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.
| Comments: | 9 pages of main text, 3 figures, 3 tables, and 1 algorithm. This version is a preliminary preprint |
| Subjects: | Quantitative Methods (q-bio.QM); Machine Learning (cs.LG); Populations and Evolution (q-bio.PE) |
| Cite as: | arXiv:2605.04119 [q-bio.QM] |
| (or arXiv:2605.04119v1 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2605.04119 arXiv-issued DOI via DataCite |
From: Emil Sharafutdinov [view email]
[v1]
Tue, 5 May 2026 13:04:45 UTC (1,045 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。