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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).

Cancers Originate from Somatic Mutations in a Single Cell

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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

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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.
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Mutations

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Related Experiment Video

Updated: Oct 1, 2025

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

SomaticSiMu: a mutational signature simulator.

David Chen¹, Gurjit S Randhawa², Maximillian P M Soltysiak¹

¹Department of Biology, Western University, London, ON N6A 5B7, Canada.

Bioinformatics (Oxford, England)

|March 8, 2022

Summary

This summary is machine-generated.

SomaticSiMu is a novel in silico tool that simulates DNA mutations to mimic mutational signatures. This simulator aids in testing genomic classification and mutation extraction tools, providing reliable ground truth data.

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Characterizing Mutational Load and Clonal Composition of Human Blood

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Last Updated: Oct 1, 2025

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

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Published on: October 18, 2013

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Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Understanding cancer genomes requires accurate identification of mutational signatures.
Existing tools for simulating mutations lack user-friendly interfaces and comprehensive control over mutation parameters.

Purpose of the Study:

To introduce SomaticSiMu, a graphical user interface-based in silico simulator for generating DNA sequences with specific mutational signatures.
To provide a reliable ground truth dataset for evaluating the performance of DNA sequence classification and mutational signature extraction tools.

Main Methods:

SomaticSiMu simulates single/double base substitutions and single base insertions/deletions in genomic sequences.
The tool offers control over mutation rates and includes visualization tools for simulated mutations.
Simulated datasets were validated using supervised machine learning for classification and mutational signature extraction.

Main Results:

SomaticSiMu successfully generated simulated DNA sequences and mutational catalogues reflecting imposed mutational signatures.
The tool's reliability was confirmed through accurate classification of simulated sequences and extraction of mutational signatures.
The graphical user interface and control over mutation rates enhance usability for researchers.

Conclusions:

SomaticSiMu is the first mutational signature simulator with a GUI, offering researchers a powerful tool for generating realistic simulated genomic data.
The validated simulations provide a crucial resource for benchmarking and improving computational tools in cancer genomics.
The open-source availability of SomaticSiMu facilitates its adoption and further development within the research community.