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

Nature Methods

Volumn 13, Issue 9, 2016, Pages 777-783

  Röst, Hannes L ^a,b   Liu, Yansheng ^a   D'Agostino, Giuseppe ^c   Zanella, Matteo ^c   Navarro, Pedro ^a,d   Rosenberger, George ^a,e   Collins, Ben C ^a   Gillet, Ludovic ^a   Testa, Giuseppe ^c,f   Malmström, Lars ^a,e   Aebersold, Ruedi ^a,e  

^a ETH ZURICH   (Switzerland)

^b STANFORD UNIVERSITY   (United States)

^c EUROPEAN INSTITUTE OF ONCOLOGY   (Italy)

^d JOHANNES GUTENBERG UNIVERSITY   (Germany)

^e UNIVERSITY OF ZURICH   (Switzerland)

^f UNIVERSITY OF MILAN   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEOME; PEPTIDE; PROTEIN PRECURSOR;

ALGORITHM; ARTICLE; AUTOMATION; MASS SPECTROMETRY; NEXT GENERATION SEQUENCING; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN METABOLISM; PROTEOMICS; SOFTWARE; VIRULENCE; DEVICES; HUMAN; INFORMATION PROCESSING; METABOLISM; PLURIPOTENT STEM CELL; PROCEDURES; PROTEIN DEGRADATION; REPRODUCIBILITY; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; STREPTOCOCCUS PYOGENES;

ALGORITHMS; AUTOMATIC DATA PROCESSING; HUMANS; MASS SPECTROMETRY; PEPTIDES; PLURIPOTENT STEM CELLS; PROTEIN PRECURSORS; PROTEOLYSIS; PROTEOMICS; REPRODUCIBILITY OF RESULTS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; STREPTOCOCCUS PYOGENES;

EID: 84980367708     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.3954     Document Type: Article

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