Pseudoknots prediction on RNA secondary structure using term rewriting

Abstract

The presences of Pseudoknots generate computational complexities during RNA (Ribonucleic Acid) secondary structure analysis. It is a well known NP hard problem in computational system. It is very essential to have an automated algorithm based system to predict the Pseudoknots from billions of data set. RNA plays a vital role in meditation of cellular information transfer from genes to functional proteins. Pseudoknots are seldom repeated forms that produce misleading computational cost and memory. Memory reducing under bloom filter proposes a memory efficient algorithm for prediction Pseudoknot of RNA secondary structure. RNA Pseudoknot structure prediction based on bloom filter rather than dynamic programming and context free grammar. At first, Structure Rewriting (SR) technique is used to represent secondary structure. Secondary structure is represented in dot bracket representation. Represented secondary structure is separated into two portions to reduce structural complexity. Dot bracket is placed into bloom filter for finding Pseudoknot. In bloom filter, hashing table is used to occupy the RNA based nucleotide. Our proposed algorithm experiences on 105 Pseudoknots in pseudobase and achieves accuracy 66.159% to determine structure.