Modelling-Alignment for Non-random Sequences

  • David R. Powell
  • Lloyd Allison
  • Trevor I. Dix
Conference paper

DOI: 10.1007/978-3-540-30549-1_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3339)
Cite this paper as:
Powell D.R., Allison L., Dix T.I. (2004) Modelling-Alignment for Non-random Sequences. In: Webb G.I., Yu X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science, vol 3339. Springer, Berlin, Heidelberg

Abstract

Populations of biased, non-random sequences may cause standard alignment algorithms to yield false-positive matches and false-negative misses. A standard significance test based on the shuffling of sequences is a partial solution, applicable to populations that can be described by simple models. Masking-out low information content intervals throws information away. We describe a new and general method, modelling-alignment: Population models are incorporated into the alignment process, which can (and should) lead to changes in the rank-order of matches between a query sequence and a collection of sequences, compared to results from standard algorithms. The new method is general and places very few conditions on the nature of the models that can be used with it. We apply modelling-alignment to local alignment, global alignment, optimal alignment, and the relatedness problem.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • David R. Powell
    • 1
    • 2
  • Lloyd Allison
    • 1
  • Trevor I. Dix
    • 1
    • 2
  1. 1.School of Computer Science and Software EngineeringMonash UniversityAustralia
  2. 2.Victorian Bioinformatics Consortium 

Personalised recommendations