Efficient implementation of parametric spectral estimation techniques for DNA exon prediction

This paper is mainly concerned with the application of different parametric spectral estimation techniques on deoxyribonucleic acid (DNA) sequences. The objective of this study is to allow the analysis of these sequences for useful information extraction such as exon information. It is known that the exon, if existing, is represented with a spectral peak at the normalized frequency of 0.667. A comparison study is presented between Burg, Covariance, Modified Covariance, Yule-Walker, MUltiple SIgnal Classification (MUSIC) and Auto-Regressive Moving Average (ARMA) techniques for efficient representation of DNA sequences in the frequency domain for further exon prediction. Moreover, to filter the out-of-band noise that appears in the frequency domain in the prediction process, an inverse Chebyshev bandpass filter tuned at 0.667 is utilized. The obtained results reveal the importance of bandpass filtering and ensure that Burg, Covariance and Modified Covariance techniques are the best for exon prediction with a detection range of about 60 dB.