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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, 5 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 (1112 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 |
|---|---|
| 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]