AI Insight
This study introduces the first likelihood-free method for inferring phylogenetic trees, which are used to reconstruct evolutionary relationships between genetic sequences. The approach uses a neural network with novel sequence encoding and a factorized probability distribution over tree structures, eliminating the need for computationally expensive likelihood calculations that require simplifying assumptions. The method demonstrates better accuracy than traditional likelihood-based approaches, with particularly strong advantages when modeling complex evolutionary processes where likelihoods cannot be feasibly computed.
Why it matters
This advancement could significantly accelerate phylogenetic analyses of large genomic datasets and enable more realistic evolutionary modeling by removing the constraint of requiring tractable likelihood functions. The method has potential applications in studying pathogen evolution, biodiversity conservation, and understanding the origins of genetic diseases.
Understand the Science
Abstract: Phylogenetic inference, the task of reconstructing how related sequences evolved from common ancestors, is a central objective in evolutionary genomics. The current state-of-the-art methods exploit probabilistic models of sequence evolution along phylogenetic trees, by searching for the tree maximizing the likelihood of observed sequences, or by estimating the posterior of the tree given the sequences in a Bayesian framework. Both approaches typically require to compute likelihoods, which is only feasible under simplifying assumptions such as independence of the evolution at the different positions of the sequence, and even then remains a costly operation. Here we present the first likelihood-free inference method for posterior distributions over phylogenies. It exploits a novel expressive encoding for pairs of sequences, and a parameterized probability distribution factorized over a succession of subtree merges. The resulting network provides well-calibrated estimates of the posterior distribution leading to more accurate tree topologies than existing methods, even under models amenable to likelihood computation. We further show that its edge against likelihood-based methods dramatically increases under models of sequence evolution with intractable likelihoods.
Source: Likelihood-free inference of phylogenetic tree posterior distributions