Autoregressive modeling and feature analysis of DNA sequences

Authors:
Niranjan Chakravarthy;A. Spanias;L. D. Iasemidis;K. Tsakalis
Affiliations:
Department of Electrical Engineering, Arizona State University, Tempe, AZ;Department of Electrical Engineering, Arizona State University, Tempe, AZ;Harrington Department of Bioengineering, Arizona State University, Tempe, AZ;Department of Electrical Engineering, Arizona State University, Tempe, AZ
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

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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 indicates 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 the proposed method is demonstrated.