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Sorting permutations by fragmentation-weighted operations.

Alexsandro Oliveira Alexandrino1, Carla Negri Lintzmayer2, Zanoni Dias1

  • 1Institute of Computing, University of Campinas (Unicamp), Campinas, São Paulo 13083-852, Brazil.

Journal of Bioinformatics and Computational Biology
|April 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new fragmentation-based cost for genome rearrangements, offering 2-approximation algorithms for sorting permutations. This weighted approach better reflects evolutionary processes than traditional methods.

Keywords:
Approximation algorithmsdiametergenome rearrangementspermutationsreversalssorting distancetranspositions

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Genomics

Background:

  • Determining evolutionary distance between species is a key challenge in computational biology.
  • Traditional methods often simplify genome rearrangements (mutations altering large DNA segments) by assuming equal probability, focusing on minimum operation sequences.
  • Realistically, some genomic rearrangements are more probable than others, necessitating weighted approaches.

Purpose of the Study:

  • To introduce a novel cost function for genome rearrangements based on fragmentation.
  • To develop approximation algorithms for sorting permutations using this fragmentation-weighted cost.
  • To analyze the relationship between unweighted and fragmentation-weighted rearrangement problems.

Main Methods:

  • Developed a new cost function measuring the fragmentation impact of genomic rearrangements.
  • Designed and analyzed approximation algorithms for sorting permutations under fragmentation-weighted costs.
  • Investigated lower and upper bounds for fragmentation-weighted rearrangement problems, including reversals and transpositions.

Main Results:

  • Introduced fragmentation-based cost functions for genome rearrangement problems.
  • Achieved 2-approximation algorithms for five versions of the permutation sorting problem involving reversals and transpositions.
  • Provided bounds for problem diameters and improved approximation factors for simple permutations under transpositions.

Conclusions:

  • The fragmentation-weighted approach offers a more realistic model for evolutionary distances compared to unweighted methods.
  • The developed approximation algorithms provide efficient solutions for complex genome rearrangement problems.
  • This research advances the understanding of genome evolution and computational methods for analyzing genomic data.