Research Article

Dynamic programming for LR-PCR segmentation of bacterium genomes

  1. Rumen Andonov1,*,
  2. Dominique Lavenier1,
  3. Philippe Veber1,
  4. Nicola Yanev2

Article first published online: 24 JUN 2005

DOI: 10.1002/cpe.951

Issue

Concurrency and Computation: Practice and Experience

Concurrency and Computation: Practice and Experience

Special Issue: Third IEEE International Workshop on High Performance Computational Biology (HiCOMB 2004)

Volume 17, Issue 14, pages 1657–1668, 10 December 2005

Additional Information

How to Cite

Andonov, R., Lavenier, D., Veber, P. and Yanev, N. (2005), Dynamic programming for LR-PCR segmentation of bacterium genomes. Concurrency and Computation: Practice and Experience, 17: 1657–1668. doi: 10.1002/cpe.951

Author Information

  1. 1

    IRISA, Campis de Beaulieu, 35042 Rennes Cedex, France

  2. 2

    University of Sofia, 5 J. Bouchier str., 1126 Sofia, Bulgaria

*IRISA, Campis de Beaulieu, 35042 Rennes Cedex, France

Publication History

  1. Issue published online: 13 OCT 2005
  2. Article first published online: 24 JUN 2005
  3. Manuscript Accepted:
  4. Manuscript Revised:
  5. Manuscript Received:

Funded by

  • French–Bulgarian Exchange Research Program RILA'2003. Grant Number: 06230NM

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Keywords:

  • dynamic programming;
  • LR-PCR;
  • optimization;
  • genomics

Abstract

Bacterium genome plasticity can efficiently be studied by the long-range polymerase chain reaction (LR-PCR) technique: genomes of different strains are split into hundreds of short segments which, after LR-PCR amplification, are used to sketch profiles. The segments have to: (1) cover the entire genome; (2) overlap each other; and (3) be of nearly identical size. This paper addresses the problem of finding a list of segments satisfying these constraints ‘as much as possible’. Two algorithms based on dynamic programming approach are presented. They differ on the optimization criteria for measuring the quality of the covering. The first considers the maximal deviation of the segment lengths relatively to an ideal length. The second automatically finds a segment length that minimizes the maximal deviation. Copyright © 2005 John Wiley & Sons, Ltd.

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