phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets.
PLoS Comput Biol
Public Library of Science
MetadataShow full item record
De Maio, N., Boulton, W., Weilguny, L., Walker, C. R., Turakhia, Y., Corbett-Detig, R., & Goldman, N. (2022). phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets.. PLoS Comput Biol, 18 (4) https://doi.org/10.1371/journal.pcbi.1010056
Funder: European Molecular Biology Laboratory; funder-id: http://dx.doi.org/10.13039/100013060
Funder: Schmidt Futures Foundation
Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, and are an essential component of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here, we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. > 100, 000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and it implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software allows easy integration with other Python packages as well as a variety of evolutionary models, including indel models and new hypermutability models that we developed to more realistically represent SARS-CoV-2 genome evolution.
Research Article, Biology and life sciences, Computer and information sciences, Engineering and technology, Medicine and health sciences, Research and analysis methods
National Institute of Health Research (IS-BRC-1215- 20014)
Alfred P. Sloan Foundation (R35GM128932)
National Institutes of Health (R35GM128932)
External DOI: https://doi.org/10.1371/journal.pcbi.1010056
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337042