Abstract
BackgroundPartitioning 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.MethodsWe 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.ResultsWe 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.ConclusionsThese 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.
Highlights
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
All three algorithms we discuss in this paper start with a user-defined set of data blocks, and progressively merge data blocks to improve the information-theoretic score of the partitioning scheme
We discuss algorithm performance below in two ways: in terms of the amount that they improve the score of the partitioning scheme relative to the starting scheme which has each data block assigned to an independent subset; and in terms of the percentage improvement that an algorithm achieves relative to the existing greedy algorithm in PartitionFinder
Summary
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. One of the most important aspects of model selection is to find a model that can account for variation in the substitution process among the sites of the alignment. This variation may include differences in rates of evolution, base frequencies, and substitution patterns, and the challenge is to account for all such variation found in any given dataset. Most importantly for this study, partitioning is still the most practical method with which to account for variation in rates and patterns of substitution in very large datasets. It is important that we work to ensure that partitioned models of molecular evolution are as accurate as possible, when they are applied to large datasets, and that is the focus of this study
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
