Tree adjoining grammars for RNA structure prediction

Uemura, Yasuo; Hasegawa, Asako; Kobayashi, Satoshi; Yokomori, Takashi

doi:10.1016/s0304-3975(98)00090-5

Cited by 148 publications

(127 citation statements)

References 7 publications

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“…Since the lengths of the triple helix regions of the families RF00024 and RF01050 are similar, we put all the sequences from these two families together as set D 1 , and the other sequences as set D 2 . 7 for tertiary interactions, sensitivity 70.4 and specificity 74. 8 We use the 10-fold cross-validation approach to evaluate the accuracy of our prediction tool.…”

Section: Resultsmentioning

confidence: 99%

“…The basic idea is to start with an initial tree, and then by repeatedly replacing some internal node of the current tree with another tree, bases or base pairs can simultaneously be added to the string that the tree represents. STAG can be used to predict pseudoknotted structures [7].…”

Section: Simple Tree Adjoining Grammarmentioning

confidence: 99%

“…In practice, the project focus on restricted classes of pseudoknots that are found in nature. Examples of these prediction algorithms include [1][2][3][4][5][6][7]. All these existing methods have an assumption that each nucleotide can interact with at most one nucleotide in the RNA.…”

Section: Introductionmentioning

confidence: 99%

“…Existing grammars cannot handle standard triple helix structures. Based on the simple tree adjoining grammar (STAG) defined by [7] that can handle pseudoknots, we provide an extended version to cover the standard triple helix structures. Since STAG is an ambiguous grammar (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Local Structural Prediction Algorithm for RNA Triple Helix Structure

Hsu

Wong

Hon

et al. 2013

Pattern Recognition in Bioinformatics

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Abstract. Secondary structure prediction (with or without pseudoknots) of an RNA molecule is a well-known problem in computational biology. Most of the existing algorithms have an assumption that each nucleotide can interact with at most one other nucleotide. This assumption is not valid for triple helix structure (a pseudoknotted structure with tertiary interactions). As these structures are found to be important in many biological processes, it is desirable to develop a prediction tool for these structures. We provide the first structural prediction algorithm to handle triple helix structures. Our algorithm runs in O(n 3 ) time where n is the length of input RNA sequence. The accuracy of the prediction is reasonably high, with average sensitivity and specificity over 80% for base pairs, and over 70% for tertiary interactions.

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Section: Resultsmentioning

confidence: 99%

Section: Simple Tree Adjoining Grammarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Local Structural Prediction Algorithm for RNA Triple Helix Structure

Hsu

Wong

Hon

et al. 2013

Pattern Recognition in Bioinformatics

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“…The thermodynamic model for pseudoknot free secondary structures has been extended to include contributions of pseudoknotted stems and loops. Several algorithms have been proposed for predicting the mfe secondary structure from a class of secondary structures that allows limited types of pseudoknots [1,15,19,28]. Other algorithms are heuristic in nature, such as the genetic algorithm of Gultyaev et al [12].…”

Section: Basics On Rna Secondary Structurementioning

confidence: 99%