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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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Applying DWT to the proposed algorithm. This figure shows the results of applying DWT to the proposed algorithm for the sequence F56F11.4. (a) The output power spectrum of the proposed algorithm before DWT is applied. (b) High frequency components of level 1 DWT decomposition (detail signal). (c) Low frequency components of level 1 DWT decomposition (approximation signal).
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Figure 4: Applying DWT to the proposed algorithm. This figure shows the results of applying DWT to the proposed algorithm for the sequence F56F11.4. (a) The output power spectrum of the proposed algorithm before DWT is applied. (b) High frequency components of level 1 DWT decomposition (detail signal). (c) Low frequency components of level 1 DWT decomposition (approximation signal).

Mentions: Approximation and detail signals for the output power spectrum of the sequence F56F11.4 (GenBank access number AF099922) at positions 7021-15020 are shown in Figures 4b and 4c. By removing the detail signal and considering only the approximation signal, the extra frequencies are removed and the output power spectrum is smoothed. Therefore, the noise effect is decreased, while the accuracy of the identification is enhanced.


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)

Applying DWT to the proposed algorithm. This figure shows the results of applying DWT to the proposed algorithm for the sequence F56F11.4. (a) The output power spectrum of the proposed algorithm before DWT is applied. (b) High frequency components of level 1 DWT decomposition (detail signal). (c) Low frequency components of level 1 DWT decomposition (approximation signal).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Applying DWT to the proposed algorithm. This figure shows the results of applying DWT to the proposed algorithm for the sequence F56F11.4. (a) The output power spectrum of the proposed algorithm before DWT is applied. (b) High frequency components of level 1 DWT decomposition (detail signal). (c) Low frequency components of level 1 DWT decomposition (approximation signal).
Mentions: Approximation and detail signals for the output power spectrum of the sequence F56F11.4 (GenBank access number AF099922) at positions 7021-15020 are shown in Figures 4b and 4c. By removing the detail signal and considering only the approximation signal, the extra frequencies are removed and the output power spectrum is smoothed. Therefore, the noise effect is decreased, while the accuracy of the identification is enhanced.

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