Author:
Levinstein Hallak Keren,Rosset Saharon
Abstract
Abstract
Background
We tackle the problem of estimating species TMRCAs (Time to Most Recent Common Ancestor), given a genome sequence from each species and a large known phylogenetic tree with a known structure (typically from one of the species). The number of transitions at each site from the first sequence to the other is assumed to be Poisson distributed, and only the parity of the number of transitions is observed. The detailed phylogenetic tree contains information about the transition rates in each site. We use this formulation to develop and analyze multiple estimators of the species’ TMRCA. To test our methods, we use mtDNA substitution statistics from the well-established Phylotree as a baseline for data simulation such that the substitution rate per site mimics the real-world observed rates.
Results
We evaluate our methods using simulated data and compare them to the Bayesian optimizing software BEAST2, showing that our proposed estimators are accurate for a wide range of TMRCAs and significantly outperform BEAST2. We then apply the proposed estimators on Neanderthal, Denisovan, and Chimpanzee mtDNA genomes to better estimate their TMRCA with modern humans and find that their TMRCA is substantially later, compared to values cited recently in the literature.
Conclusions
Our methods utilize the transition statistics from the entire known human mtDNA phylogenetic tree (Phylotree), eliminating the requirement to reconstruct a tree encompassing the specific sequences of interest. Moreover, they demonstrate notable improvement in both running speed and accuracy compared to BEAST2, particularly for earlier TMRCAs like the human-Chimpanzee split. Our results date the human – Neanderthal TMRCA to be $$\sim 408,000$$
∼
408
,
000
years ago, considerably later than values cited in other recent studies.
Funder
Edmond J. Safra Center for Bioinformatics at Tel-Aviv University
Israeli Science Foundation grant
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Genetics
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