Fast and Accurate Estimates of Divergence Times from Big Data

Mol Biol Evol. 2017 Jan;34(1):45-50. doi: 10.1093/molbev/msw247. Epub 2016 Nov 11.


Ongoing advances in sequencing technology have led to an explosive expansion in the molecular data available for building increasingly larger and more comprehensive timetrees. However, Bayesian relaxed-clock approaches frequently used to infer these timetrees impose a large computational burden and discourage critical assessment of the robustness of inferred times to model assumptions, influence of calibrations, and selection of optimal data subsets. We analyzed eight large, recently published, empirical datasets to compare time estimates produced by RelTime (a non-Bayesian method) with those reported by using Bayesian approaches. We find that RelTime estimates are very similar to Bayesian approaches, yet RelTime requires orders of magnitude less computational time. This means that the use of RelTime will enable greater rigor in molecular dating, because faster computational speeds encourage more extensive testing of the robustness of inferred timetrees to prior assumptions (models and calibrations) and data subsets. Thus, RelTime provides a reliable and computationally thrifty approach for dating the tree of life using large-scale molecular datasets.

Keywords: Bayesian; computational time; molecular clocks; timetree.

Publication types

  • Letter
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Bayes Theorem
  • Biological Evolution*
  • Birds / genetics
  • Computational Biology / methods*
  • Computer Simulation
  • Databases, Nucleic Acid*
  • Datasets as Topic
  • Evolution, Molecular
  • Genetic Speciation
  • Genetic Variation*
  • Mammals / genetics
  • Models, Genetic
  • Mutation Rate
  • Phylogeny
  • Spiders / genetics