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Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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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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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...

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Related Experiment Video

Updated: Aug 18, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

bmVAE: a variational autoencoder method for clustering single-cell mutation data.

Jiaqian Yan¹, Ming Ma¹, Zhenhua Yu^1,2

¹School of Information Engineering, Ningxia University, Yinchuan 750021, China.

Bioinformatics (Oxford, England)

|December 8, 2022

Summary

This summary is machine-generated.

We developed bmVAE, a bioinformatics tool to identify tumor clones from single-cell mutation data. This method effectively unmasks intra-tumor heterogeneity (ITH) for personalized cancer therapy.

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Last Updated: Aug 18, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

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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

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genetic intra-tumor heterogeneity (ITH) is crucial for personalized cancer therapy.
Single-cell DNA sequencing aids in ITH analysis but faces challenges with error-prone data.

Purpose of the Study:

Introduce bmVAE, a bioinformatics tool to address challenges in detecting tumor clones from single-cell mutation data.
Enable accurate inference of ITH for improved cancer treatment strategies.

Main Methods:

bmVAE utilizes a variational autoencoder for dimensionality reduction of single-cell mutation data.
The framework integrates cell clustering and genotype estimation modules.
Input is single-cell binary mutation data; output includes cell subpopulations and their genotypes.

Main Results:

bmVAE was evaluated on synthetic datasets with varying error rates and data sizes.
The tool demonstrated high effectiveness in inferring ITH on real-world datasets.
Performance was competitive with existing methods for tumor clone detection.

Conclusions:

bmVAE provides a robust approach for analyzing intra-tumor heterogeneity from single-cell mutation data.
The tool facilitates a deeper understanding of tumor evolution and aids in personalized medicine.
bmVAE is freely available for research use.