A statistical model for identifying proteins by tandem mass spectrometry.

By Alexey I. Nesvizhskii, Andrew Keller, E. Kolker, R. Aebersold. 2003

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Subfields: Molecular Biology, Spectroscopy

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Citations in the corpus

The beginning of a beautiful friendship: cross-linking/mass spectrometry and modelling of proteins and multi-protein complexes (2010, methodology): “, 1994), using probabilistic approaches (Perkins et al., 1999; Nesvizhskii et al., 2003; Sadygov and Yates, 2003), relying on machine learning (Käll et al.”
Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, methodology): “It not only groups protein clusters that share peptides, but also contain unique peptides, based on the ProteinProphet algorithm.”
Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice (2011, methodology): “Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii et al., 2003).”
Spectral Probabilities and Generating Functions of Tandem Mass Spectra: a Strike Against Decoy Databases (2008, background): “…remark that many one-hit-wonders with SpectralProbability below 1.55 × 10 - 8 are actually more statistically signiﬁcant than some proteins with multiple peptide hits but larger SpectralProbability values (see refs 44, 48–50 for combining peptide signiﬁcance scores into protein signiﬁcance scores).”
DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics (2015, methodology): “For the AP-SWATH data set, ProteinProphet analysis was done by taking all PeptideProphet output files (X!”
iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* (2011, background): “As we noticed early on (11) one of the most critical challenges is the inflation of the error rates when going from PSM to unique peptide sequence to protein level.”
Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation (2007, methodology): “Although both n i and p i are experimentally measured variables (here, calculated by ProteinProphe”
PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows (2008, methodology): “…match populations, and assigns a probability of being correct to each match. all peptideprophet results are then combined using proteinprophet (nesvizhskii et al, 2003), a programme that uses the spectrum-peptide match models from peptideprophet to derive protein-level probabilities, as well…”
Novel Staphylococcal Glycosyltransferases SdgA and SdgB Mediate Immunogenicity and Protection of Virulence-Associated Cell Wall Proteins (2013, methodology): “Thresholds for accepting peptide and protein identifications were set at greater than 90% and 99% respectively, using the Peptide Prophet and Protein Prophet algorithms [37].”
The PeptideAtlas project (2005, methodology): “PeptideProphet computes a probability that an assignment of an MS/MS spectrum to a peptide sequence is correct based on the database search scores, the difference between the measured and theoretical peptide masses, the expected and found number of termini for the type of enzymatic cleavage used and”
Comet: An open‐source MS/MS sequence database search tool (2013, methodology): “For example, its pepXML output can be input to the Trans-Proteomic Pipeline suite [11] and processed through tools such as PeptideProphet [19] and ProteinProphet [20].”
Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* (2009, methodology): “Widely used protein inference tools like ProteinProphet (24) have proven to yield reliable error estimates on data sets at the experiment level (typically 10–50 LC-MS/MS runs) but fail to estimate an accurate protein identification FDR for large data sets (Fig.”
mProphet: automated data processing and statistical validation for large-scale SRM experiments (2011, neutral): “This is analogous to the situation in early shotgun proteomics, in which the tools to control the quality of the identification were developed long after the principal measurement method”