An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements

Area of Science:

• Evolutionary biology
• Computational biology
• Genomics

Background:

• Genome rearrangement events are crucial drivers of molecular evolution.
• Probabilistic evolutionary models and parameter inference methods for these events are underutilized.
• Understanding rearrangement rates is key to deciphering evolutionary dynamics.

Purpose of the Study:

• To develop a probabilistic framework for inferring genome rearrangement rate parameters.
• To introduce two novel genome rearrangement models: one for gene order and another including chromosome number changes.
• To assess the accuracy of the developed inference method using simulations.

Main Methods:

• Utilized an Approximate Bayesian Computation (ABC) framework for parameter inference.
• Developed and implemented two distinct genome rearrangement models.
• Validated the methodology through extensive simulations on both prokaryotic and eukaryotic datasets.

Main Results:

• The developed ABC approach accurately infers genome rearrangement rate parameters.
• The models effectively capture genomic changes in gene order and chromosome number.
• Empirical data from prokaryotes and eukaryotes were successfully analyzed.

Conclusions:

• The new probabilistic method provides a robust tool for estimating genome rearrangement rates.
• This methodology advances the study of molecular evolution and genome dynamics.
• The inferred rates can improve the simulation of genomes that mirror evolutionary processes.