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  Improved multi-type birth-death phylodynamic inference in BEAST 2

Scire, J., Barido-Sottani, J., Kühnert, D., Vaughan, T. G., & Stadler, T. (2020). Improved multi-type birth-death phylodynamic inference in BEAST 2. bioRxiv, 895532. doi:10.1101/2020.01.06.895532.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-76AE-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-76AF-2
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 Creators:
Scire, Jérémie, Author
Barido-Sottani, Joëlle, Author
Kühnert, Denise1, Author              
Vaughan, Timothy G., Author
Stadler, Tanja, Author
Affiliations:
1tide, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2591691              

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Free keywords: phylogenetics, Bayesian inference, phylodynamics, population structure
 Abstract: The multi-type birth-death model with sampling is a phylodynamic model which enables 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 limited to trees consisting of up to approximately 250 genetic samples for numerical reasons. We implemented important algorithmic changes to bdmm which dramatically increase the number of genetic samples that can be analyzed, and improve the numerical robustness and efficiency of the calculations. Being able to use bigger datasets leads to improved precision of parameter estimates. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We apply this improved algorithm to two partly overlapping datasets of Influenza A virus HA sequences sampled around the world, one with 500 samples, the other with only 175, for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset.Availability The latest release with our updates, bdmm 0.3.5, is freely available as an open access package of BEAST 2. The source code can be accessed at https://github.com/denisekuehnert/bdmm.

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Language(s): eng - English
 Dates: 2020-01-06
 Publication Status: Published online
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Method: No review
 Identifiers: DOI: 10.1101/2020.01.06.895532
Other: shh2491
 Degree: -

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Title: bioRxiv
Source Genre: Journal
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Publ. Info: Cold Spring Harbor : Cold Spring Harbor Laboratory
Pages: - Volume / Issue: - Sequence Number: 895532 Start / End Page: - Identifier: URN: https://www.biorxiv.org/