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001449618 001__ 1449618
001449618 003__ OCoLC
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001449618 019__ $$a1343313142$$a1344161333
001449618 020__ $$a9783030973810$$q(electronic bk.)
001449618 020__ $$a3030973816$$q(electronic bk.)
001449618 020__ $$z9783030973803
001449618 020__ $$z3030973808
001449618 0247_ $$a10.1007/978-3-030-97381-0$$2doi
001449618 035__ $$aSP(OCoLC)1345056145
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001449618 049__ $$aISEA
001449618 050_4 $$aQH455
001449618 08204 $$a576.5/8$$223/eng/20220919
001449618 1001_ $$aHaasl, Ryan J.,$$eauthor.
001449618 24510 $$aNature in silico :$$bpopulation genetic simulation and its evolutionary interpretation using C++ and R /$$cRyan J. Haasl.
001449618 264_1 $$aCham :$$bSpringer,$$c[2022]
001449618 264_4 $$c©2022
001449618 300__ $$a1 online resource (xviii, 313 pages) :$$billustrations
001449618 336__ $$atext$$btxt$$2rdacontent
001449618 337__ $$acomputer$$bc$$2rdamedia
001449618 338__ $$aonline resource$$bcr$$2rdacarrier
001449618 504__ $$aIncludes bibliographical references.
001449618 5050_ $$aIntroduction and relevance -- Retrospective and prospective simulation -- Data structures and computational efficiency -- Mutation -- Population size and genetic drift -- Migration and population structure -- Meiotic recombination -- Natural selection -- Implementing all five factors simultaneously -- Modeling different life histories -- Spatially-explicit simulation -- Calculating summary statistics and visualization -- Approximate Bayesian computation: preliminaries -- Approximate Bayesian computation: implementation -- Comparing simulated genetic data to 1000 Genomes data -- The spread of the invasive species Japanese hops in the Upper Midwest, USA.
001449618 506__ $$aAccess limited to authorized users.
001449618 520__ $$aDramatic advances in computing power enable simulation of DNA sequences generated by complex microevolutionary scenarios that include mutation, population structure, natural selection, meiotic recombination, demographic change, and explicit spatial geographies. Although retrospective, coalescent simulation is computationally efficient--and covered here--the primary focus of this book is forward-in-time simulation, which frees us to simulate a wider variety of realistic microevolutionary models. The book walks the reader through the development of a forward-in-time evolutionary simulator dubbed FORward Time simUlatioN Application (FORTUNA). The capacity of FORTUNA grows with each chapter through the addition of a new evolutionary factor to its code. Each chapter also reviews the relevant theory and links simulation results to key evolutionary insights. The book addresses visualization of results through development of R code and reference to more than 100 figures. All code discussed in the book is freely available, which the reader may use directly or modify to better suit his or her own research needs. Advanced undergraduate students, graduate students, and professional researchers will all benefit from this introduction to the increasingly important skill of population genetic simulation. .
001449618 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 19, 2022).
001449618 650_0 $$aPopulation genetics$$xComputer simulation.
001449618 650_0 $$aEvolutionary genetics$$xComputer simulation.
001449618 655_0 $$aElectronic books.
001449618 77608 $$iPrint version:$$aHaasl, Ryan J.$$tNature in Silico$$dCham : Springer International Publishing AG,c2022$$z9783030973803
001449618 852__ $$bebk
001449618 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-97381-0$$zOnline Access$$91397441.1
001449618 909CO $$ooai:library.usi.edu:1449618$$pGLOBAL_SET
001449618 980__ $$aBIB
001449618 980__ $$aEBOOK
001449618 982__ $$aEbook
001449618 983__ $$aOnline
001449618 994__ $$a92$$bISE