How much can evolved characters tell us about the tree that generated them?
Elchanan Mossel, Mike Steel

TL;DR
This paper reviews recent theoretical results on the limits of phylogenetic signal in evolved characters, exploring bounds, model parameters, and the impact of character state space size on tree reconstruction accuracy.
Contribution
It provides new bounds and insights on the amount of phylogenetic information characters can carry, including extensions for rate variation and large state spaces.
Findings
Explicit bounds on ancestral state reconstruction probability
Phase transition in sequence length needed for accurate tree inference
Impact of large state spaces on character-based phylogenetic analysis
Abstract
In this paper we review some recent results that shed light on a fundamental question in molecular systematics: how much phylogenetic `signal' can we expect from characters that have evolved under some Markov process? There are many sides to this question and we begin by describing some explicit bounds on the probability of correctly reconstructing an ancestral state from the states observed at the tips. We show how this bound sets upper limits on the probability of tree reconstruction from aligned sequences, and we provide some new extensions that allow site-to-site rate variation or a covarion mechanism. We then explore the relationship between the number of sites required for accurate tree reconstruction and other model parameters - such as the number of species, and substitution probabilities, and we describe a phase transition that occurs when substitution probabilities exceed a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenomics and Phylogenetic Studies · Genetic diversity and population structure · Genome Rearrangement Algorithms
