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Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics

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Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics. / Dowle, Adam; Wilson, Julie Carol; Thomas, Jerry R.

In: Journal of Proteome Research, Vol. 15, No. 10, 07.10.2016, p. 3550-3562.

Research output: Contribution to journalArticlepeer-review

Harvard

Dowle, A, Wilson, JC & Thomas, JR 2016, 'Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics', Journal of Proteome Research, vol. 15, no. 10, pp. 3550-3562. https://doi.org/10.1021/acs.jproteome.6b00308

APA

Dowle, A., Wilson, J. C., & Thomas, J. R. (2016). Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics. Journal of Proteome Research, 15(10), 3550-3562. https://doi.org/10.1021/acs.jproteome.6b00308

Vancouver

Dowle A, Wilson JC, Thomas JR. Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics. Journal of Proteome Research. 2016 Oct 7;15(10):3550-3562. https://doi.org/10.1021/acs.jproteome.6b00308

Author

Dowle, Adam ; Wilson, Julie Carol ; Thomas, Jerry R. / Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics. In: Journal of Proteome Research. 2016 ; Vol. 15, No. 10. pp. 3550-3562.

Bibtex - Download

@article{89f2856723934ee3934f09690090b706,
title = "Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics",
abstract = "Diagnostic classification accuracy is critical in expression proteomics to ensure that as many true differences as possible are identified with acceptable false-positive rates. We present a comparison of the diagnostic accuracy of iTRAQ with three label-free methods, peak area, spectral counting, and emPAI, for relative quantification using a spiked proteome standard. We provide the first validation of emPAI for intersample relative quantification and find clear differences among the four quantification approaches that could be considered when designing an experiment. Spectral counting was observed to perform surprisingly well in all regards. Peak area performed best for smaller fold differences and was shown to be capable of discerning a 1.1-fold difference with acceptable specificity and sensitivity. The performance of iTRAQ was dramatically worse than the label-free methods with low abundance proteins. Using the iTRAQ data set for validation, we also demonstrate a novel iTRAQ analysis regime that avoids the use of ratios in significance testing and outperforms a common commercial alternative.",
author = "Adam Dowle and Wilson, {Julie Carol} and Thomas, {Jerry R.}",
note = "{\textcopyright} 2016 American Chemical Society. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details. Embargo period: 12 months ",
year = "2016",
month = oct,
day = "7",
doi = "10.1021/acs.jproteome.6b00308",
language = "English",
volume = "15",
pages = "3550--3562",
journal = "Journal of Proteome Research",
issn = "1535-3893",
publisher = "American Chemical Society",
number = "10",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics

AU - Dowle, Adam

AU - Wilson, Julie Carol

AU - Thomas, Jerry R.

N1 - © 2016 American Chemical Society. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. Embargo period: 12 months

PY - 2016/10/7

Y1 - 2016/10/7

N2 - Diagnostic classification accuracy is critical in expression proteomics to ensure that as many true differences as possible are identified with acceptable false-positive rates. We present a comparison of the diagnostic accuracy of iTRAQ with three label-free methods, peak area, spectral counting, and emPAI, for relative quantification using a spiked proteome standard. We provide the first validation of emPAI for intersample relative quantification and find clear differences among the four quantification approaches that could be considered when designing an experiment. Spectral counting was observed to perform surprisingly well in all regards. Peak area performed best for smaller fold differences and was shown to be capable of discerning a 1.1-fold difference with acceptable specificity and sensitivity. The performance of iTRAQ was dramatically worse than the label-free methods with low abundance proteins. Using the iTRAQ data set for validation, we also demonstrate a novel iTRAQ analysis regime that avoids the use of ratios in significance testing and outperforms a common commercial alternative.

AB - Diagnostic classification accuracy is critical in expression proteomics to ensure that as many true differences as possible are identified with acceptable false-positive rates. We present a comparison of the diagnostic accuracy of iTRAQ with three label-free methods, peak area, spectral counting, and emPAI, for relative quantification using a spiked proteome standard. We provide the first validation of emPAI for intersample relative quantification and find clear differences among the four quantification approaches that could be considered when designing an experiment. Spectral counting was observed to perform surprisingly well in all regards. Peak area performed best for smaller fold differences and was shown to be capable of discerning a 1.1-fold difference with acceptable specificity and sensitivity. The performance of iTRAQ was dramatically worse than the label-free methods with low abundance proteins. Using the iTRAQ data set for validation, we also demonstrate a novel iTRAQ analysis regime that avoids the use of ratios in significance testing and outperforms a common commercial alternative.

U2 - 10.1021/acs.jproteome.6b00308

DO - 10.1021/acs.jproteome.6b00308

M3 - Article

VL - 15

SP - 3550

EP - 3562

JO - Journal of Proteome Research

JF - Journal of Proteome Research

SN - 1535-3893

IS - 10

ER -