EURASIP Journal on Applied Signal Processing
Volume 2004 (2004), Issue 1, Pages 13-28
doi:10.1155/S111086570430925X

Autoregressive Modeling and Feature Analysis of DNA Sequences

Niranjan Chakravarthy,1 A. Spanias,1 L. D. Iasemidis,2 and K. Tsakalis1

1Department of Electrical Engineering, Arizona State University, Tempe 85287-5706, AZ, USA
2Harrington Department of Bioengineering, Arizona State University, Tempe 85287-9709, AZ, USA

Received 28 February 2003; Revised 15 September 2003

Copyright © 2004 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A parametric signal processing approach for DNA sequence analysis based on autoregressive (AR) modeling is presented. AR model residual errors and AR model parameters are used as features. The AR residual error analysis indicate a high specificity of coding DNA sequences, while AR feature-based analysis helps distinguish between coding and noncoding DNA sequences. An AR model-based string searching algorithm is also proposed. The effect of several types of numerical mapping rules in th proposed method is demonstrated.