Skip to Main content Skip to Navigation
New interface
Journal articles

Scalable Empirical Mixture Models That Account for Across-Site Compositional Heterogeneity

Abstract : Biochemical demands constrain the range of amino acids acceptable at specific sites resulting in acrosssite 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-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 4,096 components. Detailed analyses of the UDM models demonstrate the removal of various long-branch attraction artifacts and improved performance compared with the C10-C60 models. Ready-to-use implementations of the UDM models are provided for three established software packages (IQ-TREE, Phylobayes, and RevBayes).
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03764553
Contributor : Nicolas Lartillot Connect in order to contact the contributor
Submitted on : Tuesday, August 30, 2022 - 1:22:06 PM
Last modification on : Tuesday, October 18, 2022 - 4:31:09 AM

File

msaa145.pdf
Publisher files allowed on an open archive

Identifiers

Collections

Citation

Dominik Schrempf, Nicolas Lartillot, Gergely Szöllősi. Scalable Empirical Mixture Models That Account for Across-Site Compositional Heterogeneity. Molecular Biology and Evolution, 2020, 37, pp.3616 - 3631. ⟨10.1093/molbev/msaa145⟩. ⟨hal-03764553⟩

Share

Metrics

Record views

1

Files downloads

1