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Paola Picotti

Researcher at ETH Zurich

Publications -  122
Citations -  12651

Paola Picotti is an academic researcher from ETH Zurich. The author has contributed to research in topics: Proteome & Proteomics. The author has an hindex of 45, co-authored 103 publications receiving 10723 citations. Previous affiliations of Paola Picotti include École Polytechnique Fédérale de Lausanne & University of Zurich.

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Selected reaction monitoring for quantitative proteomics: a tutorial

Vinzenz Lange, +3 more
- 01 Jan 2008 - 
TL;DR: This tutorial explains the application of SRM for quantitative proteomics, including the selection of proteotypic peptides and the optimization and validation of transitions, and normalization and various factors affecting sensitivity and accuracy are discussed.
Journal ArticleDOI

Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions

Paola Picotti, +2 more
- 01 Jun 2012 - 
TL;DR: How SRM is applied in proteomics is described, recent advances are reviewed, present selected applications and a perspective on the future of this powerful technology is provided.
Journal ArticleDOI

L-Arginine Modulates T Cell Metabolism and Enhances Survival and Anti-tumor Activity.

Roger Geiger, +15 more
- 20 Oct 2016 - 
TL;DR: Elevating L-arginine levels induced global metabolic changes including a shift from glycolysis to oxidative phosphorylation in activated T cells and promoted the generation of central memory-like cells endowed with higher survival capacity and, in a mouse model, anti-tumor activity.
Journal ArticleDOI

Full Dynamic Range Proteome Analysis of S. cerevisiae by Targeted Proteomics

Paola Picotti, +4 more
- 21 Aug 2009 - 
TL;DR: The potential of SRM-based proteomics to provide assays for the measurement of any set of proteins of interest in yeast at high-throughput and quantitative accuracy is demonstrated.
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mProphet: automated data processing and statistical validation for large-scale SRM experiments

Lukas Reiter, +10 more
- 01 May 2011 - 
TL;DR: In this article, the authors present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.