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Title
Nature in silico : population genetic simulation and its evolutionary interpretation using C++ and R / Ryan J. Haasl.
ISBN
9783030973810 (electronic bk.)
3030973816 (electronic bk.)
9783030973803
3030973808
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource (xviii, 313 pages) : illustrations
Item Number
10.1007/978-3-030-97381-0 doi
Call Number
QH455
Dewey Decimal Classification
576.5/8
Summary
Dramatic advances in computing power enable simulation of DNA sequences generated by complex microevolutionary scenarios that include mutation, population structure, natural selection, meiotic recombination, demographic change, and explicit spatial geographies. Although retrospective, coalescent simulation is computationally efficient--and covered here--the primary focus of this book is forward-in-time simulation, which frees us to simulate a wider variety of realistic microevolutionary models. The book walks the reader through the development of a forward-in-time evolutionary simulator dubbed FORward Time simUlatioN Application (FORTUNA). The capacity of FORTUNA grows with each chapter through the addition of a new evolutionary factor to its code. Each chapter also reviews the relevant theory and links simulation results to key evolutionary insights. The book addresses visualization of results through development of R code and reference to more than 100 figures. All code discussed in the book is freely available, which the reader may use directly or modify to better suit his or her own research needs. Advanced undergraduate students, graduate students, and professional researchers will all benefit from this introduction to the increasingly important skill of population genetic simulation. .
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed September 19, 2022).
Introduction and relevance
Retrospective and prospective simulation
Data structures and computational efficiency
Mutation
Population size and genetic drift
Migration and population structure
Meiotic recombination
Natural selection
Implementing all five factors simultaneously
Modeling different life histories
Spatially-explicit simulation
Calculating summary statistics and visualization
Approximate Bayesian computation: preliminaries
Approximate Bayesian computation: implementation
Comparing simulated genetic data to 1000 Genomes data
The spread of the invasive species Japanese hops in the Upper Midwest, USA.
