title: Phylogenomic species tree estimation in the presence of incomplete lineage sorting and horizontal gene transfer.
abstract: BACKGROUND
  Species tree estimation is challenged by gene tree heterogeneity resulting from biological processes such as duplication and loss, hybridization, incomplete lineage sorting (ILS), and horizontal gene transfer (HGT). Mathematical theory about reconstructing species trees in the presence of HGT alone or ILS alone suggests that quartet-based species tree methods (known to be statistically consistent under ILS, or under bounded amounts of HGT) might be effective techniques for estimating species trees when both HGT and ILS are present.
  
  
  RESULTS
  We evaluated several publicly available coalescent-based methods and concatenation under maximum likelihood on simulated datasets with moderate ILS and varying levels of HGT. Our study shows that two quartet-based species tree estimation methods (ASTRAL-2 and weighted Quartets MaxCut) are both highly accurate, even on datasets with high rates of HGT. In contrast, although NJst and concatenation using maximum likelihood are highly accurate under low HGT, they are less robust to high HGT rates.
  
  
  CONCLUSIONS
  Our study shows that quartet-based species-tree estimation methods can be highly accurate under the presence of both HGT and ILS. The study suggests the possibility that some quartet-based methods might be statistically consistent under phylogenomic models of gene tree heterogeneity with both HGT and ILS.
authors.forename: Ruth
authors.initials: R
authors.lastname: Davidson
authors.forename: Pranjal
authors.initials: P
authors.lastname: Vachaspati
authors.forename: Siavash
authors.initials: S
authors.lastname: Mirarab
authors.forename: Tandy
authors.initials: T
authors.lastname: Warnow
mesh_terms: D019295:Computational Biology; D003198:Computer Simulation; D022761:Gene Transfer, Horizontal / Q000235:genetics*; D049810:Genetic Speciation; D016678:Genome; D008962:Models, Theoretical; D010802:Phylogeny; D011336:Probability; D012044:Regression Analysis