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Spontaneous and Induced Mutations

Spontaneous and Induced Mutations

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).

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The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

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Related Experiment Video

Updated: Jun 27, 2026

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

Detecting clusters of mutations.

Tong Zhou¹, Peter J Enyeart, Claus O Wilke

¹Center for Computational Biology and Bioinformatics, Section of Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America.

|November 20, 2008

Summary

We developed a new algorithm to detect mutation clusters in protein evolution, accounting for protein structure. This method improves accuracy and identifies functionally important divergence, revealing clustered evolution is rare.

Area of Science:

Evolutionary biology
Genomics
Structural bioinformatics

Background:

Positive selection can drive multiple mutations in localized DNA regions, forming mutation clusters.
Existing methods for detecting mutation clusters do not account for varying residue solvent accessibility.
Residue solvent accessibility influences inherent variability, impacting mutation detection accuracy.

Purpose of the Study:

To develop a novel algorithm for detecting clustered evolution in protein sequences.
To account for differential substitution probabilities based on residue solvent accessibility (buried vs. exposed).
To identify functionally relevant divergence and potential artifacts in genomic data.

Main Methods:

Proposed a new algorithm incorporating solvent accessibility into mutation cluster detection.

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Last Updated: Jun 27, 2026

Characterizing Mutational Load and Clonal Composition of Human Blood

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Published on: July 11, 2019

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Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

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Published on: September 25, 2018

Utilized random permutations to calculate accurate P-values for inferred mutation clusters.

Applied the algorithm to bacterial, fly, and mammalian genomes.

Main Results:

Identified several mutation clusters in functionally important protein regions across diverse species.
Demonstrated that clustered evolution is a rare phenomenon, found in only 2-10% of analyzed genes.
Showed that ignoring solvent accessibility leads to an overestimation of clusters in exposed regions.

Conclusions:

The novel algorithm accurately detects mutation clusters by controlling for solvent accessibility.
Clustered evolution is infrequent, suggesting specific constraints on protein functional divergence.
The method enhances the identification of functionally significant evolutionary patterns and genomic assembly artifacts.