NOTICE: SAILFISH IS IN EARLY DEVELOPMENT AND SUBJECT TO CHANGE. API AND FEATURES MAY BE UNSTABLE. IT IS PROVIDED AS-IS WITHOUT ANY WARRANTY. USE AT YOUR OWN RISK. PLEASE REPORT ISSUES AND SUGGESTIONS ON GITHUB.
Sailfish
Sailfish is a high-performance multiple sequence alignment (MSA) simulator written in C++ with a Python API. It enables rapid generation of large-scale simulated datasets with support for indels, substitutions, and realistic evolutionary models.
Features
- High-performance C++ engine with ergonomic Python interface
- Support for DNA, protein, and codon sequence evolution
- Flexible indel modeling with multiple length distributions (Zipf, Geometric, Poisson, Custom)
- 26+ substitution models including JTT, WAG, LG, HKY, GTR, and more
- Gamma rate heterogeneity, invariant sites, and site rate correlation
- Per-branch parameter specification for heterogeneous models
- Low-memory mode for large-scale simulations (1M+ sequences)
- Reproducible simulations with explicit seed control
Installation
pip install msasim
Requirements: Python >= 3.7
Quick Start
Full simulation (indels + substitutions)
from msasim import SimProtocol, Simulator, ReplacementModelSpec
from msasim import MODEL_CODES, ALPHABET_CODES
from msasim.distributions import ZipfDistribution
protocol = SimProtocol(
tree="(A:0.5,B:0.5);",
root_seq_size=100,
deletion_rate=0.05,
insertion_rate=0.02,
deletion_dist=ZipfDistribution(1.7, 50),
insertion_dist=ZipfDistribution(1.7, 50),
seed=42,
)
rep_model = ReplacementModelSpec(model=MODEL_CODES.WAG, alphabet=ALPHABET_CODES.PROTEIN)
simulator = Simulator(protocol, replacement_model=rep_model)
msa = simulator()
msa.write_msa("output.fasta")
Indels only (no substitutions)
Omit the replacement_model argument:
from msasim import SimProtocol, Simulator
from msasim.distributions import ZipfDistribution
protocol = SimProtocol(
tree="(A:0.5,B:0.5);",
root_seq_size=100,
deletion_rate=0.05,
insertion_rate=0.02,
deletion_dist=ZipfDistribution(1.7, 50),
insertion_dist=ZipfDistribution(1.7, 50),
seed=42,
)
simulator = Simulator(protocol)
msa = simulator()
Substitutions only (no indels)
Set both rates to zero:
from msasim import SimProtocol, Simulator, ReplacementModelSpec
from msasim import MODEL_CODES, ALPHABET_CODES
protocol = SimProtocol(
tree="(A:0.5,B:0.5);",
root_seq_size=100,
insertion_rate=0.0,
deletion_rate=0.0,
seed=42,
)
rep_model = ReplacementModelSpec(model=MODEL_CODES.NUCJC, alphabet=ALPHABET_CODES.DNA)
simulator = Simulator(protocol, replacement_model=rep_model)
msa = simulator()
Gamma rate heterogeneity
from msasim import SimProtocol, Simulator, ReplacementModelSpec, SiteRateModelSpec
from msasim import MODEL_CODES, ALPHABET_CODES
protocol = SimProtocol(tree="(A:0.5,B:0.5);", root_seq_size=100, seed=42)
rate_model = SiteRateModelSpec(gamma_alpha=1.0, gamma_categories=4)
rep_model = ReplacementModelSpec(
model=MODEL_CODES.WAG,
alphabet=ALPHABET_CODES.PROTEIN,
site_rate_model=rate_model,
)
simulator = Simulator(protocol, replacement_model=rep_model)
msa = simulator()
Batch simulations
from msasim import SimProtocol, Simulator, ReplacementModelSpec
from msasim import MODEL_CODES, ALPHABET_CODES
protocol = SimProtocol(tree="tree.nwk", root_seq_size=500, seed=42)
rep_model = ReplacementModelSpec(model=MODEL_CODES.LG, alphabet=ALPHABET_CODES.PROTEIN)
simulator = Simulator(protocol, replacement_model=rep_model)
# Internal RNG advances deterministically across calls
for i in range(100):
msa = simulator()
msa.write_msa(f"replicate_{i:04d}.fasta")
Low-memory mode for large simulations
import pathlib
from msasim import SimProtocol, Simulator, ReplacementModelSpec
from msasim import MODEL_CODES, ALPHABET_CODES
protocol = SimProtocol(tree="large_tree.nwk", root_seq_size=10000, seed=42)
rep_model = ReplacementModelSpec(model=MODEL_CODES.NUCJC, alphabet=ALPHABET_CODES.DNA)
simulator = Simulator(protocol, replacement_model=rep_model)
simulator.simulate(output_path=pathlib.Path("large_alignment.fasta"))
# → writes large_alignment_replicate_1.fasta
Documentation
For the complete API reference see API_REFERENCE.md.
Core Concepts
Simulation type is inferred from the model
There is no simulation_type argument. The alphabet (DNA, PROTEIN, CODON) is taken from the ReplacementModelSpec. Passing no replacement_model runs an indels-only simulation.
Indel length distributions
ZipfDistribution(a, truncation)— power-law, typical for biological indelsGeometricDistribution(p, truncation)— exponentially decreasingPoissonDistribution(lambda, truncation)— Poisson-basedCustomDistribution(probs)— user-defined probability vector
Available substitution models
DNA: NUCJC, HKY, GTR, TAMURA92
Protein: WAG, LG, JONES (JTT), DAYHOFF, MTREV24, CPREV45, HIVB, HIVW, and more
Codon: EMPIRICODON, WYANG
See API_REFERENCE.md for the full list.
Project Goals
- Ease of use: Simple, intuitive Python API
- Speed: High-performance C++ implementation
- Modularity: Flexible configuration of all evolutionary parameters
Contributing
Bug reports and feature requests are welcome via GitHub issues.
Citation
If you use Sailfish in your research, please cite:
[Citation information to be added]
License
Academic Free License v3.0. See LICENSE for details.
1 maintainer
Metadata
1 maintainer
Release files for msasim 26.7.30
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