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

Hannes L. Röst, Yansheng Liu, Giuseppe D'Agostino, Matteo Zanella, Pedro Navarro, George Rosenberger, Ben C. Collins, Ludovic Gillet, Giuseppe Testa, Lars Malmström, Ruedi Aebersold

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)777-783
Number of pages7
JournalNature Methods
Volume13
Issue number9
DOIs
Publication statusPublished - Aug 30 2016

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ASJC Scopus subject areas

  • Biotechnology
  • Molecular Biology
  • Biochemistry
  • Cell Biology

Cite this

Röst, H. L., Liu, Y., D'Agostino, G., Zanella, M., Navarro, P., Rosenberger, G., Collins, B. C., Gillet, L., Testa, G., Malmström, L., & Aebersold, R. (2016). TRIC: An automated alignment strategy for reproducible protein quantification in targeted proteomics. Nature Methods, 13(9), 777-783. https://doi.org/10.1038/nmeth.3954