ArticlePLoS computational biology2020
A new method for inferring timetrees from temporally sampled molecular sequences.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 27 citations in OpenAlex.
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- The Evolution of Wisteria Vein Mosaic Virus: A Case Study Approach to Track the Emergence of New Potyvirus Threats.Pathogens (Basel, Switzerland) · 2024Article
- Modeling Substitution Rate Evolution across Lineages and Relaxing the Molecular Clock.Genome biology and evolution · 2024Review
- Population structure and ongoing microevolution of the emerging multidrug-resistant Salmonella Typhimurium ST213.npj antimicrobials and resistance · 2024Article
- Molecular epidemiology of HIV-1 among adult female sex workers at the Guatemala-Mexico border.Global public health · 2023Article
- Molecular evidence for SARS-CoV-2 in samples collected from patients with morbilliform eruptions since late 2019 in Lombardy, northern Italy.Environmental research · 2022Article
- Enhanced Apiaceous Potyvirus Phylogeny, Novel Viruses, and New Country and Host Records from SequencingPlants (Basel, Switzerland) · 2022Article
- Lifestyle Transitions in Fusarioid Fungi are Frequent and Lack Clear Genomic Signatures.Molecular biology and evolution · 2022Article
- Phylogenetic reconstruction of the initial stages of the spread of the SARS-CoV-2 virus in the Eurasian and American continents by analyzing genomic data.Virus research · 2021Article
- Article
- MEGA11: Molecular Evolutionary Genetics Analysis Version 11.Molecular biology and evolution · 2021Article
- Identification of Hidden Population Structure in Time-Scaled Phylogenies.Systematic biology · 2020Article
- Evolutionary and structural analyses of SARS-CoV-2 D614G spike protein mutation now documented worldwide.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
Abstract
Pathogen timetrees are phylogenies scaled to time. They reveal the temporal history of a pathogen spread through the populations as captured in the evolutionary history of strains. These timetrees are inferred by using molecular sequences of pathogenic strains sampled at different times. That is, temporally sampled sequences enable the inference of sequence divergence times. Here, we present a new approach (RelTime with Dated Tips [RTDT]) to estimating pathogen timetrees based on a relative rate framework underlying the RelTime approach that is algebraic in nature and distinct from all other current methods. RTDT does not require many of the priors demanded by Bayesian approaches, and it has light computing requirements. In analyses of an extensive collection of computer-simulated datasets, we found the accuracy of RTDT time estimates and the coverage probabilities of their confidence intervals (CIs) to be excellent. In analyses of empirical datasets, RTDT produced dates that were similar to those reported in the literature. In comparative benchmarking with Bayesian and non-Bayesian methods (LSD, TreeTime, and treedater), we found that no method performed the best in every scenario. So, we provide a brief guideline for users to select the most appropriate method in empirical data analysis. RTDT is implemented for use via a graphical user interface and in high-throughput settings in the newest release of cross-platform MEGA X software, freely available from http://www.megasoftware.net.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.