The IGBB is a member of the NIH-funded Mississippi IDeA Network of Biomedical Research Excellence (Mississippi INBRE or MS-INBRE), a program designed to build biomedical infrastructure throughout the state. Through MS-INBRE, Mississippi students and faculty at Mississippi's undergraduate institutions are (1) trained in biomedical research techniques, (2) given the opportunity to work with top researchers at Mississippi's major research universities, (3) afforded access to state-of-the-art bioscience equipment, and (4) provided with assistance in preparing grant proposals. The IGBB serves as the MS-INBRE proteomics/computational biology core. For more information about MS-INBRE, click here.
Dr. Daniel G. Peterson (Dan)Director & Professor
DIRECTOR
email(662) 325-2747
Portera 131A
ProtQuant: a tool for the label-free quantification of MudPIT proteomics data
IGBB Authors:
Susan M. Bridges, Shane C. Burgess, Bindu NanduriPUBLICATION YEAR:
2007IMPACT FACTOR:
4.249CITATION COUNT:
46Bridges SM, Magee GB, Wang N, Williams WP, Burgess SC, Nanduri B (2007) ProtQuant: a tool for the label-free quantification of MudPIT proteomics data.
BMC Bioinformatics 8(Suppl 7): S24.
DOI:
10.1186/1471-2105-8-S7-S24EID:
2-s2.0-38549091691PMID: 18047724
DOWNLOAD PDFABSTRACTBACKGROUND: 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 (SigmaXCorr). 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 SigmaXCorr 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 SigmaXCorr 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 SigmaXCorr.
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