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Nat Methods. 2016 Sep;13(9):777-83. doi: 10.1038/nmeth.3954. Epub 2016 Aug 1.

TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics.

Author information

1
Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
2
Department of Genetics, Stanford University, Stanford, California, USA.
3
Department of Experimental Oncology, European Institute of Oncology, Milan, Italy.
4
Institute for Immunology, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany.
5
PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland.
6
Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
7
S3IT, University of Zurich, Zurich, Switzerland.
8
Faculty of Science, University of Zurich, Zurich, Switzerland.

Abstract

Next-generation mass spectrometric (MS) techniques such as SWATH-MS have substantially increased the throughput and reproducibility of proteomic analysis, but ensuring consistent quantification of thousands of peptide analytes across multiple liquid chromatography-tandem MS (LC-MS/MS) runs remains a challenging and laborious manual process. To produce highly consistent and quantitatively accurate proteomics data matrices in an automated fashion, we developed TRIC (http://proteomics.ethz.ch/tric/), a software tool that utilizes fragment-ion data to perform cross-run alignment, consistent peak-picking and quantification for high-throughput targeted proteomics. TRIC reduced the identification error compared to a state-of-the-art SWATH-MS analysis without alignment by more than threefold at constant recall while correcting for highly nonlinear chromatographic effects. On a pulsed-SILAC experiment performed on human induced pluripotent stem cells, TRIC was able to automatically align and quantify thousands of light and heavy isotopic peak groups. Thus, TRIC fills a gap in the pipeline for automated analysis of massively parallel targeted proteomics data sets.

PMID:
27479329
PMCID:
PMC5008461
DOI:
10.1038/nmeth.3954
[Indexed for MEDLINE]
Free PMC Article

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