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

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء


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.

اللغة الأصليةالإنجليزيّة
رقم المقالmsac231
دوريةMolecular Biology and Evolution
مستوى الصوت39
رقم الإصدار11
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 1 نوفمبر 2022

All Science Journal Classification (ASJC) codes

  • !!Ecology, Evolution, Behavior and Systematics
  • !!Molecular Biology
  • !!Genetics


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