PIQMEE: Bayesian phylodynamic method for analysis of large datasets with duplicate sequences.

ABSTRACT: Next-generation sequencing of pathogen quasispecies within a host yields datasets of tens to hundreds of unique sequences. However, the full dataset often contains thousands of sequences, since many of those unique sequences have multiple identical copies. Datasets of this size represent a computational challenge for currently available Bayesian phylogenetic and phylodynamic methods. Through simulations we explore how large datasets 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 datasets with increased computational efficiency as compared to 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 datasets. While the classic method converges poorly for datasets of 6000 sequences when allowed to run for 7 days, our method converges in slightly more than one day. In fact, PIQMEE can handle datasets of around 21000 sequences with 20 unique sequences in 14 days. Finally, we apply the method to a real, within-host HIV sequencing dataset with several thousand sequences per patient.

SUBMITTER: Boskova V 

PROVIDER: S-EPMC7530608 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature