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Bioinformatics Advance Access published online on March 22, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti385
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received December 7, 2004
Revised March 7, 2005
Accepted March 8, 2005

Article

Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures

Hiroshi Matsui 1, Kengo Sato 1, and Yasubumi Sakakibara 1*

1 Keio University, Department of Biosciences and Informatics, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan

* To whom correspondence should be addressed.
Yasubumi Sakakibara, E-mail: yasu{at}bio.keio.ac.jp


  Abstract

Motivation: Since the whole genome sequences of many species have been determined, computational prediction of RNA secondary structures and computational identification of those non-coding RNA regions by comparative genomics become important. Therefore, more advanced alignment methods are required. Recently, an approach of structural alignment for RNA sequences has been introduced to solve these problems. Pair HMMs on tree structures (PHMMTSs) proposed by Sakakibara are efficient automata-theoretic models for structural alignment of RNA secondary structures, although PHMMTSs are incapable of handling pseudoknots. On the other hand, tree adjoining grammars (TAGs), a subclass of context sensitive grammars, are suitable for modeling pseudoknots. Our goal is to extend PHMMTSs by incorporating TAGs to be able to handle pseudoknots.

Results: We propose pair stochastic tree adjoining grammars (PSTAGs) for aligning and predicting RNA secondary structures including a simple type of pseudoknots which can represent most of known pseudoknot structures. First, we extend PHMMTSs defined on alignment of "trees" to PSTAGs defined on alignment of "TAG trees" which represent derivation process of TAGs and are functionally equivalent to derived trees of TAGs. Then, we develop an efficient dynamic programming algorithm of PSTAGs for obtaining an optimal structural alignment including pseudoknots. We implement the PSTAG algorithm and demonstrate the properties of algorithm by using it to align and predict several small pseudoknot structures. We believe that our implemented program based on PSTAGs is the first grammar-based and practically executable software for comparative analyses of RNA pseudoknot structures, and further non-coding RNAs.

Availability: The source code of PSTAG and its web application are available at http://phmmts.dna.bio.keio.ac.jp/pstag.


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