‘Multi-SpaM’: A maximum-likelihood approach to phylogeny reconstruction using multiple spaced-word matches and quartet trees

Thomas Dencker, Chris André Leimeister, Michael Gerth, Christoph Bleidorn, Sagi Snir, Burkhard Morgenstern

Research output: Contribution to journalArticlepeer-review

Abstract

Word-based or ‘alignment-free’ methods for phylogeny inference have become popular in recent years. These methods are much faster than traditional, alignment-based approaches, but they are generally less accurate. Most alignment-free methods calculate ‘pairwise’ distances between nucleic-acid or protein sequences; these distance values can then be used as input for tree-reconstruction programs such as neighbor-joining. In this paper, we propose the first word-based phylogeny approach that is based on ‘multiple’ sequence comparison and ‘maximum likelihood’. Our algorithm first samples small, gap-free alignments involving four taxa each. For each of these alignments, it then calculates a quartet tree and, finally, the program ‘Quartet MaxCut’ is used to infer a super tree for the full set of input taxa from the calculated quartet trees. Experimental results show that trees produced with our approach are of high quality.

Original languageEnglish
Article numberlqz013
JournalNAR Genomics and Bioinformatics
Volume2
Issue number1
DOIs
StatePublished - 1 Mar 2020

Bibliographical note

Publisher Copyright:
© The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

ASJC Scopus subject areas

  • Applied Mathematics
  • Genetics
  • Molecular Biology
  • Structural Biology
  • Computer Science Applications

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  • Multi-SpaM: A maximum-likelihood approach to phylogeny reconstruction using multiple spaced-word matches and quartet trees

    Dencker, T., Leimeister, C. A., Gerth, M., Bleidorn, C., Snir, S. & Morgenstern, B., 2018, Comparative Genomics - 16th International Conference, RECOMB-CG 2018, Proceedings. Ouangraoua, A. & Blanchette, M. (eds.). Springer Verlag, p. 227-241 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11183 LNBI).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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