Using simulated data, we demonstrate that explicit incorporation of a relaxed clock leads to more accurate inference of the mean rate of evolution in addition to providing information on the variation in evolutionary rates.Our implementation generates confidence intervals for the evolutionary rate and the time to the most recent common ancestor using parametric bootstrapping (PB), which lends itself well to parallelization. a strict) molecular clock, as advised by Duchene et al.
2015) and compare the performance of the new method with other state-of-the-art methods.
Given a lineage with units of substitutions per site, which can be estimated from a sequence alignment using maximum likelihood (ML), a Bayesian approach, or a distance-based approach such as neighbour joining.
Estimation of relaxed molecular clocks using Bayesian Markov chain Monte Carlo is computationally expensive and may not scale well to large datasets.
We build on recent advances in maximum likelihood and least-squares phylogenetic and molecular clock dating methods to develop a fast relaxed-clock method based on a Gamma-Poisson mixture model of substitution rates.
This method estimates a distinct substitution rate for every lineage in the phylogeny while being scalable to large phylogenies.
Unknown lineage sample dates can be estimated as well as unknown root position.In many real applications, dates of lineage sampling may not be known with certainty.Sometimes, the exact sampling time is not known; it may be missing from the annotations, or recorded to a particular precision (e.g. Given an initial guess of tip dates model is optimized heuristically, it is challenging to apply standard likelihood based approaches such as profiling to estimate confidence intervals.A standard approach for assessing uncertainty in phylogenetic analyses is to perform non-PB, in which columns in the multiple sequence alignment are resampled with replacement in order to generate new datasets.Fast approximations to non-PB for phylogenetic reconstruction have also been proposed (Nguyen et al.We estimate confidence intervals for rates, dates, and tip dates using parametric and non-parametric bootstrap approaches.