AI 中文总结
本文提出了系统发育网络中复制事件聚类问题的扩展方法NetEC，开发了多项式时间动态规划算法及相关推理与分析程序，在模拟数据和含29000余个基因树的露兜树目数据集上验证了其推断基因组复制事件的准确性。
AI 中文摘要
Guigó等人在20世纪90年代提出的经典复制事件聚类（EC）模型是推断基因组复制事件的基础方法，对理解基因组进化至关重要。该模型将一组基因树中的单基因复制聚类到物种树的节点位置，以最小化此类位置的总数，这些位置被称为复制事件。本文中，我们引入NetEC，这是该问题向系统发育网络的新扩展。为解决NetEC，我们首先开发了一个多项式时间动态规划（DP）算法，用于测试给定的一组网络节点是否可作为事件位置。然后，我们提出了一个主要推理算法，该算法利用此DP组件优化事件数量；虽然可行性测试在多项式时间内运行，但完整优化在最坏情况下具有指数复杂度，并且为更大规模的实例提供了可选的启发式模式。我们还提出了一个扩展的事件分析程序，用于识别网状节点下方的额外基因组复制候选，通过解决网状结构诱导的复制向上聚类问题，对主要算法进行补充。我们在模拟数据和包含超过29000个基因树的实证露兜树目数据集上评估了我们的方法，证明即使在存在多个网状结构的情况下，也能准确且精确地推断基因组复制事件。
英文摘要
The classical duplication episode clustering (EC) model introduced by Guigó et al. in the 1990s provides a foundational approach for inferring genomic duplication events crucial to understanding genome evolution. This model clusters single gene duplications from a collection of gene trees at locations in the species tree to minimize the total number of such locations, called duplication episodes. Here, we introduce NetEC, a novel extension of this problem to phylogenetic networks. To solve NetEC, we first develop a polynomial-time dynamic programming (DP) algorithm for testing whether a given set of network nodes can serve as episode locations. We then propose a main inference algorithm that utilizes this DP component to optimize the episode count; while the feasibility test runs in polynomial time, the full optimization has exponential worst-case complexity, and an optional heuristic mode is provided for larger instances. We also propose an extended episode analysis procedure that identifies additional genomic duplication candidates below reticulation nodes, complementing the main algorithm by resolving potential upward clustering of duplications induced by reticulation. We evaluate our method on simulated data and on an empirical Pandanales dataset comprising over 29,000 gene trees, demonstrating exact and accurate inference of genomic duplication events even in the presence of multiple reticulations.
CommentsPreprint version submitted to ECCB 2026 before peer review. A revised version has been accepted for publication in Bioinformatics as part of the ECCB 2026 Proceedings
