Metapop is a stochastic individual-based simulation program. It uses quantitative genetics theory to produce an explicit description of the typical life cycle of monoecious and hermaphroditic plant species. Genome structure, the relationship between genotype and phenotype, and the effects of landscape heterogeneity on each individual can be finely parameterized by the user. Unlike most existing simulation packages, Metapop can simulate phenotypic plasticity, which may have a genetic component, and assortative mating, two important features of tree species. Each simulation is parameterized through text files, and raw data are generated recurrently, describing the allelic state of each quantitative trait locus involved in phenotypic variability. The data can be generated in Genepop or Fstat format, and may thus be analysed with other existing packages. Metapop also automatically computes a range of populations statistics, enabling the user to monitor evolutionary dynamics directly, from gene to metapopulation level.
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http://dx.doi.org/10.1111/1755-0998.12958 | DOI Listing |
Microbiome
March 2022
Department of Microbiology, Ohio State University, Columbus, OH, 43210, USA.
Background: Microbes and their viruses are hidden engines driving Earth's ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter-(macrodiversity) and intra-(microdiversity) population variation, analytical tools to do so remain scattered or under-developed.
Results: Here, we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro- and microdiversity levels.
Mol Ecol Resour
January 2019
BIOGECO, INRA, Univ. Bordeaux, Cestas, France.
Metapop is a stochastic individual-based simulation program. It uses quantitative genetics theory to produce an explicit description of the typical life cycle of monoecious and hermaphroditic plant species. Genome structure, the relationship between genotype and phenotype, and the effects of landscape heterogeneity on each individual can be finely parameterized by the user.
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