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Matching algorithms for assigning orthologs after genome duplication events.

Guillaume Fertin1, Falk Hüffner2, Christian Komusiewicz3

  • 1LS2N UMR CNRS 6004, University of Nantes, Nantes, France.

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We developed graph models to identify gene orthologs after whole-genome duplication events. Our algorithms efficiently predict biologically significant orthologs, especially when using a duplication bonus parameter.

Keywords:
Comparative genomicsGraph algorithmsNP-hard problemPlant genomicsSynteny blocks

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Whole-genome duplications complicate the identification of orthologous genes.
  • Existing methods may not fully account for the complexities of duplicated genomes.

Purpose of the Study:

  • To introduce and analyze novel graph-based models for ortholog assignment in genomes with whole-genome duplications.
  • To develop efficient algorithms for these models, considering a 'duplication bonus' parameter.

Main Methods:

  • Development of two graph-based models for ortholog assignment.
  • Algorithmic analysis, including polynomial-time solvability and NP-hardness.
  • Design of fixed-parameter algorithms for the NP-hard model.
  • Experimental evaluation on plant genome datasets.

Main Results:

  • The first model is polynomial-time solvable; the second is NP-hard but addressed with fixed-parameter algorithms.
  • The NP-hard model offers improved cluster quality over coverage.
  • Biologically significant ortholog predictions are achievable with appropriate parameter tuning.

Conclusions:

  • The proposed graph-based models and algorithms provide effective tools for ortholog assignment in duplicated genomes.
  • The 'duplication bonus' parameter is crucial for balancing prediction quality and coverage.
  • The methods demonstrate potential for biologically meaningful ortholog discovery in plant genomics.