Selecting optimal partitioning schemes for phylogenomic datasets

By Robert Lanfear, B. Calcott, David Kainer, Christoph Mayer, Alexandros Stamatakis. BMC Evolutionary Biology, 2014

700 citations recorded by the source corpus.

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. 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. 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. 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.

Subfields: Genetics, Molecular Biology, Paleontology

View Paper: https://www.semanticscholar.org/paper/49190cfdec90c46617367ed364be116cd838bc75

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Citations in the corpus

Major clades and a revised classification of Magnolia and Magnoliaceae based on whole plastid genome sequences via genome skimming (2020, methodology): “, 2012) and rcluster (Lanfear et al., 2014) algorithm option for the whole plastome and plastid CDS, respectively, with prior defined data blocks by codon positions of each protein‐coding gene and all models.”
Owlflies are derived antlions: anchored phylogenomics supports a new phylogeny and classification of Myrmeleontidae (Neuroptera) (2018, methodology): “The concatenated nucleotide alignment was initially partitioned by locus, and these partitions were then merged using the rcluster search algorithm (Lanfear et al., 2014).”
Evolutionary History of the Hymenoptera. (2017, methodology): “0pre10) [33] to simultaneously infer a partition scheme and proper amino acid substitution models for analyzing each partition with the rcluster algorithm.”
PartitionFinder 2: New Methods for Selecting Partitioned Models of Evolution for Molecular and Morphological Phylogenetic Analyses. (2016, methodology): “First, we wrote faster versions of the k -means, relaxed-clustering, and greedy algorithms (Lanfear et al. 2014; Frandsen et al. 2015), although we urge caution with relying on purely data-driven approaches to partitioning such as k -means, because we still lack evidence that they perform…”
Using PhyloSuite for molecular phylogeny and tree‐based analyses (2023, unknown): “This can reduce over ‐ parameterization and increase the model fit [36, 42 – 44]. c. rcluster: specify the percentage of partition schemes analyzed by the relaxed clustering algorithm to reduce computational burden and speed up the analysis [45].”
Evolution of lacewings and allied orders using anchored phylogenomics (Neuroptera, Megaloptera, Raphidioptera) (2018, methodology): “, 2016) using the rcluster search algorithm (Lanfear et al., 2014) (Tables S6–S7).”