Abstract: The recent techniques of spectrum estimation are based on linear algebraic concepts of subspaces. In this study, the researchers have used noise subspace method for finding hidden periodicities in DNA. With the vast growth of genomic sequences, the demand to identify accurately the protein coding regions in DNA is increasingly rising. In the past, several techniques involving various cross-fields have come up, among which application of digital signal processing tools is of prime importance. It is known that coding segments have a 3-base periodicity while non-protein coding regions do not have this unique feature. One of the most important spectrum analysis technique based on the concept of subspace is the minimum norm method. The minimum norm estimator developed in this study shows sharp period-3 peaks in coding regions completely eliminating background noise. Comparison of proposed method with existing Sliding Discrete Fourier Transform (SDFT) method popularly known as periodogram has been drawn on several genes from various organisms showing that the proposed method has effective approach towards gene prediction. Resolution, quality factor, sensitivity, specificity, miss rate, wrong rate and computation time are used to establish superiority of minimum norm gene prediction method over existing method.
M. Roy and S. Barman, 2013. High Resolution Frequency Estimation by Minimum Norm Solution for Effective Gene Prediction. Journal of Engineering and Applied Sciences, 8: 198-207.