fastsimcoal2: demographic inference under complex evolutionary scenarios-Reference-Cited by-同舟云学术

fastsimcoal2: demographic inference under complex evolutionary scenarios

Published:2021-06-23 Issue:24 Volume:37 Page:4882-4885
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Excoffier Laurent¹²^ORCID,Marchi Nina¹²,Marques David Alexander³⁴⁵,Matthey-Doret Remi¹²,Gouy Alexandre¹⁶,Sousa Vitor C¹⁷

Affiliation:

1. Computational and Molecular Population Genetics Lab, Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland

2. Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland

3. Life Science Division, Natural History Museum Basel, 4051 Basel, Switzerland

4. Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland

5. Department of Fish Ecology and Evolution, EAWAG Swiss Federal Institute of Aquatic Science and Technology, Center for Ecology, Evolution and Biogeochemistry, 6047 Kastanienbaum, Switzerland

6. Gouy Data Consulting, 1026 Denges, Switzerland

7. cE3c—Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências da Universidade de Lisboa, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal

Abstract

Abstract Motivation fastsimcoal2 extends fastsimcoal, a continuous time coalescent-based genetic simulation program, by enabling the estimation of demographic parameters under very complex scenarios from the site frequency spectrum under a maximum-likelihood framework. Results Other improvements include multi-threading, handling of population inbreeding, extended input file syntax facilitating the description of complex demographic scenarios, and more efficient simulations of sparsely structured populations and of large chromosomes. Availability and implementation fastsimcoal2 is freely available on http://cmpg.unibe.ch/software/fastsimcoal2/. It includes console versions for Linux, Windows and MacOS, additional scripts for the analysis and visualization of simulated and estimated scenarios, as well as a detailed documentation and ready-to-use examples.

Funder

Swiss National Science Foundation

Portuguese National Science Foundation

Fundação para a Ciência e a Tecnologia

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab468/38924187/btab468.pdf

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