DateLife: leveraging databases and analytical tools to reveal the dated Tree of Life

Achieving a high-quality reconstruction of a phylogenetic tree with branch
lengths proportional to absolute time (chronogram) is a difficult and
time-consuming task. But the increased availability of fossil and
molecular data, and time-efficient analytical techniques has resulted in
many recent publications of large chronograms for a large number and wide
diversity of organisms. Knowledge of the evolutionary time frame of
organisms is key for research in the natural sciences. It also represent
valuable information for education, science communication, and policy
decisions. When chronograms are shared in public, open databases, this
wealth of expertly-curated and peer-reviewed data on evolutionary
timeframe is exposed in a programatic and reusable way, as intensive and
localized efforts have improved data sharing practices, as well as
incentivizited open science in biology. Here we present DateLife, a
service implemented as an R package and an R Shiny website application
available at www.datelife.org, that provides functionalities for efficient
and easy finding, summary, reuse, and reanalysis of expert, peer-reviewed,
public data on time frame of evolution. The main DateLife workflow
constructs a chronogram for any given combination of taxon names by
searching a local chronogram database constructed and curated from the
Open Tree of Life Phylesystem phylogenetic database, which incorporates
phylogenetic data from the TreeBASE database as well. We implement and
test methods for summarizing time data from multiple source chronograms
using supertree and congruification algorithms, and using age data
extracted from source chronograms as secondary calibration points to add
branch lengths proportional to absolute time to a tree topology. DateLife
will be useful to increase awareness of the existing variation in
alternative hypothesis of evolutionary time for the same organisms, and
can foster exploration of the effect of alternative evolutionary timing
hypotheses on the results of downstream analyses, providing a framework
for a more informed interpretation of evolutionary results.