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An Improved Algorithm for Generalized Comparison of Minisatellites

  • Behshad Behzadi
  • Jean-Marc Steyaert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2676)

Abstract

One of the most important objects in genetic mapping and forensic studies are minisatellites. They consist of a heterogeneous tandem array of short repeat units called variants. The evolution of minisatellites is realized by tandem duplication and tandem deletion of variants. Jeffrey et al. proposed a method to obtain the sequence of variants, called maps. Bérard and Rivals designed the first algorithm of comparison of two minisatellite maps under an evolutionary model including deletion, insertion, mutation, amplification and contraction. The complexity of this first algorithm was O(n 4) in time and O(n 3) in space where n is the size of the maps. In this paper we propose a more efficient algorithm using the same evolutionary model which is O(n 3) in time and O(n 2) in space. Our algorithm with this better efficiency can even solve generalized and more refined models.

Keywords

Edit Distance Generalize Comparison Improve Algorithm Tree Representation Transformation Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Behshad Behzadi
    • 1
  • Jean-Marc Steyaert
    • 1
  1. 1.LIXEcole PolytechniquePalaiseau cedexFrance

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