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Updated: Jan 22, 2026

Selective Capture of 5-hydroxymethylcytosine from Genomic DNA

Selective Capture of 5-hydroxymethylcytosine from Genomic DNA

Published on: October 5, 2012

Selection Acting on Genomes.

Carolin Kosiol^1,2, Maria Anisimova^3,4

¹Centre of Biological Diversity, School of Biology, University of St Andrews, Fife, UK. ck202@st-andrews.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)

|July 7, 2019

Summary

This summary is machine-generated.

New mutations can drive rapid genome evolution, especially in immunity genes, through positive selection. This study explores methods for detecting positive selection across genomes using probabilistic models and new genome-wide data.

Keywords:

Codon models Conserved and accelerated regions Polymorphism-aware phylogenetic models Positive selection scans Selection-mutation models

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Published on: October 5, 2012

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Published on: November 14, 2018

Area of Science:

Evolutionary biology
Genomics
Population genetics

Background:

Organismal populations evolve through mutations that can become fixed via random drift or positive selection.
Positive selection favors beneficial mutations, leading to rapid genome evolution, particularly in genes related to immunity and defense.
Genome-wide scans for selection have significantly advanced our understanding of species' evolutionary trajectories.

Purpose of the Study:

To review and discuss methods for detecting selection acting on genomes.
To highlight the role of probabilistic models in identifying genomic regions under selection.
To examine how advancements in genome-wide data have impacted these detection methods.

Main Methods:

Focus on probabilistic models for detecting selection.
Discussion of how these models are adapted for new genome-wide data.
Exploration of genome-wide scans as a tool for evolutionary inference.

Main Results:

Regions of the genome experiencing positive selection evolve more rapidly.
Immunity and defense genes are frequently cited examples of rapid evolution driven by selection.
Modern genome-wide data enables more powerful detection of selection across the entire genome.

Conclusions:

Understanding the mechanisms of selection is crucial for comprehending genome evolution.
Probabilistic models, enhanced by genome-wide data, are key to identifying evolutionary processes like positive selection.
The study of selection provides insights into adaptation and the "arms race" between species and their pathogens.