Software

tskit-dev projects

tskit
image/svg+xml Documentation tskit-dev/tskit

Population-scale genomics

ts = tskit.load("example.trees")
afs = ts.allele_frequency_spectrum()
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msprime
image/svg+xml Documentation Publication tskit-dev/msprime

Simulate genealogical trees and genomic sequence data using population genetic models

ts = msprime.sim_ancestry(
    samples=10, 
    recombination_rate=1e-4, 
    sequence_length=1e6
)
ts = msprime.sim_mutations(ts, rate=1e-6)
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tsinfer
image/svg+xml Documentation Publication tskit-dev/tsinfer

Infer a tree sequence from genetic variation data.

sample_data = tsinfer.load(
    "phased_sequence_data.samples"
)
ts = tsinfer.infer(sample_data)
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tsdate
image/svg+xml Documentation Publication tskit-dev/tsdate

Infer the age of ancestral nodes in a tree sequence.

dated_ts = tsdate.date(inferred_ts)
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pyslim
image/svg+xml Documentation tskit-dev/pyslim

Tools for dealing with tree sequences coming to and from SLiM.

rts = pyslim.recapitate(
    ts, 
    ancestral_Ne=10000
)
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tskit-rust
image/svg+xml Documentation tskit-dev/tskit-rust

Rust bindings for the tskit library

let treeseq = tskit::TreeSequence::load(
  &treefile
).unwrap();
let mut tree_iterator = 
  treeseq.tree_iterator(
    tskit::TreeFlags::default()
  ).unwrap();
while let Some(tree) = 
  tree_iterator.next() {
    ...
  }
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Ecosystem projects

SLiM SLiM
image/svg+xml Documentation Publication MesserLab/SLiM

SLiM is a genetically explicit forward simulation software package for population genetics and evolutionary biology. It is highly flexible, with a built-in scripting language, and has a cross-platform graphical modeling environment called SLiMgui.

initialize() {
    initializeTreeSeq();
    initializeMutationRate(1e-8);
    initializeMutationType(
        "m1", 0.5, "e", 0.001
    );
    initializeRecombinationRate(1e-8);
}
1 early() { sim.addSubpop("p1", 500); }
2000 late() { 
    sim.treeSeqOutput("out.trees");
}
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fwdpy11
image/svg+xml Documentation molpopgen/fwdpy11

Forward-time simulation in Python using fwdpp

pop = fwdpy11.DiploidPopulation(
  100, 1000.0
)
p = {
  "nregions": [],
  "rates": (0.0, 1e-3, None),
  "prune_selected": False,
  "demography": 
    fwdpy11.DiscreteDemography(),
  "simlen": 10 * pop.N + 200,
  ...
}
params = fwdpy11.ModelParams(**p)
fwdpy11.evolvets(rng, pop, params)
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To add a project here, please open an issue on the website GitHub repository.