ArticleMolecular biology and evolution2014
A generalized mechanistic codon model.
Article in Molecular biology and evolution, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Toward a Semi-Supervised Learning Approach to Phylogenetic Estimation.Systematic biology · 2024Article
- Estimating contact network properties by integrating multiple data sources associated with infectious diseases.Statistics in medicine · 2023Article
- Next-generation development and application of codon model in evolution.Frontiers in genetics · 2023Review
- Women in the European Virus Bioinformatics Center.Viruses · 2022Article
- An Analysis of Methylome Evolution in Primates.Molecular biology and evolution · 2021Article
- Extra base hits: Widespread empirical support for instantaneous multiple-nucleotide changes.PloS one · 2021Article
- DGINN, an automated and highly-flexible pipeline for the detection of genetic innovations on protein-coding genes.Nucleic acids research · 2020Article
- Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril.Molecular biology and evolution · 2020Article
- FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences.Bioinformatics (Oxford, England) · 2019Article
- Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates.BMC evolutionary biology · 2019Article
- Multiple Sequence Alignment Averaging Improves Phylogeny Reconstruction.Systematic biology · 2019Article
- Big data analysis of human mitochondrial DNA substitution models: a regression approach.BMC genomics · 2018Article
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3 authors.
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Abstract
Models of codon evolution have attracted particular interest because of their unique capabilities to detect selection forces and their high fit when applied to sequence evolution. We described here a novel approach for modeling codon evolution, which is based on Kronecker product of matrices. The 61 × 61 codon substitution rate matrix is created using Kronecker product of three 4 × 4 nucleotide substitution matrices, the equilibrium frequency of codons, and the selection rate parameter. The entities of the nucleotide substitution matrices and selection rate are considered as parameters of the model, which are optimized by maximum likelihood. Our fully mechanistic model allows the instantaneous substitution matrix between codons to be fully estimated with only 19 parameters instead of 3,721, by using the biological interdependence existing between positions within codons. We illustrate the properties of our models using computer simulations and assessed its relevance by comparing the AICc measures of our model and other models of codon evolution on simulations and a large range of empirical data sets. We show that our model fits most biological data better compared with the current codon models. Furthermore, the parameters in our model can be interpreted in a similar way as the exchangeability rates found in empirical codon models.
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