|Bridges, Susan - MISSISSIPPI STATE UNIV|
|Magee, G - MISSISSIPPI STATE UNIV|
|Wang, Nan - MISSISSIPPI STATE UNIV|
|Burgess, Shane - MISSISSIPPI STATE UNIV|
|Nanduri, Bindu - MISSISSIPPI STATE UNIV|
Submitted to: BMC Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 16, 2007
Publication Date: November 1, 2007
Citation: Bridges, S.M., Magee, G.B., Wang, N., Williams, W.P., Burgess, S., Nanduri, B. 2007. ProtQuant: A tool for the label-free quantification of MudPIT proteomics data. BMC Bioinformatics. 8(Suppl 7):S24:1-9. Interpretive Summary: Identification of genes and proteins associated with the inheritance of a desirable trait has the potential for enhancing progress from plant and animal breeding through the use of molecular marker assisted selection. To effectively use information obtained through proteomic analyses requires computational techniques for quantifying high throughput mass spectrometry data. Two types of methods have been developed to detect differential protein expression in multidimensional protein identification experiments. The techniques described for label-free protein quantification provide a publicly available tool for proteomic analysis.
Technical Abstract: Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (SXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published SXCorr method for quantification and includes an improved method for handling missing data. ProtQuant was designed for ease of use and portability for the bench scientist. It implements the SXCorr method for label free protein quantification from MudPIT datasets. ProtQuant has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,ProtQuant implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called SXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that SXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to SXCorr. ProtQuant is a tool for protein quantification built for multi-platform use with an intuitive user interface. ProtQuant efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, ProtQuant is available as a self-installing executable for the Windows environment used by many bench scientists.