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

Author:

Dencker Thomas1,Leimeister Chris-André1,Gerth Michael2,Bleidorn Christoph34,Snir Sagi5,Morgenstern Burkhard16

Affiliation:

1. Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany

2. Institute for Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, L69 7ZB Liverpool, UK

3. Department of Animal Evolution and Biodiversity, Universität Göttingen, Untere Karspüle 2, 37073 Göttingen, Germany

4. Museo Nacional de Ciencias Naturales, Spanish National Research Council (CSIC), 28006 Madrid, Spain

5. Institute of Evolution, Department of Evolutionary and Environmental Biology, University of Haifa, 199 Aba Khoushy Ave. Mount Carmel, Haifa, Israel

6. Göttingen Center of Molecular Biosciences (GZMB), Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany

Abstract

AbstractWord-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.

Funder

W Foundation

Göttingen University

Publisher

Oxford University Press (OUP)

Subject

General Medicine

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