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TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics.
- Source :
-
Nature methods [Nat Methods] 2016 Sep; Vol. 13 (9), pp. 777-83. Date of Electronic Publication: 2016 Aug 01. - Publication Year :
- 2016
-
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.<br />Competing Interests: The authors declare that they have no competing financial interests.
- Subjects :
- Algorithms
Electronic Data Processing instrumentation
Humans
Mass Spectrometry
Peptides metabolism
Pluripotent Stem Cells metabolism
Protein Precursors analysis
Protein Precursors metabolism
Proteolysis
Proteomics instrumentation
Reproducibility of Results
Sequence Alignment instrumentation
Sequence Analysis, Protein instrumentation
Streptococcus pyogenes metabolism
Electronic Data Processing methods
Peptides analysis
Proteomics methods
Sequence Alignment methods
Sequence Analysis, Protein methods
Software
Subjects
Details
- Language :
- English
- ISSN :
- 1548-7105
- Volume :
- 13
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- Nature methods
- Publication Type :
- Academic Journal
- Accession number :
- 27479329
- Full Text :
- https://doi.org/10.1038/nmeth.3954