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PAReTT: a Python package for the Automated Retrieval and management of divergence time data from the Time Tree resource
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  • Louis-Stéphane Le Clercq,
  • J. Paul Grobler,
  • Antoinette Kotze,
  • Desiré Dalton
Louis-Stéphane Le Clercq
South African National Biodiversity Institute, University of the Free State

Corresponding Author:[email protected]

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J. Paul Grobler
University of the Free State
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Antoinette Kotze
University of the Free State, South African National Biodiversity Institute
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Desiré Dalton
Teesside University
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Evolutionary processes such as speciation happens gradually over time making such processes time-dependant. Many studies conducted over the past two decades have aimed at providing accurate, fossil-calibrated, estimates of the divergence times of both extant and extinct species in most lineages of the tree of life, including fish, amphibians, reptiles, birds, and mammals. Data from more than 4 000 of these studies are now publicly available from a central time tree resource and provide opportunities of retrieving divergence times, evolutionary timelines, and time trees in various formats to enhance scientific investigations of evolution. There is, however, still limited functionality when studying large lists of species that would require the batch retrieval of data. To overcome this, a PYTHON package called Python Automated Retrieval of Time Tree data, abbreviated as PAReTT, was created to facilitate the interaction with the time tree resource when working with species lists. This package was recently used in a meta-analysis of candidate genes to study migration genetics and was able to successfully retrieve data for forty or more species to illustrate the relationship between divergence times and genetic data. The PAReTT package is freely available for download from GitHub to implement in PYTHON or as a pre-compiled Windows executable, with extensive documentation on the package available on the PAReTT GitHub wiki pages on dependencies, installation, and implementation of the various functions.
Aug 2023Published in Journal of Molecular Evolution volume 91 issue 4 on pages 502-513. 10.1007/s00239-023-10106-3