This article is part of the supplement: Proceedings of the Fourth Annual MCBIOS Conference. Computational Frontiers in Biomedicine

Proceedings

ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

Susan M Bridges^1,4 , G Bryce Magee^1,4 , Nan Wang^1,4 , W Paul Williams^3,4 , Shane C Burgess^{* 2,4,5} and Bindu Nanduri^{* 2,4}

¹Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, 39762, USA

²College of Veterinary Medicine, Mississippi State University, Starkville, MS, 39762, USA

³USDA ARS Corn Host Plant Resistance Research Unit, Mississippi State University, Starkville, MS, 39762, USA

⁴Institute for Digital Biology, Mississippi State University, Starkville, MS, 39762, USA

⁵Mississippi Agriculture and Forestry Experiment Station, Mississippi State University, Starkville, MS, 39762, USA

author email corresponding author email* Contributed equally

BMC Bioinformatics 2007, 8(Suppl 7):S24doi:10.1186/1471-2105-8-S7-S24

Published:	1 November 2007

Abstract

Background

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 (ΣXCorr). 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 ΣXCorr method for quantification and includes an improved method for handling missing data.

Results

ProtQuant was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr 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 ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.

Conclusion

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.