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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <b>Trans-Proteomic Pipeline (TPP)</b>, a commonly used open source proteomics analysis pipeline.</p> <p>Conclusions</p> <p>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 <url>http://systemsbiozju.org/data/WaveletQuant</url>.</p

Computational Quantification of Peptides from LC-MS data

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a major tool for the study of biological processes. High-throughput LC-MS experimentsare frequently conducted in modern laboratories, generating an enormous amountof data per day. A manual inspection is therefore no longer a feasible task. Consequently, there is a need for computational tools that can rapidly provide informationabout mass, elution time, and abundance of the compounds in a LC-MS sample. Wepresent an algorithm for the detection and quantification of peptides in LC-MS data. Our approach is flexible and independent of the MS technology in use. It is basedon a combination of the sweep line paradigm with a novel wavelet function tailoredto detect isotopic patterns of peptides. We propose a simple voting schema to usethe redundant information in consecutive scans for an accurate determination ofmonoisotopic masses and charge states. By explicitly modeling the instrument inaccuracy, we are also able to cope with data sets of different quality and resolution.We evaluate our technique on data from different instruments and show that we canrapidly estimate mass, centroid of retention time and abundance of peptides in a sound algorithmic framework. Finally, we compare the performance of our method to several other techniques on three data sets of varying complexity