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Identification of exonic regions in DNA sequences using cross-correlation and noise suppression by discrete wavelet transform.

Abbasi O, Rostami A, Karimian G - BMC Bioinformatics (2011)

Bottom Line: The method reduces the dependency of window length on identification accuracy.The proposed method increased the accuracy of exon detection by 4% to 41% relative to the most common digital signal processing methods for exon prediction.In addition, discrete wavelet transform (DWT) can minimise noise while maintaining the signal.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Engineering-Emerging Technologies, University of Tabriz, Tabriz 5166614761, Iran.

ABSTRACT

Background: The identification of protein coding regions (exons) in DNA sequences using signal processing techniques is an important component of bioinformatics and biological signal processing. In this paper, a new method is presented for the identification of exonic regions in DNA sequences. This method is based on the cross-correlation technique that can identify periodic regions in DNA sequences.

Results: The method reduces the dependency of window length on identification accuracy. The proposed algorithm is applied to different eukaryotic datasets and the output results are compared with those of other established methods. The proposed method increased the accuracy of exon detection by 4% to 41% relative to the most common digital signal processing methods for exon prediction.

Conclusions: We demonstrated that periodic signals can be estimated using cross-correlation. In addition, discrete wavelet transform (DWT) can minimise noise while maintaining the signal. The proposed algorithm, which combines cross-correlation and DWT, significantly increases the accuracy of exonic region identification.

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DWT decomposition. This figure shows the schematic of DWT decomposition at three levels. The low pass and high pass half-band filters are denoted g[n] and h[n] respectively.
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Figure 3: DWT decomposition. This figure shows the schematic of DWT decomposition at three levels. The low pass and high pass half-band filters are denoted g[n] and h[n] respectively.

Mentions: To this end, by down-sampling the output of low pass and high pass filters, samples are divided into two signals; high frequency samples (detail signals) and low frequency samples (approximation signals), each embracing half the number of samples as the original signal. Figure 3 shows this procedure operating over three levels.


Identification of exonic regions in DNA sequences using cross-correlation and noise suppression by discrete wavelet transform.

Abbasi O, Rostami A, Karimian G - BMC Bioinformatics (2011)

DWT decomposition. This figure shows the schematic of DWT decomposition at three levels. The low pass and high pass half-band filters are denoted g[n] and h[n] respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3306003&req=5

Figure 3: DWT decomposition. This figure shows the schematic of DWT decomposition at three levels. The low pass and high pass half-band filters are denoted g[n] and h[n] respectively.
Mentions: To this end, by down-sampling the output of low pass and high pass filters, samples are divided into two signals; high frequency samples (detail signals) and low frequency samples (approximation signals), each embracing half the number of samples as the original signal. Figure 3 shows this procedure operating over three levels.

Bottom Line: The method reduces the dependency of window length on identification accuracy.The proposed method increased the accuracy of exon detection by 4% to 41% relative to the most common digital signal processing methods for exon prediction.In addition, discrete wavelet transform (DWT) can minimise noise while maintaining the signal.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Engineering-Emerging Technologies, University of Tabriz, Tabriz 5166614761, Iran.

ABSTRACT

Background: The identification of protein coding regions (exons) in DNA sequences using signal processing techniques is an important component of bioinformatics and biological signal processing. In this paper, a new method is presented for the identification of exonic regions in DNA sequences. This method is based on the cross-correlation technique that can identify periodic regions in DNA sequences.

Results: The method reduces the dependency of window length on identification accuracy. The proposed algorithm is applied to different eukaryotic datasets and the output results are compared with those of other established methods. The proposed method increased the accuracy of exon detection by 4% to 41% relative to the most common digital signal processing methods for exon prediction.

Conclusions: We demonstrated that periodic signals can be estimated using cross-correlation. In addition, discrete wavelet transform (DWT) can minimise noise while maintaining the signal. The proposed algorithm, which combines cross-correlation and DWT, significantly increases the accuracy of exonic region identification.

Show MeSH