title: Selecting optimal partitioning schemes for phylogenomic datasets.
abstract: BACKGROUND
  Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics.
  
  
  METHODS
  We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere.
  
  
  RESULTS
  We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores.
  
  
  CONCLUSIONS
  These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets.
authors.affiliation: Ecology Evolution and Genetics, Research School of Biology, Australian National University, Canberra, ACT, Australia. rob.lanfear@gmail.com.
authors.forename: Robert
authors.initials: R
authors.lastname: Lanfear
authors.forename: Brett
authors.initials: B
authors.lastname: Calcott
authors.forename: David
authors.initials: D
authors.lastname: Kainer
authors.forename: Christoph
authors.initials: C
authors.lastname: Mayer
authors.forename: Alexandros
authors.initials: A
authors.lastname: Stamatakis
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019143:Evolution, Molecular; D023281:Genomics; D010802:Phylogeny; D012984:Software
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