Autoregressive modeling of DNA features for short exon recognition

Abstract

This paper presents a new technique for the detection of short exons in DNA sequences. In this method, we analyze the DNA propeller twist and bending stiffness using the autoregressive (AR) model. The linear prediction matrices for the two features are combined to find the same set of linear prediction coefficients, from which we estimate the spectrum of the DNA sequence and detect protein-coding regions based on the 1/3 frequency component. To overcome the non-stationarity of DNA sequences, we use moving windows of different sizes in the AR model. Experiments on the human genome show that our multi-feature based method is superior in performance to existing exon detection algorithms.