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Sorting permutations by prefix and suffix rearrangements.

Carla Negri Lintzmayer1, Guillaume Fertin2, Zanoni Dias2

  • 1† Institute of Computing, University of Campinas, Campinas, São Paulo, 13083-852, Brazil.

Journal of Bioinformatics and Computational Biology
|February 17, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces new algorithms for genome rearrangement problems, specifically prefix and suffix reversals and transpositions. The research offers approximation algorithms and improved practical results for sorting genomes represented as permutations.

Keywords:
Permutationsapproximation algorithmsdiameterprefixreversalssortingsuffixtranspositions

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Combinatorics

Background:

  • Genome rearrangements are mutations impacting large genomic segments.
  • Representing genomes as permutations aids in studying these rearrangements.
  • The objective is to sort permutations using a minimum number of operations.

Purpose of the Study:

  • To analyze prefix and suffix reversals and transpositions in genome rearrangement problems.
  • To develop approximation algorithms for sorting signed and unsigned permutations.
  • To establish bounds for rearrangement distances and enhance algorithmic performance.

Main Methods:

  • Developing 2-approximation and ([Formula: see text])-approximation algorithms.
  • Analyzing prefix and suffix reversals and transpositions.
  • Investigating both unsigned and signed permutation models.
  • Calculating bounds for diameters related to these rearrangement problems.

Main Results:

  • Introduced approximation algorithms with proven performance guarantees.
  • Provided theoretical bounds for the diameter of permutation sorting under specific rearrangement operations.
  • Identified methods to enhance the practical efficiency of the developed algorithms.

Conclusions:

  • The study offers efficient algorithmic solutions for complex genome rearrangement scenarios.
  • The findings contribute to understanding the computational complexity of sorting genomes.
  • The research provides a foundation for further advancements in computational genomics and bioinformatics.