A digital signal processing method for gene prediction with improved noise suppression
EURASIP Journal on Applied Signal Processing
Identification of Protein Coding Regions Using the Modified Gabor-Wavelet Transform
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An efficient sliding window strategy for accurate location of eukaryotic protein coding regions
Computers in Biology and Medicine
Identification of protein coding regions using antinotch filters
Digital Signal Processing
An Adaptive Window Length Strategy for Eukaryotic CDS Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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.