Probability-based validation of protein identifications using a modified SEQUEST algorithm.

By Michael J. MacCoss, Christine C. Wu, John R. Yates III. Analytical Chemistry, 2002

353 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/48db31e1e79e823bbd807bcb544c8cc502d65209

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Citations in the corpus

Proteomic analysis of post-translational modifications (2003, background): “The last two were initially developed for unmodified peptides but have been extended to modified peptide”
Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* (2009, background): “…to good error estimates for individual experiments (typically 10-100 LC-MS/MS runs), the complexity level at which most proteomics studies operate (MacCoss, Wu et al. 2002; Nesvizhskii, Keller et al. 2003; Adamski, Blackwell et al. 2005; Weatherly, Astwood et al. 2005; Price, Lucitt et al. 2007).”
PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse (2011, background): “This changed in the beginning of 2004 with high-throughput (HTP) tandem mass spectrometry [MS; (7)], when articles reporting many hundreds to thousands of phosphorylation sites began appearing (8–10).”
A method for the comprehensive proteomic analysis of membrane proteins (2003, background): “, and 1,610 proteins were identified at >95% confidenc”
Large-scale database searching using tandem mass spectra: Looking up the answer in the back of the book (2004, methodology): “To normalize XCorr for spectral noise and peptide size, the XCorr value is divided by auto-correlation of the experimental spectrum or by the square root of the products of auto-correlations of experimental and theoretical spectr”