Accurate and Sensitive Peptide Identification with Mascot Percolator

• DOI: 10.1021/pr800982s
• Publication Date: 2009-04-01T18:44:32Z
• Authors: Markus Brosch; Lu Yu; Tim Hubbard; Jyoti Choudhary
• Keywords: Proteomics; 0301 basic medicine; Chromatography; Liquid; Protein; Reproducibility of Results; Sensitivity and Specificity; Peptide Fragments; 576; Databases; 03 medical and health sciences; Artificial Intelligence; Sequence Analysis, Protein; Tandem Mass Spectrometry; Databases, Protein; Sequence Analysis; Algorithms; Software; Chromatography, Liquid

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.