Selecting optimal partitioning schemes for phylogenomic datasets

Robert Lanfear, Brett Calcott, David Kainer, Christoph Mayer, Alexandros Stamatakis

Research output: Contribution to journalArticlepeer-review

505 Citations (Scopus)
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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.
Original languageEnglish
Article number82
Pages (from-to)82-1-82-14
Number of pages14
JournalBMC Evolutionary Biology
Publication statusPublished - 17 Apr 2014
Externally publishedYes

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Model selection
  • Partitioning
  • Partitionfinder
  • BIC
  • AICc
  • AIC
  • Phylogenetics
  • Phylogenomics
  • Clustering
  • Hierarchical clustering


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