PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny

Abstract

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.

Detection and spread of high pathogenicity avian influenza virus H5N1 in the Antarctic Region

“…The remaining dataset was separated by segment and aligned using Mafft v7.520 79 , and manually trimmed to the open-reading frame using Aliview version 1.26 80 The trimmed alignments were then used to a infer maximum-likelihood phylogenetic tree using IQ-Tree version 2.2.3 81 along with ModelFinder were downloaded to create a sequence dataset. As North America and Europe were over-represented in this dataset, these were sub-sampled to maintain representative sequences using PARNAS 78 . The remaining dataset was separated by segment and aligned using Mafft v7.520 79 , and manually trimmed to the open-reading frame using Aliview version 1.26 80 The trimmed alignments were then used to a infer maximum-likelihood phylogenetic tree using IQ-Tree version 2.2.3 81 along with ModelFinder 8 and 1,000 ultrafast bootstraps 82 .…”

“…All H5N1 HPAIV clade 2.3.4.4b sequences available in the EpiFlu database between 1 st September 2020 and 22 nd January 2024 were downloaded to create a sequence dataset. As North America and Europe were over-represented in this dataset, these were sub-sampled to maintain representative sequences using PARNAS 78 . The remaining dataset was separated by segment and aligned using Mafft v7.520 79 , and manually trimmed to the open-reading frame using Aliview version 1.26 80 The trimmed alignments were then used to a infer maximum-likelihood phylogenetic tree using IQ-Tree version 2.2.3 81 along with ModelFinder were downloaded to create a sequence dataset.…”

2018-2019 human seasonal H3N2 influenza A virus spillovers into swine with demonstrated virus transmission in pigs were not sustained in the pig population

“…The human seasonal H3, swine H3 1990.4.a, and swine H3 2010.1 lineage datasets were aligned separately using mafft v7.490 (33) and a maximum-likelihood tree was inferred for each alignment following automatic model selection with IQ-Tree2 v2.2.2.6 (34, 35). To representatively subsample the datasets, we used PARNAS v0.1.4 (36) with a defined number of sequences (n=75 human H3, n=10 swine H3 1990.4.a, n = 10 swine H3 2010.1). The selected sequences from these three datasets were combined with the strains used in the biological assays into one FASTA file.…”

DNA damage drives antigen diversification through mosaic Variant Surface Glycoprotein (VSG) formation inTrypanosoma brucei

“…Threshold distances of 0.24659 and 0.21677 were used for EATRO1125 and Lister427, respectively. Down sampling and subtree generation of representative sequences was performed using PARNAS (0.1.4) 59 . The command used for PARNAS included flag: --cover -radius <threshold distance>.…”