms

Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...

Principles of Pharmacogenetics: Types of Genetic Variants

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Related Experiment Video

Updated: Feb 22, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Understanding mutational effects in digenic diseases.

Andrea Gazzo^1,2,3, Daniele Raimondi^1,2,4, Dorien Daneels^1,3,5

¹Interuniversity Institute for Bioinformatics in Brussels, ULB-VUB, Boulevard du Triomphe CP 263, 1050 Brussels, Belgium.

Nucleic Acids Research

|September 16, 2017

Summary

This summary is machine-generated.

Understanding rare diseases requires studying gene variant combinations. This study differentiates between true digenic and composite digenic effects, improving our grasp of genetic heterogeneity in disease.

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Last Updated: Feb 22, 2026

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Published on: August 24, 2013

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Area of Science:

Genetics
Rare Diseases
Computational Biology

Background:

Rare diseases often stem from complex genetic interactions, not single gene mutations.
Understanding digenic effects (two gene variants) is crucial for deciphering genetic heterogeneity.
A new resource enables the study of digenic disease phenotypes.

Purpose of the Study:

To differentiate between true digenic and composite digenic disease classes.
To identify features that distinguish these digenic effect classes.
To analyze digenic disease profiles for insights into disease mechanisms.

Main Methods:

Utilized a resource of digenic disease instances classified as true digenic or composite.
Employed a machine learning approach combining variant, gene, and higher-level features for classification.
Analyzed decision profiles from a predictive model for three digenic disorders.

Main Results:

High accuracy achieved in differentiating true digenic from composite digenic classes using combined features.
Demonstrated that variant, gene, and higher-level features are effective predictors.
Digenic effect decision profiles provided explanations for classification outcomes.

Conclusions:

Digenic disease data offers valuable insights into the genetic basis of rare diseases.
The study advances our understanding of oligogenic inheritance patterns.
Predictive modeling can elucidate the specific contributions of gene variants in complex diseases.