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ProtQuant: a tool for the label-free quantification of MudPIT proteomics data
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)...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2099493/ https://www.ncbi.nlm.nih.gov/pubmed/18047724 http://dx.doi.org/10.1186/1471-2105-8-S7-S24 |
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author | Bridges, Susan M Magee, G Bryce Wang, Nan Williams, W Paul Burgess, Shane C Nanduri, Bindu |
author_facet | Bridges, Susan M Magee, G Bryce Wang, Nan Williams, W Paul Burgess, Shane C Nanduri, Bindu |
author_sort | Bridges, Susan M |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2099493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-20994932007-12-01 ProtQuant: a tool for the label-free quantification of MudPIT proteomics data Bridges, Susan M Magee, G Bryce Wang, Nan Williams, W Paul Burgess, Shane C Nanduri, Bindu BMC Bioinformatics Proceedings 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. BioMed Central 2007-11-01 /pmc/articles/PMC2099493/ /pubmed/18047724 http://dx.doi.org/10.1186/1471-2105-8-S7-S24 Text en Copyright © 2007 Bridges et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Bridges, Susan M Magee, G Bryce Wang, Nan Williams, W Paul Burgess, Shane C Nanduri, Bindu ProtQuant: a tool for the label-free quantification of MudPIT proteomics data |
title | ProtQuant: a tool for the label-free quantification of MudPIT proteomics data |
title_full | ProtQuant: a tool for the label-free quantification of MudPIT proteomics data |
title_fullStr | ProtQuant: a tool for the label-free quantification of MudPIT proteomics data |
title_full_unstemmed | ProtQuant: a tool for the label-free quantification of MudPIT proteomics data |
title_short | ProtQuant: a tool for the label-free quantification of MudPIT proteomics data |
title_sort | protquant: a tool for the label-free quantification of mudpit proteomics data |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2099493/ https://www.ncbi.nlm.nih.gov/pubmed/18047724 http://dx.doi.org/10.1186/1471-2105-8-S7-S24 |
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