Optimizing viral genome subsampling by genetic diversity and temporal distribution (TARDiS) for Phylogenetics

Marini, Simone; Mavian, Carla; Riva, Alberto; Salemi, Marco; Magalis, Brittany Rife

doi:10.1101/2021.01.15.426832

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Next, we explored concordance or discordance of viral and proviral populations in both patients with concordant subtypes (PCG3 and SDS4), as well as in the two patients that showed both concordance and discordance in subtypes (VBP2 and JOR10). After subsampling patients VBP2, PCG3, and SDS4, we optimized viral genome genetic diversity and temporal distribution using TARDIS ( 43 ) and controlled for the presence of both phylogenetic signals (see Fig. S5).…”

Section: Resultsmentioning

confidence: 99%

“…Sequences from the two runs were aligned using MAFFT ( 79 ). Before performing the maximum likelihood (ML) phylogenetic and Bayesian phylodynamic analyses, we randomly subset the VBP2, PCG3, and SDS4 data sets targeting sequences by compartments (plasma and PBMC when needed) and by time point using TARDIS ( 43 ). These data sets contained a large number of sequences, many of which were nearly identical and therefore not contributing to the phylogenetic resolution.…”

Section: Methodsmentioning

confidence: 99%

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Discordance between HIV-1 Population in Plasma at Rebound after Structured Treatment Interruption and Archived Provirus Population in Peripheral Blood Mononuclear Cells

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Discordance between HIV-1 Population in Plasma at Rebound after Structured Treatment Interruption and Archived Provirus Population in Peripheral Blood Mononuclear Cells

et al. 2022

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“…One group of methods identify clusters of related sequences, e.g., TreeCluster (Balaban et al ., 2019) or PhyCLIP (Han et al ., 2019), but these are unable to objectively select strains within the identified clusters. A second group of methods select or remove single strains: such as TARDiS (Marini et al ., 2021) that can perform time-aware sampling of genetic sequences, or Treemmer (Menardo et al ., 2018) that reduces taxa on a phylogeny through pruning redundant branches. These selection approaches, either do not account for evolution, or are not able to objectively select representatives across tens of thousands of taxa in a reasonable time.…”

Section: Discussionmentioning

confidence: 99%

PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny

Markin

Wagle

Grover

et al. 2022

Preprint

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The use of next-generation sequencing technology has enabled phylogenetic studies with hun- dreds 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, de- tailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires bias free subsampling of taxa. To address this need, we propose parnas, an objective and flexible algorithm to sample and select taxa that best repre- sent 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 current approaches, and applied it to select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data. Our objective selection of 4 to 6 strains selected every two years from the 16 distinct genetic clades were sufficient to cover 80% of diversity circulating in US swine. We suggest that this method, through the objective selection of representatives in a phylogeny, provides criteria for rational multivalent vaccine design and for quantifying diversity. PARNAS is available at https://github.com/flu-crew/parnas.

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“…Random subsampling may potentially reduce unknown sampling biases. It is worth noting that the sampling biases may persist even when a large number of samples is available, and large datasets need to be downsampled to make computations tractable [17] , [18] , [19] . A theoretical analysis of small-sample support estimation in the presence of sampling biases is available at [20] .…”

Section: Introductionmentioning

confidence: 99%

Small-Sample Estimation of the Mutational Support and Distribution of SARS-CoV-2

Rana

Chien

Peng

et al. 2023

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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We consider the problem of determining the mutational support and distribution of the SARS-CoV-2 viral genome in the small-sample regime. The mutational support refers to the unknown number of sites that may eventually mutate in the SARS-CoV-2 genome while mutational distribution refers to the distribution of point mutations in the viral genome across a population. The mutational support may be used to assess the virulence of the virus and guide primer selection for real-time RT-PCR testing. Estimating the distribution of mutations in the genome of different subpopulations while accounting for the unseen may also aid in discovering new variants. To estimate the mutational support in the small-sample regime, we use GISAID sequencing data and our state-of-the-art polynomial estimation techniques based on new weighted and regularized Chebyshev approximation methods. For distribution estimation, we adapt the well-known Good-Turing estimator. Our analysis reveals several findings: First, the mutational supports exhibit significant differences in the ORF6 and ORF7a regions (older versus younger patients), ORF1b and ORF10 regions (females versus males) and in almost all ORFs (Asia/Europe/North America). Second, even though the N region of SARS-CoV-2 has a predicted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$10\%$\end{document} mutational support, mutations fall outside of the primer regions recommended by the CDC.

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Optimizing viral genome subsampling by genetic diversity and temporal distribution (TARDiS) for Phylogenetics

Abstract: TARDiS for Philogenetics is a novel tool for optimal genetic sub-sampling. It optimizes both genetic diversity and temporal distribution through a genetic algorithm. TARDiS, along with example data sets and a user manual, is available at https://github.com/smarini/tardis-phylogenetics

Cited by 5 publications

References 24 publications

Discordance between HIV-1 Population in Plasma at Rebound after Structured Treatment Interruption and Archived Provirus Population in Peripheral Blood Mononuclear Cells

Discordance between HIV-1 Population in Plasma at Rebound after Structured Treatment Interruption and Archived Provirus Population in Peripheral Blood Mononuclear Cells

PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny

Small-Sample Estimation of the Mutational Support and Distribution of SARS-CoV-2

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