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• W2088127264 abstract "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." @default.
• W2088127264 created "2016-06-24" @default.
• W2088127264 creator A5019366731 @default.
• W2088127264 creator A5042333849 @default.
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• W2088127264 date "2011-11-03" @default.
• W2088127264 modified "2023-10-18" @default.
• W2088127264 title "Identification of exonic regions in DNA sequences using cross-correlation and noise suppression by discrete wavelet transform" @default.
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• W2088127264 doi "https://doi.org/10.1186/1471-2105-12-430" @default.
• W2088127264 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3306003" @default.
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• W2088127264 hasPublicationYear "2011" @default.
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