Improved filtering approach for identification of protein-coding regions in eukaryotes by background noise reduction using S–G filter

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Unlike prokaryotic genomes, the arrangement of protein-coding regions in eukaryotic genomes is not continuous. It is interrupted by the non-coding DNA sequences called introns. Therefore, the identification of protein-coding regions in a eukaryotic DNA sequence is one of the challenging research issues in bioinformatics. Signal processing-based computational tools such as short-time discreet Fourier transforms (STDFT) and narrow bandpass digital filters has been successfully used to resolve this problem. Filtering techniques are popularly used because of its faster response than the transform techniques. However, the prediction accuracy of the filtering approach is still limited due to background noise present in its spectrum. Background noise masks the discriminative three base periodicity (TBP) features and increases the chances of false prediction. Several de-noising techniques have been proposed so far. Recently, second-order moving average filter has been used to diminish the effect of background noise. However, the problem with moving average filter is that it operates similarly on coding and non-coding regions, and therefore, along with noise reduction, it also affects the spectral features of protein-coding regions which lead to inaccurate prediction results. In this work, we used the Savitzky–Golay (SG) filter for background noise reduction and compared the performance with other existing de-noising techniques. S–G filter works on the local least-squares polynomial approximation principal and act as a weighted moving average filter. This investigation shows that along with noise reduction, S–G filter can preserve the spectral values of coding regions with a greater extent, and therefore, provide more accurate prediction results than other de-noising techniques.