Computational protein profile similarity screening for quantitative mass spectrometry experiments

• Verfasst von: Kirchner, Marc [VerfasserIn]
• Verfasst von: Renard, Bernhard Y. [VerfasserIn]
• Verfasst von: Köthe, Ullrich [VerfasserIn]
• Verfasst von: Pappin, Darryl J. [VerfasserIn]
• Verfasst von: Hamprecht, Fred [VerfasserIn]
• Verfasst von: Steen, Hanno [VerfasserIn]
• Verfasst von: Steen, Judith A. J. [VerfasserIn]
• Titel: Computational protein profile similarity screening for quantitative mass spectrometry experiments
• Verf.angabe: Marc Kirchner, Bernhard Y. Renard, Ullrich Köthe, Darryl J. Pappin, Fred A. Hamprecht, Hanno Steen and Judith A.J. Steen
• Jahr: 2010
• Umfang: 7 S.
• Fussnoten: Online veröffentlicht am 27. Oktober 2009 ; Gesehen am 14.03.2023
• Titel Quelle: Enthalten in: Bioinformatics
• Ort Quelle: Oxford : Oxford Univ. Press, 1985
• Jahr Quelle: 2010
• Band/Heft Quelle: 26(2010), 1 vom: Jan., Seite 77-83
• ISSN Quelle: 1367-4811
• DOI: doi:10.1093/bioinformatics/btp607
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1839089113

Abstract

The qualitative and quantitative characterization of protein abundance profiles over a series of time points or a set of environmental conditions is becoming increasingly important. Using isobaric mass tagging experiments, mass spectrometry-based quantitative proteomics deliver accurate peptide abundance profiles for relative quantitation. Associated data analysis workflows need to provide tailored statistical treatment that (i) takes the correlation structure of the normalized peptide abundance profiles into account and (ii) allows inference of protein-level similarity. We introduce a suitable distance measure for relative abundance profiles, derive a statistical test for equality and propose a protein-level representation of peptide-level measurements. This yields a workflow that delivers a similarity ranking of protein abundance profiles with respect to a defined reference. All procedures have in common that they operate based on the true correlation structure that underlies the measurements. This optimizes power and delivers more intuitive and efficient results than existing methods that do not take these circumstances into account.