Literature DB >> 16944945

Multi-Q: a fully automated tool for multiplexed protein quantitation.

Wen-Ting Lin1, Wei-Neng Hung, Yi-Hwa Yian, Kun-Pin Wu, Chia-Li Han, Yet-Ran Chen, Yu-Ju Chen, Ting-Yi Sung, Wen-Lian Hsu.   

Abstract

The iTRAQ labeling method combined with shotgun proteomic techniques represents a new dimension in multiplexed quantitation for relative protein expression measurement in different cell states. To expedite the analysis of vast amounts of spectral data, we present a fully automated software package, called Multi-Q, for multiplexed iTRAQ-based quantitation in protein profiling. Multi-Q is designed as a generic platform that can accommodate various input data formats from search engines and mass spectrometer manufacturers. To calculate peptide ratios, the software automatically processes iTRAQ's signature peaks, including peak detection, background subtraction, isotope correction, and normalization to remove systematic errors. Furthermore, Multi-Q allows users to define their own data-filtering thresholds based on semiempirical values or statistical models so that the computed results of fold changes in peptide ratios are statistically significant. This feature facilitates the use of Multi-Q with various instrument types with different dynamic ranges, which is an important aspect of iTRAQ analysis. The performance of Multi-Q is evaluated with a mixture of 10 standard proteins and human Jurkat T cells. The results are consistent with expected protein ratios and thus demonstrate the high accuracy, full automation, and high-throughput capability of Multi-Q as a large-scale quantitation proteomics tool. These features allow rapid interpretation of output from large proteomic datasets without the need for manual validation. Executable Multi-Q files are available on Windows platform at http://ms.iis.sinica.edu.tw/Multi-Q/.

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Year:  2006        PMID: 16944945     DOI: 10.1021/pr060132c

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  34 in total

1.  Isobaric labeling and data normalization without requiring protein quantitation.

Authors:  Phillip D Kim; Bhavinkumar B Patel; Anthony T Yeung
Journal:  J Biomol Tech       Date:  2012-04

2.  Addressing accuracy and precision issues in iTRAQ quantitation.

Authors:  Natasha A Karp; Wolfgang Huber; Pawel G Sadowski; Philip D Charles; Svenja V Hester; Kathryn S Lilley
Journal:  Mol Cell Proteomics       Date:  2010-04-10       Impact factor: 5.911

3.  A robust error model for iTRAQ quantification reveals divergent signaling between oncogenic FLT3 mutants in acute myeloid leukemia.

Authors:  Yi Zhang; Manor Askenazi; Jingrui Jiang; C John Luckey; James D Griffin; Jarrod A Marto
Journal:  Mol Cell Proteomics       Date:  2009-12-17       Impact factor: 5.911

4.  Quantitative phosphokinome analysis of the Met pathway activated by the invasin internalin B from Listeria monocytogenes.

Authors:  Tobias Reinl; Manfred Nimtz; Claudia Hundertmark; Thorsten Johl; György Kéri; Jürgen Wehland; Henrik Daub; Lothar Jänsch
Journal:  Mol Cell Proteomics       Date:  2009-07-29       Impact factor: 5.911

5.  Find pairs: the module for protein quantification of the PeakQuant software suite.

Authors:  Martin Eisenacher; Michael Kohl; Sebastian Wiese; Romano Hebeler; Helmut E Meyer; Bettina Warscheid; Christian Stephan
Journal:  OMICS       Date:  2012-08-21

6.  Defining, comparing, and improving iTRAQ quantification in mass spectrometry proteomics data.

Authors:  Lina Hultin-Rosenberg; Jenny Forshed; Rui M M Branca; Janne Lehtiö; Henrik J Johansson
Journal:  Mol Cell Proteomics       Date:  2013-03-07       Impact factor: 5.911

7.  Plasmodium falciparum proteome changes in response to doxycycline treatment.

Authors:  Sébastien Briolant; Lionel Almeras; Maya Belghazi; Elodie Boucomont-Chapeaublanc; Nathalie Wurtz; Albin Fontaine; Samuel Granjeaud; Thierry Fusaï; Christophe Rogier; Bruno Pradines
Journal:  Malar J       Date:  2010-05-25       Impact factor: 2.979

8.  WaveletQuant, an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomic analysis.

Authors:  Fan Mo; Qun Mo; Yuanyuan Chen; David R Goodlett; Leroy Hood; Gilbert S Omenn; Song Li; Biaoyang Lin
Journal:  BMC Bioinformatics       Date:  2010-04-29       Impact factor: 3.169

9.  Empirical Bayes analysis of quantitative proteomics experiments.

Authors:  Adam A Margolin; Shao-En Ong; Monica Schenone; Robert Gould; Stuart L Schreiber; Steven A Carr; Todd R Golub
Journal:  PLoS One       Date:  2009-10-14       Impact factor: 3.240

10.  iQuantitator: a tool for protein expression inference using iTRAQ.

Authors:  John H Schwacke; Elizabeth G Hill; Edward L Krug; Susana Comte-Walters; Kevin L Schey
Journal:  BMC Bioinformatics       Date:  2009-10-18       Impact factor: 3.169

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