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

Susan M. Bridges, G. Bryce Bryce, Nan Wang, W. Paul Williams, Shane C Burgess, Bindu Nanduri

Research output: Contribution to journalArticle

43 Citations (Scopus)

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.

Original languageEnglish (US)
Article numberS24
JournalBMC Bioinformatics
Volume8
Issue numberSUPPL. 7
DOIs
StatePublished - Nov 1 2007
Externally publishedYes

Fingerprint

Proteomics
Quantification
Labels
Technology
Proteins
Protein
Peptides
Missing Data
Count
Isotope Labeling
Data handling
Differential Expression
Missing Values
Portability
Graphical User Interface
Mass Spectrometry
Graphical user interfaces
Data Management
Quantitative Analysis
False Positive

ASJC Scopus subject areas

  • Medicine(all)
  • Structural Biology
  • Applied Mathematics

Cite this

Bridges, S. M., Bryce, G. B., Wang, N., Williams, W. P., Burgess, S. C., & Nanduri, B. (2007). ProtQuant: A tool for the label-free quantification of MudPIT proteomics data. BMC Bioinformatics, 8(SUPPL. 7), [S24]. https://doi.org/10.1186/1471-2105-8-S7-S24

ProtQuant : A tool for the label-free quantification of MudPIT proteomics data. / Bridges, Susan M.; Bryce, G. Bryce; Wang, Nan; Williams, W. Paul; Burgess, Shane C; Nanduri, Bindu.

In: BMC Bioinformatics, Vol. 8, No. SUPPL. 7, S24, 01.11.2007.

Research output: Contribution to journalArticle

Bridges, Susan M. ; Bryce, G. Bryce ; Wang, Nan ; Williams, W. Paul ; Burgess, Shane C ; Nanduri, Bindu. / ProtQuant : A tool for the label-free quantification of MudPIT proteomics data. In: BMC Bioinformatics. 2007 ; Vol. 8, No. SUPPL. 7.
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