Allman Elizabeth S, Baños Hector, Rhodes John A, Wicke Kristina
Department of Mathematics and Statistics, University of Alaska Fairbanks, Fairbanks, AK, USA.
Department of Mathematics, California State University San Bernardino, San Bernardino, CA, USA.
Algorithms Mol Biol. 2025 Jul 25;20(1):14. doi: 10.1186/s13015-025-00274-w.
Inference of a species network from genomic data remains a difficult problem, with recent progress mostly limited to the level-1 case. However, inference of the Tree of Blobs of a network, showing only the network's cut edges, can be performed for any network by TINNiK, suggesting a divide-and-conquer approach to network inference where the tree's multifurcations are individually resolved to give more detailed structure. Here we develop a method, , to quickly perform such a level-1 resolution. Viewed as part of the NANUQ pipeline for fast level-1 inference, this gives tools for both understanding when the level-1 assumption is likely to be met and for exploring all highly-supported resolutions to cycles.
从基因组数据推断物种网络仍然是一个难题,近期进展大多局限于一级情况。然而,对于任何网络,TINNiK都可以推断出网络的叶块树,该树仅显示网络的割边,这表明了一种分而治之的网络推断方法,即分别解析树的多歧分支以给出更详细的结构。在这里,我们开发了一种方法, ,以快速执行这种一级分辨率。作为用于快速一级推断的NANUQ管道的一部分,这提供了工具,既用于理解何时可能满足一级假设,也用于探索所有对循环的高度支持的分辨率。