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

Abstract 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-...

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Main Authors: Elizabeth S. Allman, Hector Baños, John A. Rhodes, Kristina Wicke
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Algorithms for Molecular Biology
Subjects:
Online Access:https://doi.org/10.1186/s13015-025-00274-w
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author Elizabeth S. Allman
Hector Baños
John A. Rhodes
Kristina Wicke
author_facet Elizabeth S. Allman
Hector Baños
John A. Rhodes
Kristina Wicke
author_sort Elizabeth S. Allman
collection DOAJ
description Abstract 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.
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institution Kabale University
issn 1748-7188
language English
publishDate 2025-07-01
publisher BMC
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series Algorithms for Molecular Biology
spelling doaj-art-809c678d226c4170b4e887d9da10eb4b2025-08-20T03:42:44ZengBMCAlgorithms for Molecular Biology1748-71882025-07-0120112510.1186/s13015-025-00274-wNANUQ+: A divide-and-conquer approach to network estimationElizabeth S. Allman0Hector Baños1John A. Rhodes2Kristina Wicke3Department of Mathematics and Statistics, University of Alaska FairbanksDepartment of Mathematics, California State University San BernardinoDepartment of Mathematics and Statistics, University of Alaska FairbanksDepartment of Mathematical Sciences, New Jersey Institute of TechnologyAbstract 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.https://doi.org/10.1186/s13015-025-00274-wPhylogenetic networkLevel-1Tree of blobsMultispecies coalescent
spellingShingle Elizabeth S. Allman
Hector Baños
John A. Rhodes
Kristina Wicke
NANUQ+: A divide-and-conquer approach to network estimation
Algorithms for Molecular Biology
Phylogenetic network
Level-1
Tree of blobs
Multispecies coalescent
title NANUQ+: A divide-and-conquer approach to network estimation
title_full NANUQ+: A divide-and-conquer approach to network estimation
title_fullStr NANUQ+: A divide-and-conquer approach to network estimation
title_full_unstemmed NANUQ+: A divide-and-conquer approach to network estimation
title_short NANUQ+: A divide-and-conquer approach to network estimation
title_sort nanuq a divide and conquer approach to network estimation
topic Phylogenetic network
Level-1
Tree of blobs
Multispecies coalescent
url https://doi.org/10.1186/s13015-025-00274-w
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AT hectorbanos nanuqadivideandconquerapproachtonetworkestimation
AT johnarhodes nanuqadivideandconquerapproachtonetworkestimation
AT kristinawicke nanuqadivideandconquerapproachtonetworkestimation