This article is part of the supplement: Proceedings of the Tenth Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics
From event-labeled gene trees to species trees
1 Max-Planck-Institute for Mathematics in the Sciences, Leipzig, D-04103, Germany
2 Bioinformatics Group, Department of Computer Science; and Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, D-04107, Germany
3 Center for Bioinformatics, Saarland University, Saarbrücken, D-66041, Germany
4 High Throughput Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller Universität Jena, Jena, D-07743, Germany
5 Parallel Computing and Complex Systems Group, Department of Computer Science, University of Leipzig, Leipzig, D04103, Germany
6 School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK
7 Inst. f. Theoretical Chemistry, University of Vienna, Vienna, A-1090, Austria
8 Santa Fe Institute, Santa Fe, NM, 87501, USA
Citation and License
BMC Bioinformatics 2012, 13(Suppl 19):S6 doi:10.1186/1471-2105-13-S19-S6Published: 19 December 2012
Tree reconciliation problems have long been studied in phylogenetics. A particular variant of the reconciliation problem for a gene tree T and a species tree S assumes that for each interior vertex x of T it is known whether x represents a speciation or a duplication. This problem appears in the context of analyzing orthology data.
We show that S is a species tree for T if and only if S displays all rooted triples of T that have three distinct species as their leaves and are rooted in a speciation vertex. A valid reconciliation map can then be found in polynomial time. Simulated data shows that the event-labeled gene trees convey a large amount of information on underlying species trees, even for a large percentage of losses.
The knowledge of event labels in a gene tree strongly constrains the possible species tree and, for a given species tree, also the possible reconciliation maps. Nevertheless, many degrees of freedom remain in the space of feasible solutions. In order to disambiguate the alternative solutions additional external constraints as well as optimization criteria could be employed.