Open AccessEfficient ancestry and mutation simulation with msprime 1.0
Author(s) -
Franz Baumdicker,
Gertjan Bisschop,
Daniel Goldstein,
Graham Gower,
Aaron P. Ragsdale,
Georgia Tsambos,
Sha Zhu,
Bjarki Eldon,
E. Castedo Ellerman,
Jared Galloway,
Ariella Gladstein,
Gregor Gorjanc,
Bing Guo,
Ben Jeffery,
Warren W. Kretzschmar,
Konrad Lohse,
Michael Matschiner,
Dominic Nelson,
Nathaniel S. Pope,
Consuelo D. Quinto-Cortés,
Murillo F. Rodrigues,
Kumar Saunack,
Thibaut Sellinger,
Kevin R. Thornton,
Hugo van Kemenade,
Anthony Wilder Wohns,
Yan Wong,
Simon Gravel,
Andrew D. Kern,
Jere Koskela,
Peter L. Ralph,
Jerome Kelleher
Publication year - 2021
Publication title -
genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.792
H-Index - 246
eISSN - 1943-2631
pISSN - 0016-6731
DOI - 10.1093/genetics/iyab229
Subject(s) - mutation , computer science , key (lock) , biology , quality (philosophy) , tree (set theory) , software , gene duplication , population , sequence (biology) , computational biology , genetics , programming language , gene , mathematics , epistemology , sociology , mathematical analysis , philosophy , demography , computer security
Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.