I want to map NCBI taxID, specifically a pair of Genbank sequences, to the divergence time estimate of their common ancestor. For example, using a coalescence theory approach predominantly used in phylogenetics. Do we have a database for such correlation?
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I want to map NCBI taxID to their evolution time. Do we have a database for such correlation?
It would be best to edit your question to clarify what you mean by "evolution time" since this is not a well-defined term in molecular evolution. Even so, I think we can provide a clear answer to this question.
Estimating branch lengths between divergent taxa is an area of active research—there is no one method that works best in all scenarios. As far as taxonomy is concerned, resources like the NCBI Taxonomy database are under continuous refinement by the community.
Computing estimated divergence times and mapping those times to taxids would be a non-trivial task even for a single clade. Actively maintaining a database of divergence times across the entire taxonomy would be incredibly challenging, and it's doubtful that any such resource exists.
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$\begingroup$ Daniel, thanks for the comment! Do you think it is conceptually not sound if I try to annotate a taxID pair (e.g. taxID A - taxID B) with their divergence time? $\endgroup$ Jan 13, 2022 at 8:15
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1$\begingroup$ @JohnnyTam I do agree with Daniel in that this is an area of active research and divergence times are difficult to calculate even for a single clade. That said, one source where I've been able to look for publications / estimations of divergence times for genera that I'm interested in has been timetree.org. Note that you shouldn't just use the measured statistics like 'Estimated Time' since those are likely to be misleading, but it's a good place to find associated papers. $\endgroup$– LauraJan 16, 2022 at 9:50
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Frontiers in Ecology and Evolution did a focused and succinct review looking at 'the Tree of Life' in 2017 here. They particularly highlight the importance of integrating a fossil record within molecular trees. The upshot is simply that increasingly tighter integration between palaeontology and molecular dating is the ongoing research frontier.
The best approach in context is to look at relevant literature. The key challenge in my opinion is the underlying population dynamic because this does heavily affect temporal estimates.