Phylogenetic Networks

Huson, Daniel H.; Rupp, Regula; Scornavacca, Céline

doi:10.1017/cbo9780511974076

Cited by 359 publications

(136 citation statements)

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“…Oftentimes, however, dissimilarity matrices are not tree metrics, as data from sequence alignments often fails due to horizontal gene transfer, recombination, or gene duplication [18]. Indeed, assuming a tree model and applying phylogenetic inference tools on the data can be misleading [4].…”

Section: 1mentioning

confidence: 99%

A Space of Phylogenetic Networks

Devadoss¹,

Petti²

2017

SIAM J. Appl. Algebra Geometry

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Abstract. A classical problem in computational biology is constructing a phylogenetic tree given a set of distances between n species. In many cases, a tree structure is too constraining. We consider a split network, which is a generalization of a tree in which multiple parallel edges signify divergence. A geometric space of such networks is introduced, forming a natural extension of the familiar space of phylogenetic trees. We explore properties of the space of networks and construct a natural embedding of the compactification of the real moduli space of curves within it.

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Section: 1mentioning

confidence: 99%

A Space of Phylogenetic Networks

Devadoss¹,

Petti²

2017

SIAM J. Appl. Algebra Geometry

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“…Given a binary network and a binary tree, the tree containment (TC) problem is to determine whether or not the network displays the tree [5]. This problem is known to be NP-complete [7,6], and a large part of the current research therefore focuses on finding tractable classes of binary networks that are as general as possible.…”

Section: B-d)mentioning

confidence: 99%

“…The nodes of indegree at least two are called reticulations and represent genetic material flow from several ancestral species into an "unrelated" species. A plethora of methods for reconstructing networks and related algorithmic issues have been extensively studied over the past two decades [4,5,8,10].…”

Section: Introductionmentioning

confidence: 99%

“…Biologists therefore demand that the network display existing gene trees, and the corresponding algorithmic problem is known as the tree containment problem (or TC problem for short) [5], which is well-known to be NP-complete [7,6]. Great efforts have been devoted to identifying tractable subclasses of networks, such as binary galled trees [7], normal networks, binary tree-child networks, level-k 1 networks [6], or nearly-stable networks [3].…”

Section: Introductionmentioning

confidence: 99%

“…A node v in a network is stable if there exists a leaf such that every path from the root to the leaf passes through v. A network is stable (or reticulation visible) [5] if every reticulation is stable. Motivated by the study in [2], we make progress in this work towards determining the complexity of the TC problem on stable networks by presenting a quadratic-time algorithm for a new class that we call genetically stable networks.…”

Section: Introductionmentioning

confidence: 99%

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Solving the Tree Containment Problem for Genetically Stable Networks in Quadratic Time

Gambette¹,

Gunawan

Labarre³

et al. 2016

Lecture Notes in Computer Science

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Abstract.A phylogenetic network is a rooted acyclic digraph whose leaves are labeled with a set of taxa. The tree containment problem is a fundamental problem arising from model validation in the study of phylogenetic networks. It asks to determine whether or not a given network displays a given phylogenetic tree over the same leaf set. It is known to be NP-complete in general. Whether or not it remains NP-complete for stable networks is an open problem. We make progress towards answering that question by presenting a quadratic time algorithm to solve the tree containment problem for a new class of networks that we call genetically stable networks, which include tree-child networks and comprise a subclass of stable networks.

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Machine Learning Early Detection of SARS‐CoV‐2 High‐Risk Variants

Li,

et al. 2024

Advanced Science

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The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has evolved many high‐risk variants, resulting in repeated COVID‐19 waves over the past years. Therefore, accurate early warning of high‐risk variants is vital for epidemic prevention and control. However, detecting high‐risk variants through experimental and epidemiological research is time‐consuming and often lags behind the emergence and spread of these variants. In this study, HiRisk‐Detector a machine learning algorithm based on haplotype network, is developed for computationally early detecting high‐risk SARS‐CoV‐2 variants. Leveraging over 7.6 million high‐quality and complete SARS‐CoV‐2 genomes and metadata, the effectiveness, robustness, and generalizability of HiRisk‐Detector are validated. First, HiRisk‐Detector is evaluated on actual empirical data, successfully detecting all 13 high‐risk variants, preceding World Health Organization announcements by 27 days on average. Second, its robustness is tested by reducing sequencing intensity to one‐fourth, noting only a minimal delay of 3.8 days, demonstrating its effectiveness. Third, HiRisk‐Detector is applied to detect risks among SARS‐CoV‐2 Omicron variant sub‐lineages, confirming its broad applicability and high ROC‐AUC and PR‐AUC performance. Overall, HiRisk‐Detector features powerful capacity for early detection of high‐risk variants, bearing great utility for any public emergency caused by infectious diseases or viruses.

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Phylogenetic Networks

Cited by 359 publications

References 0 publications

A Space of Phylogenetic Networks

A Space of Phylogenetic Networks

Solving the Tree Containment Problem for Genetically Stable Networks in Quadratic Time

Machine Learning Early Detection of SARS‐CoV‐2 High‐Risk Variants

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