Abstract

Species tree estimation from gene trees can be complicated by gene duplication and loss, and "gene tree parsimony" (GTP) is one approach for estimating species trees from multiple gene trees. In its standard formulation, the objective is to find a species tree that minimizes the total number of gene duplications and losses with respect to the input set of gene trees. Although much is known about GTP, little is known about how to treat inputs containing some incomplete gene trees (i.e., gene trees lacking one or more of the species). We present new theory for GTP considering whether the incompleteness is due to gene birth and death (i.e., true biological loss) or taxon sampling, and present dynamic programming algorithms that can be used for an exact but exponential time solution for small numbers of taxa, or as a heuristic for larger numbers of taxa. We also prove that the "standard" calculations for duplications and losses exactly solve GTP when incompleteness results from taxon sampling, although they can be incorrect when incompleteness results from true biological loss. The software for the DP algorithm is freely available as open source code at https://github.com/shamsbayzid/DynaDup.

Original languageEnglish (US)
Title of host publication17th International Workshop on Algorithms in Bioinformatics, WABI 2017
EditorsKnut Reinert, Russell Schwartz
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959770507
DOIs
StatePublished - Aug 1 2017
Event17th International Workshop on Algorithms in Bioinformatics, WABI 2017 - Boston, United States
Duration: Aug 21 2017Aug 23 2017

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume88
ISSN (Print)1868-8969

Other

Other17th International Workshop on Algorithms in Bioinformatics, WABI 2017
Country/TerritoryUnited States
CityBoston
Period8/21/178/23/17

Keywords

  • Deep coalescence
  • Gene duplication and loss
  • Gene tree parsimony

ASJC Scopus subject areas

  • Software

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