Abstract
Accurate simulation of complex biological processes is an essential component of developing and validating new technologies and inference approaches. As an effort to help contain the COVID-19 pandemic, large numbers of SARS-CoV-2 genomes have been sequenced from most regions in the world. More than 5.5 million viral sequences are publicly available as of November 2021. Many studies estimate viral genealogies from these sequences, as these can provide valuable information about the spread of the pandemic across time and space. Additionally such data are a rich source of information about molecular evolutionary processes including natural selection, for example allowing the identification of new variants with transmissibility and immunity evasion advantages. To our knowledge, there is no framework that is both efficient and flexible enough to simulate the pandemic to approximate world-scale scenarios and generate viral genealogies of millions of samples. Here, we introduce a new fast simulator VGsim which addresses the problem of simulation genealogies under epidemiological models. The simulation process is split into two phases. During the forward run the algorithm generates a chain of population-level events reflecting the dynamics of the pandemic using an hierarchical version of the Gillespie algorithm. During the backward run a coalescent-like approach generates a tree genealogy of samples conditioning on the population-level events chain generated during the forward run. Our software can model complex population structure, epistasis and immunity escape. The code is freely available at https://github.com/Genomics-HSE/VGsim.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
VSh, VSp, DS, RCD were funded within the framework of the HSE University Basic Research Program. EB acknowledges support within the Project Teams framework of MIEM HSE. VSh was supported by RFBR grant 20-04-60556 while working on section 2.5. This research was supported in part through computational resources of HPC facilities at NRU HSE. RCD was supported in part by NIH/NIGMS R35GM128932. NDM was supported by the European Molecular Biology Laboratory.
Author Declarations
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No IRB approval is necessary. No data were used in the work which relies exclusively on simulation.
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Footnotes
Heavily revised version.
Data Availability
There is no data and reagent used in the paper. The code is available at the GitHub repository associated with this project: https://github.com/Genomics-HSE/VGsim.