Pseudoknot Identification through Learning TAGRNA

  • Sahar Al Seesi
  • Sanguthevar Rajasekaran
  • Reda Ammar
Conference paper

DOI: 10.1007/978-3-540-88436-1_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)
Cite this paper as:
Seesi S.A., Rajasekaran S., Ammar R. (2008) Pseudoknot Identification through Learning TAGRNA. In: Chetty M., Ngom A., Ahmad S. (eds) Pattern Recognition in Bioinformatics. PRIB 2008. Lecture Notes in Computer Science, vol 5265. Springer, Berlin, Heidelberg

Abstract

Studying the structure of RNA sequences is an important problem that helps in understanding the functional properties of RNA. Pseudoknot is one type of RNA structures that cannot be modeled with Context Free Grammars (CFG) because it exhibits crossing dependencies. Pseudoknot structures have functional importance since they appear, for example, in viral genome RNAs and ribozyme active sites. Tree Adjoining Grammars (TAG) is one example of a grammatical model that is more expressive than CFG and has the capability of dealing with crossing dependencies. In this paper, we describe a new inference algorithm for TAGRNA, a sub-model of TAG. We also introduce an RNA structure identification framework, TAGRNAInf, within which the TAGRNA inference algorithm constitutes the core of the training phase. We present the results of using the proposed framework for identifying RNA sequences with pseudoknot structures. Our results outperform those reported in [14] for the same problem that employs a different grammatical formalism.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sahar Al Seesi
    • 1
  • Sanguthevar Rajasekaran
    • 1
  • Reda Ammar
    • 1
  1. 1.Computer Science and Engineering DepartmentUniversity of Connecticut 

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