GSR: Simulator - fastsimcoal2

Basic Package Attributes
AttributeValue
Title fastsimcoal2
Short Description A continuous-­‐time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios
Long Description While preserving all the simulation flexibility of simcoal2, fastsimcoal is now implemented under a faster continous-time sequential Markovian coalescent approximation, allowing it to efficiently generate genetic diversity for different types of markers along large genomic regions, for both present or ancient samples. It includes a parameter sampler allowing its integration into Bayesian or likelihood parameter estimation procedure. fastsimcoal can handle very complex evolutionary scenarios including an arbitrary migration matrix between samples, historical events allowing for population resize, population fusion and fission, admixture events, changes in migration matrix, or changes in population growth rates. The time of sampling can be specified independently for each sample, allowing for serial sampling in the same or in different populations. Different markers, such as DNA sequences, SNPs, STRs (microsatellites) or multi-locus allelic data can be generated under a variety of mutation models (e.g. finite- and infinite-site models for DNA sequences, stepwise or generalized stepwise mutation model for STRs data, infinite-allele model for standard multi-allelic data). fastsimcoal can simulate data in genomic regions with arbitrary recombination rates, thus allowing for recombination hotspots of different intensities at any position. fastsimcoal implements a new approximation to the ancestral recombination graph in the form of sequential Markov coalescent allowing it to very quickly generate genetic diversity for >100 Mb genomic segments. fastsimcoal2 now allows one to estimate demographic parameters from the (joint) site frequency spectrum (SFS) using simulations to compute the expected SFS and a robust method for the maximization of the composite likelihood.
Version 2.7.0.9
Project Started 2011
Last Release 1 year, 6 months ago
Homepagehttp://cmpg.unibe.ch/software/fastsimcoal2/
Citations Excoffier L, Foll M, fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios., Bioinformatics, 05-01-2011 [ Abstract, cited in PMC ]
GSR CertificationGSR-certified

Accessibility
Documentation
Application
Support

Last evaluated11-10-2017 (2325 days ago)
Author verificationThe basic description provided was derived from a website or publications by the GSR team and has not yet been verified by the simulation author. To modify this entry or add more information, propose changes to this simulator.
Detailed Attributes
Attribute CategoryAttribute
Target
Type of Simulated DataHaploid DNA Sequence,
VariationsBiallelic Marker, Single Nucleotide Variation,
Simulation MethodStandard Coalescent,
Input
Data Type
File formatArlequin, NEXUS,
Output
Data TypeGenotype or Sequence,
Sequencing Reads
File FormatNEXUS, Other,
Sample Type
Phenotype
Trait Type
Determinants
Evolutionary Features
Demographic
Population Size ChangesConstant Size, Exponential Growth or Decline,
Gene FlowUser-defined Matrix,
Spatiality
Life Cycle
Mating SystemRandom Mating,
Fecundity
Natural Selection
Determinant
Models
RecombinationVarying Recombination Rates,
Mutation Modelsk-Allele Model, Stepwise Mutation Model, Heterogeneity among Sites,
Events AllowedVarying Demographic Features,
Other
InterfaceCommand-line, Script-based,
Development
Tested PlatformsWindows, Mac OS X, Linux and Unix,
LanguageC or C++,
LicenseGNU Public License,
GSR CertificationAccessibility, Documentation, Application, Support,

Number of Primary Citations: 1

Number of Non-Primary Citations: 2

The following 2 publications are selected examples of applications that used fastsimcoal2.

2023

Maas DL, Prost S, de Leeuw CA, Bi K, Smith LL, Purwanto P, Aji LP, Tapilatu RF, Gillespie RG, Becking LE, Sponge diversification in marine lakes: Implications for phylogeography and population genomic studies on sponges., Ecol Evol, 04-13-2023 [Abstract]

Gilabert A, Rieux A, Robert S, Vitalis R, Zapater MF, Abadie C, Carlier J, Ravigné V, Revisiting the historical scenario of a disease dissemination using genetic data and Approximate Bayesian Computation methodology: The case of Pseudocercospora fijiensis invasion in Africa., Ecol Evol, 04-19-2023 [Abstract]


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