Scholarlink

<p>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 <i>Tree of Blobs</i> 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, <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13015_2025_274_Article_IEq3.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="72" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {NANUQ}^+\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mtext>NANUQ</mtext> <mo>+</mo> </msup> </math></EquationSource> </InlineEquation>, 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

NANUQ+: A divide-and-conquer approach to network estimation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月25日

查看全文
  • Elizabeth S. Allman,
  • Hector Baños,
  • John A. Rhodes,
  • Kristina Wicke

摘要

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, \(\text {NANUQ}^+\) NANUQ + , 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.