Improved exon prediction with transforms by de-noising period-3 measure

Abstract

Gene finding techniques in eukaryotic cells can be divided into two categories, viz. - model-dependent and model-independent. In model-independent category, transforms are commonly used to identify exons or genes present in DNA sequences. In this work, a Post-Processing Algorithm (PPA) for enhancing gene prediction features of transforms is developed. PPA compares the N/3 spectral components of DNA signal with the corresponding spectrum of period-3 suppressed DNA signal. In the N/3 spectrum of DNA sequences, the bases for which the difference between these two spectrums is within a predefined threshold level are marked as non-coding (introns) regions. In such regions the signal values are replaced by the difference signal of the two spectrums. This substitution suppresses the noise in the intronic regions of the N/3 spectrum; while the coding region (exonic) signals are not affected, resulting in de-noised period-3 measures. PPA has been applied to process the period-3 coefficients of Discrete Fourier Transform (DFT), Paired Spectral Content (PSC), and Modified Gabor Wavelet Transform (MGWT) methods to de-noise their period-3 measures. Performance of the algorithm has been evaluated on HMR195, Burset/Guigo570, and Asp67 datasets using Receiver Operating Characteristic (ROC) and specificity versus sensitivity curves. The PPA, while preserving the model-independent characteristic of transform based methods, improves the probability of correct prediction of the exonic regions.