Scalable empirical mixture models that account for across-site compositional heterogeneity
Open accessPreprint — published as 10.1093/molbev/msaa145
Abstract
Biochemical demands constrain the range of amino acids acceptable at specific sites resulting in across-site compositional heterogeneity of the amino acid replacement process. Phylogenetic models that disregard this heterogeneity are prone to systematic errors, which can lead to severe long branch attraction artifacts. State-of-the-art models accounting for across-site compositional heterogeneity include the CAT model, which is computationally expensive, and empirical distribution mixture models estimated via maximum likelihood (C10 to C60 models). Here, we present a new, scalable method EDCluster for finding empirical distribution mixture models involving a simple cluster analysis. The cluster analysis utilizes specific coordinate transformations which allow the detection of specialized amino acid distributions either from curated databases, or from the alignment at hand. We apply EDCluster to the HOGENOM and HSSP databases in order to provide universal distribution mixture (UDM) models comprising up to 4096 components. Detailed analyses of the UDM models demonstrate the removal of various long branch attraction artifacts and improved performance compared to the C10 to C60 models. Ready-to-use implementations of the UDM models are provided for three established software packages (IQ-TREE, Phylobayes, and RevBayes).
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Even more complex models (e.g. UDM models) may yet prove better fitting and more resilient to LBA.
“In fact, this high prevalence of the most complex models tested here as best fitting for single genes implies that they may still be underfitting the data (see Supplementary Note 1 for a detailed exposition on under- and overfitting in this study), and that even more complex models (e.g. UDM models77), may yet prove better fitting and more resilient to LBA.”
From Evidence for sponges as sister to all other animals from partitioned phylogenomics with mixture models and recoding (2021) · cites this paper as [77]
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GTRspmix: Capturing Heterogeneity of Exchangeabilities Across Sites to Improve Protein PhylogeneticsPreprint
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- Authors
- Schrempf, D., Lartillot, N., & Szöllősi, G.
- Affiliations
- Eötvös Loránd University (HU)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Schrempf, Dominik
- Venue
- Molecular Biology and Evolution
- Publisher
- openRxiv
- Published
- 2019-10-07
- Volume
- 37
- Issue
- 12
- Pages
- 3616-3631
- ISSN
- 0737-4038 (ISSN-L)
- PMC
- PMC7743758
- OpenAlex
- W3081516476
- Cited by
- 61
- References
- 65
- Citations by year
- 2026: 13 · 2025: 11 · 2024: 8 · 2023: 11 · 2022: 10 · 2021: 6 · 2020: 2
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Molecular Biology and Evolution (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Centre Informatique National de l’Enseignement Supérieur (A0040310449)
- Grand Équipement National De Calcul Intensif
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- amino acid substitutions, long-branch attraction, phylogenetic model selection, clustering, coordinate transformation, cat model, amino acid substitution rates
- MeSH terms
- Genetic Techniques, Models, Genetic, Phylogeny, Software, Cluster Analysis, Amino Acid Substitution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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