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A Faster Algorithm for Constructing the Frequency Difference Consensus Tree

Authors Jesper Jansson, Wing-Kin Sung, Seyed Ali Tabatabaee, Yutong Yang

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LIPIcs.STACS.2024.43.pdf
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Subject Classification

ACM Subject Classification
  • Theory of computation → Graph algorithms analysis
  • Applied computing → Bioinformatics
Keywords
  • phylogenetic tree
  • frequency difference consensus tree
  • tree algorithm
  • centroid path decomposition
  • max-Manhattan Skyline Problem

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Abstract

A consensus tree is a phylogenetic tree that summarizes the evolutionary relationships inferred from a collection of phylogenetic trees with the same set of leaf labels. Among the many types of consensus trees that have been proposed in the last 50 years, the frequency difference consensus tree is one of the more finely resolved types that retains a large amount of information. This paper presents a new deterministic algorithm for constructing the frequency difference consensus tree. Given k phylogenetic trees with identical sets of n leaf labels, it runs in O(knlog{n}) time, improving the best previously known solution.

Cite As Get BibTex

Jesper Jansson, Wing-Kin Sung, Seyed Ali Tabatabaee, and Yutong Yang. A Faster Algorithm for Constructing the Frequency Difference Consensus Tree. In 41st International Symposium on Theoretical Aspects of Computer Science (STACS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 289, pp. 43:1-43:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.STACS.2024.43

Author Details

Jesper Jansson
  • Kyoto University, Japan
Wing-Kin Sung
  • The Chinese University of Hong Kong, China
  • Hong Kong Genome Institute, Hong Kong Science Park, China
Seyed Ali Tabatabaee
  • Dept. of Computer Science, University of British Columbia, Vancouver, Canada
Yutong Yang
  • Hong Kong Genome Institute, Hong Kong Science Park, China

Funding

This work was partially funded by JSPS KAKENHI grant 22H03550 and NSERC Discovery Grants.

Acknowledgements

The authors would like to thank Varun Gupta for some ideas employed in the procedure Fast_Label_Trees.

Supplementary Materials

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