RNA Pseudoknot Folding through Inference and Identification Using TAGRNA

Studying the structure of RNA sequences is an important problem that helps in understanding the functional properties of RNA. After being ignored for a long time due to the high computational complexity it requires, pseudoknot is one type of RNA structures that has been given a lot of attention lately. Pseudoknot structures have functional importance since they appear, for example, in viral genome RNAs and ribozyme active sites. In this paper, we present a folding framework, TAGRNAInf, for RNA structures that support pseudoknots. Our approach is based on learning TAGRNA grammars from training data with structural information. The inferred grammars are used to indentify sequences with structures analogous to those in the training set and generate a folding for these sequences. We present experimental results and comparisons with other known pseudoknot folding approaches.

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