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Multi-scale parametric spectral analysis for exon detection in DNA sequences based on forward-backward linear prediction and singular value decomposition of the double-base curves

机译:基于前后线性预测和双基曲线奇异值分解的DNA序列外显子检测的多尺度参数光谱分析

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摘要

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.
机译:本文提出了一种基于多尺度参数光谱分析的DNA序列外显子检测新方法。使用可变的移动窗口大小将带有奇异值分解(SVD)算法FBLP-SVD的前向后线性预测(FBLP)应用于DNA序列的双基曲线(DB曲线),以估计信号频谱多尺度。对11bp至2032bp范围内的短人类基因进行了仿真,结果表明我们提出的方法优于经典的傅里叶变换方法。事实证明,多尺度方法比使用具有固定窗口大小的单一尺度更有效。另外,我们的方法很灵活,因为它不需要训练数据。

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