bioRxivEvolutionary Biology
Chronospaces: an R package for the statistical exploration of divergence times promotes the assessment of methodological sensitivity
Abstract
O_LIMuch of our understanding of the history of life hinges upon time calibration, the process of assigning absolute times to cladogenetic events. Bayesian approaches to time scaling phylogenetic trees have dramatically grown in complexity, and depend today upon numerous methodological choices. Arriving at objective justifications for all of these is difficult and time consuming. Thus, divergence times are routinely inferred under only one or a handful of parametric conditions, often times chosen arbitrarily. Progress towards building robust biological timescales necessitate the development of better methods to visualize and quantify the sensitivity of results to these decisions. C_LIO_LIHere, we present an R package that assists in this endeavor through the use of chronospaces, i.e., graphical representations summarizing variation in the node ages contained in time-calibrated trees. We further test this approach by estimating divergence times for three empirical datasets--spanning widely differing evolutionary timeframes--using the software PhyloBayes. C_LIO_LIOur results reveal large differences in the impact of many common methodological decisions, with the choice of clock (uncorrelated vs. autocorrelated) and loci having strong effects on inferred ages. Other decisions have comparatively minor consequences, including the use of the computationally intensive site-heterogeneous model CAT-GTR, whose effect might only be discernible for exceedingly old divergences (e.g., the deepest eukaryote nodes). C_LIO_LIThe package chronospace implements a range of graphical and analytical tools that assist in the exploration of sensitivity and the prioritization of computational resources in the inference of divergence times. C_LI
- assay: DNA methylation
- research focus: benchmarking
- research focus: variant calling
- research focus: phylogenetics / evolution
- approach: statistical model
- approach: Bayesian inference
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