Accurate and Sensitive Peptide Identification with Mascot Percolator

By Markus Brosch, Lu Yu, Tim J. P. Hubbard, Jyoti Choudhary. Journal of Proteome Research, 2009

404 citations recorded by the source corpus.

Sound scoring methods for sequence database search algorithms such as Mascot and Sequest are essential for sensitive and accurate peptide and protein identifications from proteomic tandem mass spectrometry data. In this paper, we present a software package that interfaces Mascot with Percolator, a well performing machine learning method for rescoring database search results, and demonstrate it to be amenable for both low and high accuracy mass spectrometry data, outperforming all available Mascot scoring schemes as well as providing reliable significance measures. Mascot Percolator can be readily used as a stand alone tool or integrated into existing data analysis pipelines.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/acd8740b5e830fb6ddde796c3bd36e81fc938dff

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Citations in the corpus

PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification (2011, methodology): “To improve both accuracy and sensitivity, a new scoring function needs to be developed that more accurately separates the true and false identifications (16, 17).”
A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics (2010, methodology): “Percolator reduces the dependence on the training data via a dynamic learning approach [215], in which the original fully supervised approach [27] based on a support MASCOT [216] search results.”