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Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Quantitation and Analysis of the Formation of HO-Endonuclease Stimulated Chromosomal Translocations by Single-Strand Annealing in Saccharomyces cerevisiae
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A general heuristic for genome rearrangement problems.

Ulisses Dias1, Gustavo Rodrigues Galvão, Carla Négri Lintzmayer

  • 1Institute of Computing - University of Campinas, Av. Albert Einstein, 1251, Campinas/SP, Brazil.

Journal of Bioinformatics and Computational Biology
|June 28, 2014
PubMed
Summary

This study introduces a novel heuristic to enhance genome rearrangement algorithms. The heuristic improves solutions from existing non-optimal methods, achieving top practical results across multiple genome rearrangement problems.

Keywords:
Genome rearrangementgeneral heuristicsorting by reversalssorting by transpositions

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Genome rearrangement problems are central to comparative genomics.
  • Existing algorithms often provide non-optimal solutions.
  • Improving these solutions is crucial for understanding genome evolution.

Purpose of the Study:

  • To present a general heuristic for improving genome rearrangement algorithms.
  • To evaluate the heuristic's performance across various genome rearrangement problems.
  • To provide a benchmark for future research in the field.

Main Methods:

  • Implementation of 23 algorithms for 9 genome rearrangement problems.
  • Development and application of a novel general heuristic.
  • Comparative analysis of implemented algorithms with and without the heuristic.

Main Results:

  • The heuristic significantly improved practical results for multiple genome rearrangement problems.
  • Best practical results were achieved for sorting by transpositions.
  • Source codes and benchmarks are available for reproducibility and comparison.

Conclusions:

  • The proposed heuristic is effective in enhancing solutions for genome rearrangement problems.
  • The heuristic offers a practical approach to achieving near-optimal solutions.
  • Open availability of code and data facilitates further advancements in the field.