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Detecting the Dependent Evolution of Biosequences

  • Jeremy Darot
  • Chen-Hsiang Yeang
  • David Haussler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3909)

Abstract

A probabilistic graphical model is developed in order to detect the dependent evolution between different sites in biological sequences. Given a multiple sequence alignment for each molecule of interest and a phylogenetic tree, the model can predict potential interactions within or between nucleic acids and proteins. Initial validation of the model is carried out using tRNA sequence data. The model is able to accurately identify the secondary structure of tRNA as well as several known tertiary interactions.

Keywords

Molecular Entity Nucleotide Pair Secondary Interaction Probabilistic Graphical Model tRNA 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 2006

Authors and Affiliations

  • Jeremy Darot
    • 2
    • 3
  • Chen-Hsiang Yeang
    • 1
  • David Haussler
    • 1
  1. 1.Center for Biomolecular Science and Engineering, UC Santa Cruz 
  2. 2.Department of Applied Mathematics and Theoretical PhysicsUniversity of Cambridge 
  3. 3.EMBL – European Bioinformatics Institute 

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