Identification of Protein Coding Regions in the Eukaryotic DNA Sequences Based on Marple Algorithm and Wavelet Packets Transform

Abstract

The identification of protein coding regions (exons) plays a critical role in eukaryotic gene structure prediction. Many techniques have been introduced for discriminating between the exons and the introns in the eukaryotic DNA sequences, such as the discrete Fourier transform (DFT) based techniques, but these DFT-based methods rapidly lose their effectiveness in the case of short DNA sequences. In this paper, a novel integrated algorithm based on autoregressive spectrum analysis and wavelet packets transform is presented to improve the efficiency and accuracy of the coding regions identification. The experimental results show that the new algorithm outperforms the conventional DFT-based approaches in improving the prediction accuracy of protein coding regions distinctly by testing GENSCAN65, HMR195, and BG570 benchmark datasets.

Efficient Adaptive Exon Prediction for DNA study using Proportionate LMS Variants

“…Currently the ensuing sequence is proper for an adaptive algorithm as a input. Four numerical gene sequences are used as adaptive filter input [15]. At present, proposed AEP based technique is functional for renovating numerical signals.…”

“…The protein coding segments accountable for coding of protein sectors are uninterrupted and elongated in archaea; prokaryotes and bacteria are the examples of prokaryotes. The genes are the consolidation of exon segments alienated by means of lengthy segments which are not involved in coding in eukaryotes [11]. The segments that are liable for protein coding are named as exons, whereas the sections not involved in coding are called as introns.…”

“…In the plot for power spectral density (PSD), a strident peak is observed at a frequency 'f' equal to 1/3 [14]. Various prediction techniques for exons are prevailing in works built on several techniques of signal processing [15] - [19]. Nevertheless, the size of the actual genomic sequence is remarkably long and changes between sequences.…”

A Comprehensive Review of Emerging Computational Methods for Gene Identification

“…Thus, the spectrum is derived from the AR model parameters and the variance of the error term. The model parameters are found by solving a set of linear equations that are obtained by minimizing the mean squared error term (the white noise power) over the data [26]. In addition, the recursive Marple algorithm [27,28] is often utilized for the order selection in autoregressive spectral analysis and estimating the power spectrum density of the AR model.…”

“…Other improvements for short exon prediction are wavelet-based DSP techniques [29,30], which can effectively reduce the noise from surrounding non-coding regions by using a wavelet packet transform [26,31].…”