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A general framework for genome rearrangement with biological constraints
Pijus Simonaitis1, Annie Chateau1,2, Krister M Swenson1,2
11CNRS, LIRMM, Université Montpellier, 161 Rue Ada, 34392 Montpellier, France.
Algorithms for Molecular Biology : AMB
|July 31, 2019
Summary
This study introduces a weighted double cut and join (DCJ) model for genome rearrangement, presenting a new Minimum Cost Parsimonious Scenario (MCPS) framework. This approach handles complex genomic structures and generalizes existing computational methods.
Area of Science:
- Computational Biology
- Bioinformatics
- Genomics
Background:
- Genome rearrangement studies analyze evolutionary DNA changes.
- Existing models like double cut and join (DCJ) have limitations with biological constraints.
- Parsimony principles are crucial for inferring evolutionary scenarios.
Purpose of the Study:
- To generalize genome rearrangement studies using a weighted DCJ model.
- To introduce and solve Minimum Cost Parsimonious Scenario (MCPS) problems on labeled graphs.
- To develop a flexible framework for arbitrary genomic structures and gene content.
Main Methods:
- Development of a weighted DCJ model.
- Formulation of -MCPS optimization problems based on labeled graphs.
- Algorithm design for computing -MCPS on general genome instances (circular/linear chromosomes, arbitrary gene content).
Main Results:
- A general method for solving -MCPS problems is established.
- Polynomial-time algorithms are presented, generalizing prior work.
- The framework effectively handles complex genomic scenarios and biological constraints.
Conclusions:
- The proposed weighted DCJ and -MCPS framework offers a powerful generalization for genome rearrangement.
- This approach provides practical computational solutions for complex evolutionary inference.
- The study advances the understanding of genome evolution under various biological constraints.
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