$\textsf{\textcolor{#0f766e}{Z}OMBI2}$

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Simulating the evolution of species, genomes, sequences and traits.

ZOMBI2 simulates evolution at four levels: the species tree of lineages, the genomes that evolve along it, the sequences inside each gene, and the traits a lineage carries. Use it to generate benchmark datasets with known ground truth for phylogenetic and comparative methods.

Install

pip install zombi2

Quickstart

zombi2 species   out/ --birth 1 --death 0.3 --n-extant 20 --seed 1
zombi2 genomes   out/ --duplication 0.2 --transfer 0.1 --loss 0.25 --seed 1
zombi2 sequences out/ --model hky85 --length 1000 --divergence 0.2 --seed 1

From Python, each level is one function, and the result object carries the history:

from zombi2 import species, genomes, sequences
from zombi2.sequences.substitution_models import hky85

sp = species.simulate_species_tree(birth=1.0, death=0.3, n_extant=20, seed=1)
g  = genomes.simulate_genomes_family(sp, duplication=0.2, transfer=0.1, loss=0.25, seed=1)
s  = sequences.simulate_sequences(g, model=hky85(), length=1000, divergence=0.2, seed=1)

g.gene_trees                    # the true gene tree of every family

sp.write("out/")                # the trees, the event log and the fates
g.write("out/")                 # gene trees, the event log, profiles
s.write("out/")                 # alignments and phylograms (ancestral is opt-in)

Each write fills the directory it is given, so these three share out/; the commands above group the same run into out/species/, out/genomes/ and out/sequences/, and add a log per level and a run.zombi2 report. The two routes count families differently, too: the Python result holds an alignment for every family born, empty where no copy survived, while the commands count the survivors. run.zombi2 gives both, as families 100 born · 92 surviving · 8 died out.

Levels

ZOMBI2 is organized around four levels of evolution. A genome, a sequence or a trait always evolves along a species tree, so you run whichever you need, composed into one seeded, reproducible run.

One simulated dataset at all four levels: a species tree with its extinct lineages, the gene order of every surviving genome with homologues linked, the alignment behind one gene family, and two traits drifting together

  • Species trees — a birth–death process with rates that can shift in time, saturate with diversity or drift down the tree, plus mass extinctions, incomplete sampling and fossils.
  • Genomes — gene families under duplication, transfer, loss and origination, at three resolutions: gene families, ordered chromosomes with rearrangements, and nucleotide genomes, with real DNA along each chromosome.
  • Sequences — nucleotide (JC69, K80, HKY85, GTR) and protein substitution models run down each gene tree, with ancestral sequences at every node.
  • Traits — continuous traits that diffuse, revert to an optimum or shift at speciation, and discrete traits switching between states.

The four levels of evolution ZOMBI2 simulates: the species tree forks into genomes and traits, and sequences continue below genomes

Conditioning

Conditioning is how ZOMBI2 simulates a scenario where one part of the run controls another — a habitat trait that makes lineages lose genes four times faster in the water, a gene family whose presence speeds up transfer for the rest of the genome, a GC content that sets how fast a trait changes. Three parts: the driver, the thing doing the controlling; the target, the parameter it controls (a rate, an extent, or which lineage receives a transfer); and the connection between them, which says both how they are joined (scaled_by) and what each value of the driver is worth.

Conditioning: a habitat trait on the left, an arrow labelled drives running right to the gene loss rate and carrying a multiplier for each habitat state, and under the loss rate the expression you write on it, a per-copy loss rate of 0.25 scaled by habitat 

zombi2 species out/ --birth 1 --death 0.3 --n-extant 20 --seed 1
zombi2 traits  out/ --kind discrete --states aquatic,terrestrial --switch 0.4 --seed 1
zombi2 genomes out/ --loss "PerCopy(0.25).scaled_by('out/traits/trait_events.tsv', {'aquatic': 4.0})" --seed 1

Joining

Joining is how ZOMBI2 simulates two levels at the same time, for the scenarios where neither can be grown first because each shapes the other. A trait that speeds up speciation is the standard case: lineages carrying it split more often, so the trait decides the shape of the tree while the tree decides where the trait can go. One run grows both, and the tree comes out as a result rather than going in as an input.

Joining, drawn as conditioning is: body size on the left, the speciation rate on the right, and two arrows between them — one carrying the multiplier each state hands over, one running back, because the tree that rate builds is the tree body size evolves along 

zombi2 joint out/ --birth "PerLineage(1.0).scaled_by('trait', {'small': 1.0, 'large': 3.0})" \
    --states small,large --switch 0.3 --n-extant 100 --seed 1

Performance

A species tree of a million leaves takes a few seconds. On the same gene-family task, ZOMBI2 runs about 183× faster than the legacy ZOMBI v1 — both pure Python.

ZOMBI2 performance overview: (a) species-tree simulation scaling to millions of tips; (b) genome simulation at the family, ordered and nucleotide resolutions; (c) ZOMBI2 about 183 times faster than the legacy ZOMBI v1 on one shared 1,000-tip tree

Gallery

The gallery is a page of worked examples, one figure each, with the code that produced it: species trees, genomes, sequences, traits, conditioning and joining. Every figure is drawn with Phylustrator.

Citation

A dedicated ZOMBI2 paper is in preparation. Until then, cite the original ZOMBI.

License

ZOMBI2 is released under the MIT License.

Maintainer, and contributing

ZOMBI2 is maintained and supervised by Adrián Arellano Davín.

Contributions are welcome, from humans and from agents alike — a bug report, a model you wish it could express, a fix, a worked example for the gallery. Start with CONTRIBUTING.md, and open an issue or a pull request.

Project links

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Credits

Author: Adrián A. Davin

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