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A fast algorithm for exonic regions prediction in DNA sequences.

Saberkari H, Shamsi M, Heravi H, Sedaaghi MH - J Med Signals Sens (2013)

Bottom Line: First, the symbolic DNA sequences were converted to digital signal using the electron ion interaction potential method.Then, to reduce the effect of background noise in the period-3 spectrum, we used the discrete wavelet transform at three levels and applied it on the input digital signal.Simulation results confirmed that the proposed method can be used as a promising tool for exon prediction in DNA sequences.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

ABSTRACT
The main purpose of this paper is to introduce a fast method for gene prediction in DNA sequences based on the period-3 property in exons. First, the symbolic DNA sequences were converted to digital signal using the electron ion interaction potential method. Then, to reduce the effect of background noise in the period-3 spectrum, we used the discrete wavelet transform at three levels and applied it on the input digital signal. Finally, the Goertzel algorithm was used to extract period-3 components in the filtered DNA sequence. The proposed algorithm leads to decrease the computational complexity and hence, increases the speed of the process. Detection of small size exons in DNA sequences, exactly, is another advantage of the algorithm. The proposed algorithm ability in exon prediction was compared with several existing methods at the nucleotide level using: (i) specificity - sensitivity values; (ii) receiver operating curves (ROC); and (iii) area under ROC curve. Simulation results confirmed that the proposed method can be used as a promising tool for exon prediction in DNA sequences.

No MeSH data available.


ROC curves of the methods for gene sequence F56F11.4
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Figure 12: ROC curves of the methods for gene sequence F56F11.4

Mentions: Finally, Figures 12-15 illustrate the ROC's of the algorithms. It is obvious that the proposed algorithm has the highest value of its parameter over the other methods. By way of illustration, the area under the ROC curve is improved by the factors of 1.36, 1.84, 1.38, and 1.83 over the DFT and 2, 1.82, 1.56, and 1.25 over the MS filter methods in F56F11.4, AF009962, AF019074.1, and AJ223321.1 gene sequences, respectively. This implies that the proposed algorithm is superior to the other methods for identifying exonic gene regions.


A fast algorithm for exonic regions prediction in DNA sequences.

Saberkari H, Shamsi M, Heravi H, Sedaaghi MH - J Med Signals Sens (2013)

ROC curves of the methods for gene sequence F56F11.4
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3959004&req=5

Figure 12: ROC curves of the methods for gene sequence F56F11.4
Mentions: Finally, Figures 12-15 illustrate the ROC's of the algorithms. It is obvious that the proposed algorithm has the highest value of its parameter over the other methods. By way of illustration, the area under the ROC curve is improved by the factors of 1.36, 1.84, 1.38, and 1.83 over the DFT and 2, 1.82, 1.56, and 1.25 over the MS filter methods in F56F11.4, AF009962, AF019074.1, and AJ223321.1 gene sequences, respectively. This implies that the proposed algorithm is superior to the other methods for identifying exonic gene regions.

Bottom Line: First, the symbolic DNA sequences were converted to digital signal using the electron ion interaction potential method.Then, to reduce the effect of background noise in the period-3 spectrum, we used the discrete wavelet transform at three levels and applied it on the input digital signal.Simulation results confirmed that the proposed method can be used as a promising tool for exon prediction in DNA sequences.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

ABSTRACT
The main purpose of this paper is to introduce a fast method for gene prediction in DNA sequences based on the period-3 property in exons. First, the symbolic DNA sequences were converted to digital signal using the electron ion interaction potential method. Then, to reduce the effect of background noise in the period-3 spectrum, we used the discrete wavelet transform at three levels and applied it on the input digital signal. Finally, the Goertzel algorithm was used to extract period-3 components in the filtered DNA sequence. The proposed algorithm leads to decrease the computational complexity and hence, increases the speed of the process. Detection of small size exons in DNA sequences, exactly, is another advantage of the algorithm. The proposed algorithm ability in exon prediction was compared with several existing methods at the nucleotide level using: (i) specificity - sensitivity values; (ii) receiver operating curves (ROC); and (iii) area under ROC curve. Simulation results confirmed that the proposed method can be used as a promising tool for exon prediction in DNA sequences.

No MeSH data available.