Improved prediction of short exons via multiscale products

Abstract

Exon is an important functional region of eukaryotic DNA sequence. Prediction of exons can help to understand the structure and function of protein. However, the issue of finding an efficient technique to detect the numbers and locations of short coding sequences automatically is an unsolved problem. In this work, a short exon prediction method based on multiscale products in B-spline wavelet domain is proposed. The proposed wavelet denoising and multiscale products-based technique (WDMP) for short exons prediction have the following three features. (1) A wavelet package denoising method is applied to smooth the DNA numerical sequences. (2) A new B-spline wavelet function is designed to extract the exon features in multiscale domain, so the effect of window length is avoided. In addition, this wavelet has a higher degree of freedom for curve design. (3) We multiply the adjacent coefficients to exploit the high inter-scale correlation of the exon data, while these correlation features are used to separate the exon signals from background noise. Compared with four well-known model-independent methods, case studies demonstrate that the proposed WDMP method helps to improve the prediction accuracy of short exons significantly.