An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements

Asher Moshe, Elya Wygoda, Noa Ecker, Gil Loewenthal, Oren Avram, Omer Israeli, Einat Hazkani-Covo, Itsik Pe'er, Tal Pupko

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalMolecular Biology and Evolution
Volume39
Issue number11
DOIs
StatePublished - 3 Nov 2022

Keywords

  • approximate Bayesian computation
  • genome evolution
  • genome rearrangement

Fingerprint

Dive into the research topics of 'An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements'. Together they form a unique fingerprint.

Cite this