Literature DB >> 23270375

Statistical inference from multiple iTRAQ experiments without using common reference standards.

Shelley M Herbrich1, Robert N Cole, Keith P West, Kerry Schulze, James D Yager, John D Groopman, Parul Christian, Lee Wu, Robert N O'Meally, Damon H May, Martin W McIntosh, Ingo Ruczinski.   

Abstract

Isobaric tags for relative and absolute quantitation (iTRAQ) is a prominent mass spectrometry technology for protein identification and quantification that is capable of analyzing multiple samples in a single experiment. Frequently, iTRAQ experiments are carried out using an aliquot from a pool of all samples, or "masterpool", in one of the channels as a reference sample standard to estimate protein relative abundances in the biological samples and to combine abundance estimates from multiple experiments. In this manuscript, we show that using a masterpool is counterproductive. We obtain more precise estimates of protein relative abundance by using the available biological data instead of the masterpool and do not need to occupy a channel that could otherwise be used for another biological sample. In addition, we introduce a simple statistical method to associate proteomic data from multiple iTRAQ experiments with a numeric response and show that this approach is more powerful than the conventionally employed masterpool-based approach. We illustrate our methods using data from four replicate iTRAQ experiments on aliquots of the same pool of plasma samples and from a 406-sample project designed to identify plasma proteins that covary with nutrient concentrations in chronically undernourished children from South Asia.

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Year:  2013        PMID: 23270375      PMCID: PMC4223774          DOI: 10.1021/pr300624g

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


  27 in total

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3.  Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays.

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4.  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

Review 5.  A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies.

Authors:  Lynn R Zieske
Journal:  J Exp Bot       Date:  2006-03-30       Impact factor: 6.992

6.  Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research.

Authors:  Sebastian Wiese; Kai A Reidegeld; Helmut E Meyer; Bettina Warscheid
Journal:  Proteomics       Date:  2007-02       Impact factor: 3.984

Review 7.  Statistics for proteomics: a review of tools for analyzing experimental data.

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Journal:  Proteomics       Date:  2006-09       Impact factor: 3.984

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Journal:  Mol Cell Proteomics       Date:  2007-07-07       Impact factor: 5.911

9.  Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA.

Authors:  Ann L Oberg; Douglas W Mahoney; Jeanette E Eckel-Passow; Christopher J Malone; Russell D Wolfinger; Elizabeth G Hill; Leslie T Cooper; Oyere K Onuma; Craig Spiro; Terry M Therneau; H Robert Bergen
Journal:  J Proteome Res       Date:  2008-01-04       Impact factor: 4.466

10.  Antenatal micronutrient supplementation reduces metabolic syndrome in 6- to 8-year-old children in rural Nepal.

Authors:  Christine P Stewart; Parul Christian; Kerry J Schulze; Steven C Leclerq; Keith P West; Subarna K Khatry
Journal:  J Nutr       Date:  2009-06-23       Impact factor: 4.798

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  62 in total

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Authors:  R B S Valadares; S Perotto; E C Santos; M R Lambais
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Journal:  J Neurosci       Date:  2014-09-24       Impact factor: 6.167

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Journal:  EuPA Open Proteom       Date:  2015-06

6.  CONSTANd : A Normalization Method for Isobaric Labeled Spectra by Constrained Optimization.

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Journal:  Mol Cell Proteomics       Date:  2016-06-14       Impact factor: 5.911

7.  Quantitative Proteomic Analysis Reveals Similarities between Huntington's Disease (HD) and Huntington's Disease-Like 2 (HDL2) Human Brains.

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Journal:  J Proteome Res       Date:  2016-08-03       Impact factor: 4.466

8.  KDEL Receptors Are Differentially Regulated to Maintain the ER Proteome under Calcium Deficiency.

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9.  Analysis of KLF4 regulated genes in cancer cells reveals a role of DNA methylation in promoter- enhancer interactions.

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10.  Identification of putative biomarkers for HIV-associated neurocognitive impairment in the CSF of HIV-infected patients under cART therapy determined by mass spectrometry.

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Journal:  J Neurovirol       Date:  2014-07-24       Impact factor: 2.643

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