Interface |
Separation of ancestry and mutation simulations. Ability to store arbitrary metadata along with simulation results, and automatic recording of provenance information for reproducibility. Jupyter Notebook (Kluyver et al. 2016) integration. Rich suite of analytical and visualization methods via the tskit library. |
Ancestry |
SMC, SMC’, Beta- and Dirac-coalescent, discrete time Wright–Fisher, and selective sweep models. Instantaneous bottlenecks. Discrete or continuous genomic coordinates, arbitrary ploidy, gene conversion. Output full ARG with recombination nodes, ARG likelihood calculations. Record full migration history and census events. Improved performance for large numbers of populations. Integration with forward simulators such as SLiM and fwdpy11 (“recapitation”). |
Demography |
Improved interface with integrated metadata and referencing populations by name. Import from Newick species tree, *BEAST (Heled and Drummond 2010), and Demes (Gower et al. 2022). Numerical methods to compute coalescence rates. |
Mutations |
JC69, HKY, F84, GTR, BLOSUM62, PAM, infinite alleles, SLiM and general matrix mutation models. Varying rates along the genome, recurrent/back mutations, discrete or continuous genomic coordinates, overlaying multiple layers of mutations, exact times associated with mutations. |