Robust smoothing of quantitative genomic data using second-generation wavelets and bivariate shrinkage

IEEE Transactions on Bio-medical Engineering
H Hatsuda

Abstract

Recent high-throughput nucleotide sequencing technologies provide large amounts of quantitative genomic data, and thus, biologists currently need to process vast quantities of the data on a regular basis. The first step of the process is almost always smoothing of the data because biomedical data generally tend to contain a lot of noise. In this first step, classical wavelet transforms are widely used; however, the second-generation wavelet transform has not been used in biomedical studies. Smoothing based on the second-generation wavelets is more effective than classical wavelets-based methods because it employs data-dependent wavelet functions and does not require predefined explicit base functions. Since biomedical data usually lack regularity, it is more useful in biomedical research to use the second-generation wavelets than to use the classical wavelets. Therefore, we propose a novel smoothing method based on the second-generation wavelets and bivariate shrinkage, which enables to determine robust thresholds for wavelet-based smoothing, and apply it to synthetic and real genomic data. Experimental results demonstrate the effectiveness of the proposed method.

References

Dec 31, 2005·Nucleic Acids Research·Hideya KawajiYoshihide Hayashizaki
Mar 27, 2007·Bioinformatics·Nathan DayWilliam S Noble
Jan 19, 2010·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Nha NguyenAn Vo

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