4.8 Article

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

期刊

NATURE METHODS
卷 13, 期 9, 页码 777-783

出版社

NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.3954

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资金

  1. SyBIT project of SystemsX.ch
  2. ETH Zurich [ETH-30 11-2]
  3. Swiss National Science Foundation (SNSF) [P2EZP3_162268, 31003A_166435]
  4. ERC [233226, 670821]
  5. PhosphonetX project of SystemsX.ch
  6. ERC DISEASEAVATARS [616441]
  7. Telethon Foundation [GGP14265]
  8. Regione Lombardia
  9. Fondazione Umberto Veronesi
  10. Swiss National Science Foundation (SNF) [P2EZP3_162268] Funding Source: Swiss National Science Foundation (SNF)
  11. European Research Council (ERC) [616441, 670821] Funding Source: European Research Council (ERC)

向作者/读者索取更多资源

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.

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