用双向编辑轨迹重建祖先序列，能更好处理插入删除。
Tree-Conditioned Edit Flows for Ancestral Sequence Reconstruction
- 基于树结构的双向编辑流，强制祖先状态一致。
- 在含插入删除的自然序列上定位演化变化最准。
- 适合研究复杂变异的进化分析者使用。
祖先序列重构（ASR）旨在推断系统发育树内部节点上的已灭绝蛋白质序列。传统ASR方法通常基于连续时间马尔可夫替换模型，但大多独立处理位点，对插入和缺失的处理较弱或忽略不计。本文提出一种树条件化的编辑流模型，用于可变长度的ASR。给定两个后代序列及其到共同祖先的分支距离，模型通过约束一致祖先状态的双向编辑轨迹来重构祖先。在仅含上下文无关替换的实验进化序列基准上，该模型未达到最优传统方法的精度，但仍表现出合理性能，且训练数据包含插入、缺失和替换。在含丰富插入与缺失的自然同源序列基准上，该模型最准确地定位了推断的演化变化。
原文摘要 · Abstract (English)
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.
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