title: Confident protein identification using the average peptide score method coupled with search-specific, ab initio thresholds.
abstract: Perhaps the greatest difficulty in interpreting large sets of protein identifications derived from mass spectrometric methods is whether or not to trust the results. For such experiments, the level of confidence in each protein identification made needs to be far greater than the often used 95% significance threshold to avoid the identification of many false-positives. To provide higher confidence results, we have developed an innovative scoring strategy coupling the recently published Average Peptide Score (APS) method with pre-filtering of peptide identifications, using a simple peptide quality filter. Iterative generation of these filters in conjunction with reversed database searching is used to determine the correct levels at which the APS and peptide quality thresholds should be set to return virtually zero false-positive reports. This proceeds without the need to reference a known dataset.
authors.affiliation: Department of Analytical Science and Informatics, Cranfield University at Silsoe, Befordshire, UK. i.p.shadforth.s01@cranfield.ac.uk
authors.forename: Ian
authors.initials: I
authors.lastname: Shadforth
authors.forename: Tom
authors.initials: T
authors.lastname: Dunkley
authors.forename: Kathryn
authors.initials: K
authors.lastname: Lilley
authors.forename: Daniel
authors.initials: D
authors.lastname: Crowther
authors.forename: Conrad
authors.initials: C
authors.lastname: Bessant
mesh_terms: D019295:Computational Biology; D030562:Databases, Protein; D013058:Mass Spectrometry / Q000592:standards*; D010455:Peptides / Q000032:analysis; D011506:Proteins / Q000032:analysis* / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D010455:Peptides; D011506:Proteins