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PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny.
Alexey Markin1, Sanket Wagle2, Siddhant Grover2
1Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, 50010, USA.
Systematic Biology
|May 19, 2023
Summary
We developed parnas, a novel algorithm for objective taxon subsampling in large phylogenies. This method efficiently selects representative taxa to capture genetic diversity, aiding genomic epidemiology and vaccine design.
Area of Science:
- Computational Biology
- Genomics
- Epidemiology
Background:
- Large-scale phylogenies are crucial for genomic epidemiology of pathogens like SARS-CoV-2 and influenza A virus.
- Objective subsampling of taxa is needed for detailed analysis and phenotypic characterization of pathogens.
Purpose of the Study:
- To introduce parnas, an objective and flexible algorithm for selecting representative taxa from large phylogenetic trees.
- To address the need for computationally tractable datasets in phylogenetic analysis.
Main Methods:
- parnas utilizes a generalized k-medoids approach on phylogenetic trees, optimized for efficiency and exact solutions.
- The algorithm allows for weighted taxa and user-constrained selection pools for nuanced sampling.
- It adapts algorithms from operations research for effective implementation.
Main Results:
- parnas demonstrated superior efficiency and flexibility compared to existing subsampling methods.
- Applied to SARS-CoV-2, it quantified genetic diversity over time.
- Used for influenza A virus surveillance, it selected representative swine strains and identified vaccine coverage gaps for H3N2.
Conclusions:
- parnas provides an objective method for quantifying genetic diversity within phylogenies.
- The algorithm supports rational design of multivalent vaccines and enhances genomic epidemiology efforts.
- It offers a valuable tool for managing and analyzing large-scale genomic data.
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