WaveletQuant, an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomi

Background: Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification. Results: We developed a novel discrete wavelet transform (DWT) and a 'Spatial Adaptive Algorithm' to remove noi...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Fan Mo, Qun Mo, Yuanyuan Chen, David R. Goodlett, Leroy Hood, Gilbert S. Omenn, Song Li, Biaoyang Lin
• Keywords: BMCBI 2010, Peptide Quantification, Proteomic, Quantitative Proteomic