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Experimental Analysis of a New Algorithm for Partial Haplotype Completion

  • Paola Bonizzoni
  • Gianluca Della Vedova
  • Riccardo Dondi
  • Lorenzo Mariani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3515)

Abstract

This paper deals with the computational problem of inferring complete information on haplotypes from haplotypes with missing data. This problem is one of the main issues in haplotyping, as the current DNA sequencing technology often produces haplotypes with missing bases and thus the complete information on haplotypes has to be inferred through computational methods. In this paper we propose a new algorithmic approach to the problem that assumes both the Coalescent and the Minimum Entropy models and we provide an experimental analysis relating it to the previously investigated approaches. In particular, the reconstruction of a perfect phylogeny from haplotypes with missing data is addressed.

Keywords

Greedy Heuristic Average Error Rate Completion Matrix Coalescent Model Perfect Phylogeny 
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 2005

Authors and Affiliations

  • Paola Bonizzoni
    • 1
    • 3
  • Gianluca Della Vedova
    • 2
    • 3
  • Riccardo Dondi
    • 1
    • 3
  • Lorenzo Mariani
    • 1
    • 3
  1. 1.Dipartimento di InformaticaSistemistica e Comunicazione 
  2. 2.Dipartimento di Statistica 
  3. 3.Univ. Milano–BicoccaMilanoItaly

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