Abstract
This paper presents a new method for exon detection in DNA sequences based on multi-scale parametric spectral analysis. A forward-backward linear prediction (FBLP) with the singular value decomposition (SVD) algorithm FBLP-SVD is applied to the double-base curves (DB-curves) of a DNA sequence using a variable moving window sizes to estimate the signal spectrum at multiple scales. Simulations are done on short human genes in the range of 11bp to 2032bp and the results show that our proposed method out-performs the classical Fourier transform method. The multi-scale approach is shown to be more effective than using a single scale with a fixed window size. In addition, our method is flexible as it requires no training data.
Highlights
Genome sequences contain the genetic information of living organisms
Multi-scale parametric spectral analysis for exon detection in deoxyribonucleic acid (DNA) sequences based on forward-backward linear prediction and singular value decomposition of the doublebase curves
This paper presents a new method for exon detection in DNA sequences based on multi-scale parametric spectral analysis
Summary
Genome sequences contain the genetic information of living organisms. This information, which is common to all life, is coded in the deoxyribonucleic acid (DNA) sequences. Multi-scale parametric spectral analysis for exon detection in DNA sequences based on forward-backward linear prediction and singular value decomposition of the doublebase curves Abstract: This paper presents a new method for exon detection in DNA sequences based on multi-scale parametric spectral analysis.
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