Polynomial-Time Algorithms for Building a Consensus MUL-Tree

Cui, Yun; Jansson, Jesper; Sung, Wing-Kin

doi:10.1089/cmb.2012.0008

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…One challenge will be to develop and integrate new methods to compute ancestral annotations combining the advantages of the nuanced outputs of the ML/Bayesian settings, and the overly stringent outputs of the joint and MAP methods, such as the recent decision theory-based PastML approach [12]. In the long-term, a next step will be to integrate statistical tools and information visualization methods to quickly identify robust evolutionary scenarios, such as a consensus of transition maps between different methods (tree computations, ancestral annotation inferences, see for instance [17]), or for more elaborated transition maps in the case of huge datasets. In the context of massive data, which are more are more common nowadays (e.g.…”

Section: Resultsmentioning

confidence: 99%

PastView: a user-friendly interface to explore ancestral scenarios

et al. 2019

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Background Ancestral character states computed from the combination of phylogenetic trees with extrinsic traits are used to decipher evolutionary scenarios in various research fields such as phylogeography, epidemiology, and ecology. Despite the existence of powerful methods and software in ancestral character state inference, difficulties may arise when interpreting the outputs of such inferences. The growing complexity of data (trees, annotations), the diversity of optimization criteria for computing trees and ancestral character states, the combinatorial explosion of potential evolutionary scenarios if some ancestral characters states do not stand out clearly from others, requires the design of new methods to explore associations of phylogenetic trees with extrinsic traits, to ease the visualization and interpretation of evolutionary scenarios. Result We developed PastView, a user-friendly interface that includes numerical and graphical features to help users to import and/or compute ancestral character states from discrete variables and extract ancestral scenarios as sets of successive transitions of character states from the tree root to its leaves. PastView provides summarized views such as transition maps and integrates comparative tools to highlight agreements or discrepancies between methods of ancestral annotations inference. Conclusion The main contribution of PastView is to assemble known numerical and graphical methods into a multi-maps graphical user interface dedicated to the computing, searching and viewing of evolutionary scenarios based on phylogenetic trees and ancestral character states. PastView is available publicly as a standalone software on www.pastview.org .

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Section: Resultsmentioning

confidence: 99%

PastView: a user-friendly interface to explore ancestral scenarios

et al. 2019

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“…Third, extensions of consensus trees to multi-labeled phylogenetic trees (MUL-trees), where the same leaf label may be used more than once in the same tree, were introduced by Lott et al [2009] and further studied in Cui et al [2012] and Huber et al [2012]. Here, a major obstacle is that MUL-trees' cluster collections are no longer sets but multisets, and certain basic problems become NP-hard when extended to multisets.…”

Section: Testingmentioning

confidence: 99%

Improved Algorithms for Constructing Consensus Trees

Jansson

Shen

Sung

2016

J. ACM

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A consensus tree is a single phylogenetic tree that summarizes the branching structure in a given set of conflicting phylogenetic trees. Many different types of consensus trees have been proposed in the literature; three of the most well-known and widely used ones are the majority rule consensus tree, the loose consensus tree, and the greedy consensus tree. This article presents new deterministic algorithms for constructing them that are faster than all the previously known ones. Given k phylogenetic trees with n leaves each and with identical leaf label sets, our algorithms run in O(nk) time (majority rule consensus tree), O(nk) time (loose consensus tree), and O(n 2 k) time (greedy consensus tree). Our algorithms for the majority rule consensus and the loose consensus trees are optimal since the input size is (nk). Experimental results show that the algorithms are fast in practice.

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“…Thirdly, extensions of consensus trees to multilabeled phylogenetic trees (MUL-trees), where the same leaf label may be used more than once in the same tree, were recently introduced by [15] and further studied in [6,12]. Here, a major obstacle is that MUL-trees' cluster collections are no longer sets but multisets, and certain basic problems become NP-hard when extended to multisets.…”

Section: Final Remarksmentioning

confidence: 99%

Improved Algorithms for Constructing Consensus Trees

Jansson¹,

Shen²,

Sung³

2013

Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms

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A consensus tree is a single phylogenetic tree that summarizes the branching structure in a given set of conflicting phylogenetic trees. Many different types of consensus trees have been proposed in the literature; three of the most well-known and widely used ones are the majority rule consensus tree, the loose consensus tree, and the greedy consensus tree. This paper presents new deterministic algorithms for constructing them that are faster than all the previously known ones. Given k phylogenetic trees with n leaves each and with identical leaf label sets, our algorithms run in O(nk log k) time (majority rule consensus tree), O(nk) time (loose consensus tree), and O(n 2 k) time (greedy consensus tree).

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Polynomial-Time Algorithms for Building a Consensus MUL-Tree

Cited by 11 publications

References 24 publications

PastView: a user-friendly interface to explore ancestral scenarios

PastView: a user-friendly interface to explore ancestral scenarios

Improved Algorithms for Constructing Consensus Trees

Improved Algorithms for Constructing Consensus Trees

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