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fastsimcoal website  

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, SNP, STR (microsatellite) 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.

Environment Modules

Run module spider fastsimcoal to find out what environment modules are available for this application.

System Variables

  • HPC_FASTSIMCOAL_DIR - installation directory
  • HPC_FASTSIMCOAL_BIN - executable directory


If you publish research that uses fastsimcoal you have to cite it as follows:

Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V.C., and M. Foll (2013) Robust demographic inference from genomic and SNP data. PLOS Genetics, 9(10):e1003905.