Tree-Conditioned Edit Flows for Ancestral Sequence Reconstruction

TL;DR

This paper introduces a tree-conditioned edit-flow model for ancestral sequence reconstruction that handles variable-length sequences with insertions and deletions, offering improved localization of evolutionary changes.

Contribution

The novel model jointly reconstructs ancestral sequences using paired bidirectional edit trajectories constrained by phylogenetic information, addressing limitations of classical methods.

Findings

Model performs well on natural sequences with indels, despite not surpassing classical methods on simple substitution benchmarks.

It most accurately localizes evolutionary changes in sequences with abundant insertions and deletions.

The approach effectively incorporates variable-length sequences into ancestral reconstruction.

Abstract

Ancestral sequence reconstruction (ASR) aims to infer extinct protein sequences at internal nodes of a phylogenetic tree. Classical ASR methods are typically based on continuous-time Markov substitution models, but they treat sites largely independently and handle insertions and deletions only weakly or not at all. We introduce a tree-conditioned edit-flow model for variable-length ASR. Given two descendant sequences and their branch distances to a shared ancestor, the model reconstructs the ancestor through paired bidirectional edit trajectories constrained to agree on a common ancestral state. On a benchmark of experimentally evolved sequences with only context-independent substitutions, the model does not match the accuracy of the best classical method, yet still achieves reasonable performance despite being trained on natural sequences that include insertions, deletions, and substitutions. On a benchmark of natural homologous sequences with abundant insertions and deletions, the model most accurately localizes inferred evolutionary change.