Introducing Dependencies into Alignment Analysis and Its Use for Local Structure Prediction in Proteins

  • Szymon Nowakowski
  • Krzysztof Fidelis
  • Jerzy Tiuryn
Conference paper

DOI: 10.1007/11752578_134

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3911)
Cite this paper as:
Nowakowski S., Fidelis K., Tiuryn J. (2006) Introducing Dependencies into Alignment Analysis and Its Use for Local Structure Prediction in Proteins. In: Wyrzykowski R., Dongarra J., Meyer N., Waśniewski J. (eds) Parallel Processing and Applied Mathematics. PPAM 2005. Lecture Notes in Computer Science, vol 3911. Springer, Berlin, Heidelberg

Abstract

In this paper we explore several techniques of analysing sequence alignments. Their main idea is to generalize an alignment by means of a probability distribution. The Dirichlet mixture method is used as a reference to assess new techniques. They are compared based on a cross validation test with both synthetic and real data: we use them to identify sequence-structure relationships between target protein and possible local motifs. We show that the Beta method is almost as successful as the reference method, but it is much faster (up to 17 times). MAP (Maximum a Posteriori) estimation for two PSSMs (Position Specific Score Matrices) introduces dependencies between columns of an alignment. It is shown in our experiments to be much more successful than the reference method, but it is very computationally expensive. To this end we developed its parallel implementation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Szymon Nowakowski
    • 1
  • Krzysztof Fidelis
    • 2
  • Jerzy Tiuryn
    • 1
  1. 1.Institute of InformaticsWarsaw UniversityWarszawaPoland
  2. 2.Genome CenterUniversity of California, Davis, Genome and Biomedical Sciences FacilityDavisUSA

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