2003
DOI: 10.1021/ac0341261
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A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry

Abstract: A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation-maximization algorithm. Using peptide assignments to… Show more

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Cited by 4,385 publications
(3,941 citation statements)
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References 42 publications
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“…Peptide identifications were accepted if they could be established with less than 1.0% false discovery by the Scaffold Local false discovery rate (FDR) algorithm and contained at least two identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii, Keller, Kolker & Aebersold, 2003). …”
Section: Methodsmentioning
confidence: 99%
“…Peptide identifications were accepted if they could be established with less than 1.0% false discovery by the Scaffold Local false discovery rate (FDR) algorithm and contained at least two identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii, Keller, Kolker & Aebersold, 2003). …”
Section: Methodsmentioning
confidence: 99%
“…The peptide identifications were accepted if they could be established at greater than 99.0% probability as assigned by the Peptide Prophet algorithm [49]. The protein identifications were accepted if they could be established at greater than 95% probability as assigned by the Protein Prophet algorithm [50]; were based on at least 2 identified peptides; and were detected in at least two out of three replicates (both biological and technical).…”
Section: Methodsmentioning
confidence: 99%
“…Protein probabilities were assigned by the Protein Prophet algorithm. 59 Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Normalization was performed iteratively (across samples and spectra) by subtracting the average ratios in log-space.…”
Section: Methodsmentioning
confidence: 99%