Prediction of Eukaryotic Exons via the Singularity Detection Algorithm

ISSN: 2212-392X (Online)
ISSN: 1574-8936 (Print)

Volume 10, 5 Issues, 2015

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Current Bioinformatics

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Alessandro Giuliani
Istituto Superiore di Sanit√° (Italian NIH) Environment and Health Dept

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Prediction of Eukaryotic Exons via the Singularity Detection Algorithm

Current Bioinformatics, 9(4): 389-401.

Author(s): Jiaxiang Zhao, Xiaolei Zhang and Wei Xu.

Affiliation: College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, P.R. China.


The prediction of eukaryotic exons is an important topic in bioinformatics. In this paper, a model-independent method based on the singularity detection (SD) algorithm and the three-base periodicity has been developed for predicting exons in DNA sequences of eukaryotes. Using the HMR195 data set, BG570 data set and 200 test data as test sets, we show that, (1) In comparison with the exon prediction by nucleotide distribution (EPND), modified Gabor-wavelet transform (MGWT) and fast Fourier transform plus empirical mode decomposition (FFTEMD) method, the proposed SD method notably improves prediction accuracy of exons, especially short exons or the ability to discern two contiguous short exons disunited by a short intron; (2) The SD method also significantly enhances the performance of the noise suppression in exon prediction over all assessed model-independent methods. The performance of the SD method is evaluated in terms of the signal-to-noise, the approximate correlation, the area under the receiver operating characteristic curve and the accuracy against those of the EPND, MGWT and FFTEMD method over HMR195 data set, BG570 data set and 200 test data. Experimental results demonstrate that the SD method outperforms all assessed model-independent methods with respect to those performance parameters.


Bioinformatics, eukaryote, exon, model-independent method, singularity detection, three-base periodicity.

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Article Details

Volume: 9
Issue Number: 4
First Page: 389
Last Page: 401
Page Count: 13
DOI: 10.2174/1574893609666140702184225

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