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Escape from Parsimony of a Double-Cut-and-Join Genome Evolution Process.

Mona Meghdari Miardan1, Arash Jamshidpey2, David Sankoff1

  • 1Department of Mathematics and Statistics, University of Ottawa, Ottawa, Canada.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 3, 2023
PubMed
Summary
This summary is machine-generated.

This study models genome evolution using double-cut-and-join (DCJ) operations with time-varying weights. It finds that evolutionary processes diverge from parsimony after a specific number of steps, regardless of model restrictions.

Keywords:
Erdös–Rényi graphsbreakpoint graphdouble-cut-and-joinparsimony bindingrandom walk

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Area of Science:

  • Computational Biology
  • Evolutionary Genetics
  • Bioinformatics

Background:

  • Genome evolution involves complex rearrangements.
  • Double-cut-and-join (DCJ) operations model these rearrangements.
  • Understanding the parsimony of evolutionary trajectories is crucial.

Purpose of the Study:

  • To analyze genome evolution models using restricted and unrestricted DCJ operations.
  • To investigate how time-varying operation weights affect evolutionary paths.
  • To determine when evolutionary processes diverge from parsimonious estimates.

Main Methods:

  • Developed models for genome evolution incorporating DCJ operations with dynamic weights.
  • Compared the number of evolutionary operations with DCJ distance at each step.
  • Adapted Berestycki and Durrett's method to approximate breakpoint graph cycles using random graph components.

Main Results:

  • Both restricted and unrestricted DCJ models were analyzed.
  • Models allow different DCJ operation types (reversals, translocations, etc.) to have fluctuating weights.
  • The number of operations diverges from the DCJ distance, indicating a departure from parsimony.
  • The evolutionary process is bound to its parsimonious estimate for up to O(n) steps in both models.

Conclusions:

  • Genome evolution models with dynamic DCJ operation weights provide insights into parsimony divergence.
  • The approximation using random graph components is effective for analyzing evolutionary trajectories.
  • Evolutionary processes deviate from parsimony after a predictable number of steps, bounded by genome size.