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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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Block diagram of the proposed algorithm. This figure shows the block diagram of the proposed algorithm designed to identify protein coding regions.
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Figure 2: Block diagram of the proposed algorithm. This figure shows the block diagram of the proposed algorithm designed to identify protein coding regions.

Mentions: Figure 2 represents these steps as a block diagram. Each step is explained in detail below:


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)

Block diagram of the proposed algorithm. This figure shows the block diagram of the proposed algorithm designed to identify protein coding regions.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Block diagram of the proposed algorithm. This figure shows the block diagram of the proposed algorithm designed to identify protein coding regions.
Mentions: Figure 2 represents these steps as a block diagram. Each step is explained in detail below:

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