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A new method for inferring timetrees from temporally sampled molecular sequences

Sayaka Miura, Koichiro Tamura, Sergei L. Kosakovsky Pond, Louise A. Huuki, View ORCID ProfileJessica Priest, View ORCID ProfileJiamin Deng, View ORCID ProfileSudhir Kumar
doi: https://doi.org/10.1101/620187
Sayaka Miura
1Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, USA
2Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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Koichiro Tamura
3Department of Biological Sciences, Tokyo Metropolitan University, Tokyo, Japan
4Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University, Tokyo, Japan
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Sergei L. Kosakovsky Pond
1Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, USA
2Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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Louise A. Huuki
1Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, USA
2Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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Jessica Priest
1Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, USA
2Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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Jiamin Deng
1Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, USA
2Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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Sudhir Kumar
1Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, USA
2Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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  • For correspondence: s.kumar@temple.edu
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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 the relative rate framework underlying the RelTime approach. RTDT does not require many of the priors demanded by Bayesian approaches, and it has light computing requirements. We found RTDT to be accurate on simulated datasets evolved under a variety of branch rates models. Interestingly, we found two non-Bayesian methods (RTDT and Least Squares Dating [LSD]) to perform similar to or better than the Bayesian approaches available in BEAST and MCMCTree programs. RTDT method was found to generally outperform all other methods for phylogenies in with autocorrelated evolutionary rates. In analyses of empirical datasets, RTDT produced dates that were similar to those from Bayesian analyses. Speed and accuracy of the new method, as compared to the alternatives, makes it appealing for analyzing growing datasets of pathogenic strains. Cross-platform MEGA X software, freely available from http://www.megasoftware.net, now contains the new method for use through a friendly graphical user interface and in high-throughput settings.

AUTHOR SUMMARY Pathogen timetrees trace the origins and evolutionary histories of strains in populations, hosts, and outbreaks. The tips of these molecular phylogenies often contain sampling time information because the sequences were generally obtained at different times during the disease outbreaks and propagation. We have developed a new method for inferring timetrees for phylogenies with tip dates, which improves on widely-used Bayesian methods (e.g., BEAST) in computational efficiency and does not require prior specification of population parameters, branch rate model, or clock model. We performed extensive computer simulation and found that RTDT performed better than the other methods for the estimation of divergence times at deep node in phylogenies where evolutionary rates were autocorrelated. The new method is available in the cross-platform MEGA software package that provides a graphical user interface, and allows use via a command line in scripting and high throughput analysis (www.megasoftware.net).

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 26, 2019.
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A new method for inferring timetrees from temporally sampled molecular sequences
Sayaka Miura, Koichiro Tamura, Sergei L. Kosakovsky Pond, Louise A. Huuki, Jessica Priest, Jiamin Deng, Sudhir Kumar
bioRxiv 620187; doi: https://doi.org/10.1101/620187
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A new method for inferring timetrees from temporally sampled molecular sequences
Sayaka Miura, Koichiro Tamura, Sergei L. Kosakovsky Pond, Louise A. Huuki, Jessica Priest, Jiamin Deng, Sudhir Kumar
bioRxiv 620187; doi: https://doi.org/10.1101/620187

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