fastsimcoal pipeline

fastsimcoal specifications


Unique identifier OMICS_06854
Name fastsimcoal
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++
Computer skills Advanced
Stability Stable
Maintained Yes


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Publications for fastsimcoal

fastsimcoal IN pipelines

PMCID: 5389967
PMID: 28450881
DOI: 10.3389/fgene.2017.00044

[…] divergence between the two species was fixed at 1.34 mya (tollis et al., 2012). parameters were estimated from the joint allele frequency spectrum (sfs) using the likelihood approach implemented in fastsimcoal2.5 (excoffier et al., 2013). parameters with the highest likelihood were obtained after 40 cycles of the algorithm, starting with 50,000 coalescent simulations per cycle, and ending […]

PMCID: 5389967
PMID: 28450881
DOI: 10.3389/fgene.2017.00044

[…] model for the same number of snps as in the original dataset. we performed parameter estimation for 150 of these pseudo-observed datasets to infer ci. coalescence simulations were performed using fastsimcoal2.5 (excoffier and foll, 2011). we further checked whether our model fit the observed data by sampling parameters from the 95% ci range for 10,000 simulations and comparing observed […]

PMCID: 5389967
PMID: 28450881
DOI: 10.3389/fgene.2017.00044

[…] in the r package abc (csillery et al., 2012)]., to estimate if the line sfs deviated significantly from neutral expectations, we simulated for each family the derived allele frequency spectrum in fastsimcoal2.5. parameters were sampled from the ci obtained for snps. we performed 5,000 simulations for each dataset, assuming unlinked line insertion sites, and obtained p-values […]

PMCID: 4928843
PMID: 27362536
DOI: 10.1371/journal.pgen.1006157

[…] may not detect admixture events that happened before the split of the sympatric species. to overcome these limitations and furthermore infer the demographic history of the radiations we used fastsimcoal2 to perform coalescent simulations in pre-defined models and evaluated their fit against our empirical data summarized in the multi-dimensional site frequency spectrum (sfs) [66, 67]. […]

fastsimcoal institution(s)
Institute of Ecology and Evolution, University of Berne, Berne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
fastsimcoal funding source(s)
Swiss SNF (grant no. 3100A0-126074)

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