RNA Secondary Structure Prediction with Simple Pseudoknots Based on Dynamic Programming.
ABSTRACT RNA molecules are sequences of nucleotides that serve as more than mere intermediaries between DNA and proteins, e.g. as catalytic
molecules. The sequence of nucleotides of an RNA molecule constitutes its primary structure, and the pattern of pairing between
nucleotides determines the secondary structure of an RNA. Computational prediction of RNA secondary structure is among the
few structure prediction problems that can be solved satisfactory in polynomial time. Most work has been done to predict structures
that do not contain pseudoknots. Pseudoknots have generally been excluded from the prediction of RNA secondary structures
due to its difficulty in modelling. In this paper, we present a computation the maximum number of base pairs of an RNA sequence
with simple pseudoknots. Our approach is based on pseudoknot technique proposed by Akutsu. We show that a structure with the
maximum possible number of base pairs could be deduced by a improved Nussinov’s trace-back procedure. In our approach we also
considered wobble base pairings (G·U). We introduce an implementation of RNA secondary structure prediction with simple pseudoknots
based on dynamic programming algorithm. To evaluate our method we use the 15 sequences with simple pseudoknots of variable
size from 19 to 25 nucleotides. We get our experimental data set from PseudoBase. Our program predicts simple pseudoknots
with correct or almost correct structure for 53% sequences.
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ABSTRACT: 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(N3) in time and O(N2) 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.Advanced Data Mining and Applications, Third International Conference, ADMA 2007, Harbin, China, August 6-8, 2007, Proceedings; 01/2007
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ABSTRACT: Ribonucleic acid (RNA) plays a fundamental and important role in cellular life forms and their function is directly related to their structure. RNA secondary structure prediction is a significant area of study for many scientists seeking insights into potential drug interactions or innovative new treatment methodologies. Predicting structure can overcome many issues related with physical structure determination and their study yields information useful in prediction of the full three dimensional structures and also in the interpretation of the biochemical abilities of the molecules. Therefore, predicting the secondary structure of RNA is very important for understanding their function. Furthermore, secondary structures are discrete and thus, well suited for computational methods. In this paper, we present a review on RNA secondary structure prediction using Dynamic Programming (DP) algorithm. An analysis of DP algorithm from previous work is discussed. We will present our proposed work for RNA secondary structure prediction using DP algorithm and special-purposed hardware of multicore and Graphical Processing Unit (GPU). We proposed to implement the DP algorithm approach on a hybrid of multicore and GPU platform to speed up the computational process of RNA secondary structure prediction.