A rule-based approach for RNA pseudoknot prediction
International Journal of Data Mining and Bioinformatics
Fixed Parameter Tractable Alignment of RNA Structures Including Arbitrary Pseudoknots
CPM '08 Proceedings of the 19th annual symposium on Combinatorial Pattern Matching
On the Generative Power of Multiple Context-Free Grammars and Macro Grammars
IEICE - Transactions on Information and Systems
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
Efficient alignment of RNAs with pseudoknots using sequence alignment constraints
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Stochastic multiple context-free grammar for RNA pseudoknot modeling
TAGRF '06 Proceedings of the Eighth International Workshop on Tree Adjoining Grammar and Related Formalisms
Stability of RNA structural motifs and its influence on editing efficiency by adenosine deaminases
International Journal of Bioinformatics Research and Applications
Stem kernels for RNA sequence analyses
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Improved Algorithms for Parsing ESLTAGs: A Grammatical Model Suitable for RNA Pseudoknots
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A probabilistic model for sequence alignment with context-sensitive indels
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Memory Efficient Algorithms for Structural Alignment of RNAs with Pseudoknots
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Structural alignment of pseudoknotted RNA
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
An Efficient Alignment Algorithm for Searching Simple Pseudoknots over Long Genomic Sequence
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A local structural prediction algorithm for RNA triple helix structure
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
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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 hidden Markov models 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 TAGs (PSTAGs) for aligning and predicting RNA secondary structures including a simple type of pseudoknot which can represent most known pseudoknot structures. First, we extend PHMMTSs defined on alignment of 'trees' to PSTAGs defined on alignment of 'TAG trees' which represent derivation processes 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 the 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/ Contact: yasu@bio.keio.ac.jp