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dc.contributor.authorDe Maio, Nicola
dc.contributor.authorBoulton, William
dc.contributor.authorWeilguny, Lukas
dc.contributor.authorWalker, Conor R
dc.contributor.authorTurakhia, Yatish
dc.contributor.authorCorbett-Detig, Russell
dc.contributor.authorGoldman, Nick
dc.date.accessioned2022-05-11T20:00:11Z
dc.date.available2022-05-11T20:00:11Z
dc.date.issued2022-04
dc.date.submitted2021-09-24
dc.identifier.issn1553-734X
dc.identifier.otherpcompbiol-d-21-01738
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337042
dc.descriptionFunder: European Molecular Biology Laboratory; funder-id: http://dx.doi.org/10.13039/100013060
dc.descriptionFunder: Schmidt Futures Foundation
dc.description.abstractSequence 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.
dc.languageen
dc.publisherPublic Library of Science
dc.subjectResearch Article
dc.subjectBiology and life sciences
dc.subjectComputer and information sciences
dc.subjectEngineering and technology
dc.subjectMedicine and health sciences
dc.subjectResearch and analysis methods
dc.titlephastSim: Efficient simulation of sequence evolution for pandemic-scale datasets.
dc.typeArticle
dc.date.updated2022-05-11T20:00:11Z
prism.issueIdentifier4
prism.publicationNamePLoS Comput Biol
prism.volume18
dc.identifier.doi10.17863/CAM.84465
dcterms.dateAccepted2022-03-25
rioxxterms.versionofrecord10.1371/journal.pcbi.1010056
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Mustonen, Ville
dc.contributor.orcidDe Maio, Nicola [0000-0002-1776-8564]
dc.contributor.orcidBoulton, William [0000-0002-8258-4673]
dc.contributor.orcidWeilguny, Lukas [0000-0001-6459-0431]
dc.contributor.orcidWalker, Conor R [0000-0001-5617-5086]
dc.contributor.orcidCorbett-Detig, Russell [0000-0001-6535-2478]
dc.contributor.orcidGoldman, Nick [0000-0001-8486-2211]
dc.identifier.eissn1553-7358
pubs.funder-project-idNational Institute of Health Research (IS-BRC-1215- 20014)
pubs.funder-project-idAlfred P. Sloan Foundation (R35GM128932)
pubs.funder-project-idNational Institutes of Health (R35GM128932)


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