Characterizing pseudobase and predicting RNA secondary structure with simple H-type pseudoknots based on dynamic programming

RNA is a unique biopolymer that has the ability to store genetic information, like DNA, but also can have a functional role in the cell, like protein. The function of an RNA is determined by its sequence and structure, and the RNA structure is to a large extent determined by RNA's ability to form base pairs with itself. Most work has been done to predict structures that do not contain pseudoknots. Pseudoknots are usually excluded due to the hardness of examining all possible structures efficiently and model the energy correctly. In this paper we will present characterization of Pseudobase and then we will introduce an improved version of dynamic programming solution to find the conformation with the maximum number of base pairs. After then we will introduce an implementation of predicting H-type pseudoknots based on dynamic programming. Our algorithm called "Iterated Dynamic Programming" has better space and time complexity than the previously known algorithms. The algorithm has a worst case complexity of O(N-3) in time and O(N-2) in storage. In addition, our approach can be easily extended and applied to other classes of more general pseudoknots. Availability: The algorithm has been implemented in C++ in a program called "IDP", which is available at http://dblab.cbu.ac.kr/idp.