GSR: Simulator - phastSim
Attribute | Value |
---|---|
Title | phastSim |
Short Description | phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets |
Long Description | We present phastSim, 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. |
Keywords | large datasets, sequence evolution, genomic epidemiology, Gillespie algorithm, |
Version | 0.0.4 |
Project Started | 2020 |
Last Release | 3 years, 4 months ago |
Homepage | https://github.com/NicolaDM/phastSim |
Citations | De Maio N, Boulton W, Weilguny L, Walker CR, Turakhia Y, Corbett-Detig R, Goldman N, phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets., PLoS Comput Biol, April 29, 2022 [ Abstract, cited in PMC ] |
GSR Certification | ![]() ✔ Accessibility |
Last evaluated | Aug. 5, 2022 (988 days ago) |
Author verification | The basic description provided was derived from a website or publications by the GSR team and has not yet been verified by the simulation author. To modify this entry or add more information, propose changes to this simulator. |
Attribute Category | Attribute |
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Target | |
Type of Simulated Data | |
Variations | |
Simulation Method | Other, |
Input | |
Data Type | |
File format | Other, |
Output | |
Data Type | Genotype or Sequence, |
Sequencing Reads | |
File Format | Fasta or Fastq, Phylip, |
Sample Type | |
Phenotype | |
Trait Type | |
Determinants | |
Evolutionary Features | |
Demographic | |
Population Size Changes | |
Gene Flow | |
Spatiality | |
Life Cycle | |
Mating System | |
Fecundity | |
Natural Selection | |
Determinant | |
Models | |
Recombination | |
Mutation Models | Codon and Amino Acid Models, |
Events Allowed | |
Other | |
Interface | |
Development | |
Tested Platforms | |
Language | Python, |
License | GNU Public License, |
GSR Certification | Accessibility, Documentation, Application, Support, |
Number of Primary Citations: 1
Number of Non-Primary Citations: 2
The following 2 publications are selected examples of applications that used phastSim.
2022
Thornlow B, Kramer A, Ye C, De Maio N, McBroome J, Hinrichs AS, Lanfear R, Turakhia Y, Corbett-Detig R, Online Phylogenetics using Parsimony Produces Slightly Better Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than <i>de novo</i> and Maximum-Likelihood Approaches., bioRxiv, May 18, 2022 [Abstract]
McBroome J, Martin J, de Bernardi Schneider A, Turakhia Y, Corbett-Detig R, Identifying SARS-CoV-2 regional introductions and transmission clusters in real time., Virus Evol, June 16, 2022 [Abstract]