PIQMEE: Bayesian phylodynamic method for analysis of large datasets with duplicate sequences
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Date
2020-10
Publication Type
Journal Article
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yes
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Abstract
Next-generation sequencing of pathogen quasispecies within a host yields data sets of tens to hundreds of unique sequences. However, the full data set often contains thousands of sequences, because many of those unique sequences have multiple identical copies. Data sets of this size represent a computational challenge for currently available Bayesian phylogenetic and phylodynamic methods. Through simulations, we explore how large data sets with duplicate sequences affect the speed and accuracy of phylogenetic and phylodynamic analysis within BEAST 2. We show that using unique sequences only leads to biases, and using a random subset of sequences yields imprecise parameter estimates. To overcome these shortcomings, we introduce PIQMEE, a BEAST 2 add-on that produces reliable parameter estimates from full data sets with increased computational efficiency as compared with the currently available methods within BEAST 2. The principle behind PIQMEE is to resolve the tree structure of the unique sequences only, while simultaneously estimating the branching times of the duplicate sequences. Distinguishing between unique and duplicate sequences allows our method to perform well even for very large data sets. Although the classic method converges poorly for data sets of 6,000 sequences when allowed to run for 7 days, our method converges in slightly more than 1 day. In fact, PIQMEE can handle data sets of around 21,000 sequences with 20 unique sequences in 14 days. Finally, we apply the method to a real, within-host HIV sequencing data set with several thousand sequences per patient.
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Publication status
published
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Book title
Journal / series
Volume
37 (10)
Pages / Article No.
3061 - 3075
Publisher
Oxford University Press
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Bayesian phylodynamics; duplicate sequences; subsampling; large data sets; BEAST 2; fast algorithms
Organisational unit
09490 - Stadler, Tanja / Stadler, Tanja
Notes
Funding
335529 - New phylogenetic methods for inferring complex population dynamics (EC)