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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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ROC curves of different methods for the BG570 genomic dataset. The ROC curves of different methods are plotted for the BG570 dataset.
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Figure 9: ROC curves of different methods for the BG570 genomic dataset. The ROC curves of different methods are plotted for the BG570 dataset.

Mentions: Table 4 shows the AC measure of our proposed method in addition to the other tested methods. At a sensitivity of 80%, the AC measure for the proposed method is 40% in the BG570 database, while that of TDP (yielding the highest AC of the established methods) is 31%. Finally, from Figures 8 and 9, illustrating the ROC's of the proposed and other methods, it is obvious that the proposed method's area under curve in both datasets is the highest of all the tested methods. This implies that our proposed algorithm is superior to the other methods at identifying exonic gene regions.


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)

ROC curves of different methods for the BG570 genomic dataset. The ROC curves of different methods are plotted for the BG570 dataset.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: ROC curves of different methods for the BG570 genomic dataset. The ROC curves of different methods are plotted for the BG570 dataset.
Mentions: Table 4 shows the AC measure of our proposed method in addition to the other tested methods. At a sensitivity of 80%, the AC measure for the proposed method is 40% in the BG570 database, while that of TDP (yielding the highest AC of the established methods) is 31%. Finally, from Figures 8 and 9, illustrating the ROC's of the proposed and other methods, it is obvious that the proposed method's area under curve in both datasets is the highest of all the tested methods. This implies that our proposed algorithm is superior to the other methods at identifying exonic gene regions.

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