proteomics aims at determining the structure, function and expression of proteins. High-throughput mass spectrometry (MS) is emerging as a leading technique in the proteomics revolution. Though it can be used to find ...
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proteomics aims at determining the structure, function and expression of proteins. High-throughput mass spectrometry (MS) is emerging as a leading technique in the proteomics revolution. Though it can be used to find disease-related protein patterns in mixtures of proteins derived from easily obtained samples, key challenges remain in the processing of proteomic MS data. Multiscale mathematical tools such as wavelets play an important role in signal processing and statistical data analysis. A wavelet-based algorithm for proteomic data processing is developed. A MATLAB implementation of the software package, called WaveSpect0, is presented including processing procedures of step-interval unification, adaptive stationary discrete wavelet denoising, baseline correction using splines, normalization, peak detection, and a newly designed peak alignment method using clustering techniques. Applications to real NIS data sets for different cancer research projects in Vanderbilt Ingram Cancer Center show that the algorithm is efficient and satisfactory in NIS data mining. (c) 2007 Published by Elsevier B.V.
Mass Spectrometry (MS) is a widely used technique in molecular biology for high throughput identification and sequencing of peptides (and proteins). Tandem mass spectrometry (MS/MS) is a specialised mass spectrometry ...
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Mass Spectrometry (MS) is a widely used technique in molecular biology for high throughput identification and sequencing of peptides (and proteins). Tandem mass spectrometry (MS/MS) is a specialised mass spectrometry technique whereby the sequence of peptides can be determined. Preprocessing of the MS/MS data is indispensable before performing any statistical analysis on the data. In this work, preprocessing of MS/MS data is proposed based on the Dual Tree Complex Wavelet Transform (DTCWT) using almost symmetric Hilbert pair of wavelets. After the preprocessing step, the identification of peptides is done using the database search approach. The performance of the proposed preprocessing technique is evaluated by comparing its performance against Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT). The preprocessing performed using DTCWT identified more peptides compared to DWT and SWT. (C) 2015 Elsevier Ltd. All rights reserved.
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