Selecting optimal partitioning schemes for phylogenomic datasets
Open accessAbstract
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.
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Standard phylogenomic analyses typically partition datasets by genes or sets of genes, with each partition having a separate model, rather than applying a single site-homogeneous model to an entire supermatrix.
“For example, it has been pointed out11,12,34 that standard phylogenomic analyses do not typically apply a single site-homogeneous model to an entire supermatrix, but rather partition datasets by genes, or by sets of genes (as merged under an optimality criterion)36,37, with each partition having a separate model.”
From Evidence for sponges as sister to all other animals from partitioned phylogenomics with mixture models and recoding (2021) · cites this paper as [36]
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- Authors
- Lanfear, R., Calcott, B., Kainer, D., Mayer, C., & Stamatakis, A.
- Affiliations
- Australian National University (AU)
- Zoological Research Museum Alexander Koenig (DE)
- Karlsruhe Institute of Technology (DE)
- Corresponding author
- Lanfear, Robert
- Venue
- BMC Evolutionary Biology
- Published
- 2014
- Volume
- 14
- Issue
- 1
- Pages
- 82
- ISSN
- 1471-2148 (ISSN-L)
- PMC
- PMC4012149
- OpenAlex
- W2029226703
- Cited by
- 762
- References
- 48
- Citations by year
- 2026: 40 · 2025: 50 · 2024: 63 · 2023: 52 · 2022: 57 · 2021: 98 · 2020: 79 · 2019: 71 · 2018: 71 · 2017: 76 · 2016: 58 · 2015: 41 · 2014: 6
- FWCI
- 24.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Evolutionary Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- ANU Open Research (Australian National University) (submittedVersion)
- Funders
- National Evolutionary Synthesis Center
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
- Keywords
- partitioning schemes, phylogenomics, hierarchical clustering, PartitionFinder, AICc, BIC
- MeSH terms
- Algorithms, Phylogeny, Software, Cluster Analysis, Evolution, Molecular, Genomics
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