Universidad Complutense de Madrid
E-Prints Complutense

General statistical framework for quantitative proteomics by stable isotope labeling.



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Navarro, Pedro y Trevisan Herraz, Marco y Bonzon Kulichenko, Elena y Núñez, Estefanía y Martínez Acedo, Pablo y Pérez Hernández, Daniel y Jorge, Inmaculada y Mesa, Raquel y Calvo, Enrique y Carrascal, Montserrat y Hernáez, María Luisa y García, Fernando y Bárcena, José Antonio y Ashman, Keith y Abian, J. y Gil, Concha y Redondo, Juan Miguel y Vázquez, Jesús (2014) General statistical framework for quantitative proteomics by stable isotope labeling. Journal of proteome research, 13 (3). pp. 1234-47. ISSN 1535-3907

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URL Oficial: http://dx.doi.org/10.1021/pr4006958


The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including (18)O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H₂O₂ concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.

Tipo de documento:Artículo
Palabras clave:Quantitative proteomics, stable isotope labeling, statistical analysis, yeast
Materias:Ciencias Biomédicas > Farmacia > Microbiología
Código ID:33630
Depositado:26 Nov 2015 13:14
Última Modificación:29 Nov 2015 15:28

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