Datasets for: PARNAS: Objectively selecting the most representative taxa on a phylogeny

The use of next-generation sequencing technology has enabled phylogenetic
studies with hundreds of thousands of taxa. Such large-scale phylogenies
have become a critical component in genomic epidemiology in pathogens such
as SARS-CoV-2 and influenza A virus. However, detailed phenotypic
characterization of pathogens or generating a computationally tractable
dataset for detailed phylogenetic analyses requires objective subsampling
of taxa. To address this need, we propose PARNAS, an objective and
flexible algorithm to sample and select taxa that best represent observed
diversity by solving a generalized k-medoids problem on a phylogenetic
tree. PARNAS solves this problem efficiently and exactly by novel
optimizations and adapting algorithms from operations research. For more
nuanced selections, taxa can be weighted with metadata or genetic sequence
parameters, and the pool of potential representatives can be
user-constrained. Motivated by influenza A virus genomic surveillance and
vaccine design, PARNAS can be applied to identify representative taxa that
optimally cover the diversity in a phylogeny within a specified distance
radius. We demonstrated that PARNAS is more efficient and flexible than
existing approaches. To demonstrate its utility, we applied PARNAS to (i)
quantify SARS-CoV-2 genetic diversity over time, (ii) select
representative influenza A virus in swine genes derived from over 5 years
of genomic surveillance data, and (iii) identify gaps in H3N2 human
influenza A virus vaccine coverage. We suggest that our method, through
the objective selection of representatives in a phylogeny, provides
criteria for quantifying genetic diversity that has application in the the
rational design of multivalent vaccines and genomic epidemiology. PARNAS
is available at https://github.com/flu-crew/parnas.