Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0

By Matthew The, Michael J. MacCoss, William Stafford Noble, Lukas Käll. Journal of the American Society for Mass Spectrometry, 2016

407 citations recorded by the source corpus.

AbstractPercolator is a widely used software tool that increases yield in shotgun proteomics experiments and assigns reliable statistical confidence measures, such as q values and posterior error probabilities, to peptides and peptide-spectrum matches (PSMs) from such experiments. Percolator’s processing speed has been sufficient for typical data sets consisting of hundreds of thousands of PSMs. With our new scalable approach, we can now also analyze millions of PSMs in a matter of minutes on a commodity computer. Furthermore, with the increasing awareness for the need for reliable statistics on the protein level, we compared several easy-to-understand protein inference methods and implemented the best-performing method—grouping proteins by their corresponding sets of theoretical peptides and then considering only the best-scoring peptide for each protein—in the Percolator package. We used Percolator 3.0 to analyze the data from a recent study of the draft human proteome containing 25 million spectra (PM:24870542). The source code and Ubuntu, Windows, MacOS, and Fedora binary packages are available from http://percolator.ms/ under an Apache 2.0 license. Graphical Abstractᅟ

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/ea6c85fb540e426b6600fc61fdd46ce8b981ee5d

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Citations in the corpus

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• Modeling the CRL4A ligase complex to predict target protein ubiquitination induced by cereblon-recruiting PROTACs (2022, methodology): “The peptide spectral matches were filtered using the Percolator (56) node for target FDR of 1% at the peptide level, with Target/Decoy Selection set to Concatenated with Validation based on q-value.”