Pseudoknot Identification through Learning TAGRNA

Authors:
Sahar Al Seesi;Sanguthevar Rajasekaran;Reda Ammar
Affiliations:
Computer Science and Engineering Department, University of Connecticut,;Computer Science and Engineering Department, University of Connecticut,;Computer Science and Engineering Department, University of Connecticut,
Venue:
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2008

Citing 7
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Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots

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Pattern Discovery in Biosequences

ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Some computational properties of Tree Adjoining Grammars

ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Grammatical Inference in Bioinformatics

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Tree adjunct grammars

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RNA Pseudoknot Folding through Inference and Identification Using TAGRNA

BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Improved Algorithms for Parsing ESLTAGs: A Grammatical Model Suitable for RNA Pseudoknots

ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Improved Algorithms for Parsing ESLTAGs: A Grammatical Model Suitable for RNA Pseudoknots

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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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 TAGRNAinference 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.