期刊
VIRUSES-BASEL
卷 14, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/v14081648
关键词
phylogenetics; Bayesian inference; phylodynamics; population structure
类别
资金
- European Research Council [335529, 101001077]
- European Research Council (ERC) [101001077, 335529] Funding Source: European Research Council (ERC)
The multi-type birth-death model with sampling is an evolution dynamic model that quantifies past population dynamics in structured populations based on phylogenetic trees, implemented using the bdmm package. Important algorithmic changes to bdmm allows for the analysis of more genetic samples, improving numerical robustness and efficiency, leading to increased precision of parameter estimates, particularly for structured models with a high number of inferred parameters.
The multi-type birth-death model with sampling is a phylodynamic model which enables the quantification of past population dynamics in structured populations based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were computationally limited to trees consisting of up to approximately 250 genetic samples. We implemented important algorithmic changes to bdmm which dramatically increased the number of genetic samples that could be analyzed and which improved the numerical robustness and efficiency of the calculations. Including more samples led to the improved precision of parameter estimates, particularly for structured models with a high number of inferred parameters. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We applied this improved algorithm to two partly overlapping datasets of the Influenza A virus HA sequences sampled around the world-one with 500 samples and the other with only 175-for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset. In this way, we show the information that is gained by analyzing the bigger dataset, which became possible with the presented algorithmic changes to bdmm. In summary, bdmm allows for the robust, faster, and more general phylodynamic inference of larger datasets.
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