ArticleMolecular biology and evolution2022
An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements.
Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 5 citations in OpenAlex.
- Genome rearrangements as double coset Markov chains.Journal of mathematical biology · 2026Article
- Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation.Bioinformatics (Oxford, England) · 2024Article
- Statistical framework to determine indel-length distribution.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
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Abstract
The inference of genome rearrangement events has been extensively studied, as they play a major role in molecular evolution. However, probabilistic evolutionary models that explicitly imitate the evolutionary dynamics of such events, as well as methods to infer model parameters, are yet to be fully utilized. Here, we developed a probabilistic approach to infer genome rearrangement rate parameters using an Approximate Bayesian Computation (ABC) framework. We developed two genome rearrangement models, a basic model, which accounts for genomic changes in gene order, and a more sophisticated one which also accounts for changes in chromosome number. We characterized the ABC inference accuracy using simulations and applied our methodology to both prokaryotic and eukaryotic empirical datasets. Knowledge of genome-rearrangement rates can help elucidate their role in evolution as well as help simulate genomes with evolutionary dynamics that reflect empirical genomes.
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