Software
WaveletQuant, an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomic analysis
- † Equal contributors
1 Systems Biology Division, Zhejiang-California Nanosystems Institute (ZCNI) of Zhejiang University, Zhejiang University Huajiachi Campus, 268 Kaixuan Road, Hangzhou 310029, China
2 Department of Mathematics, College of Science, Zhejiang University Yuquan Campus, 38 Zheda Road, Hangzhou 310027, China
3 Department of Medicinal Chemistry, University of Washington, Seattle, Washington, USA
4 The Institute for Systems Biology, Seattle, Washington, USA
5 Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA
6 Swedish Neuroscience Institute, Swedish Medical Center, Seattle, WA 98122, USA
7 Dept. of Urology, University of Washington, Seattle, WA 98195, USA
BMC Bioinformatics 2010, 11:219 doi:10.1186/1471-2105-11-219
Published: 29 April 2010Abstract
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 noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the Trans-Proteomic Pipeline (TPP), a commonly used open source proteomics analysis pipeline.
Conclusions
We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at http://systemsbiozju.org/data/WaveletQuant webcite.
