Estimating the Statistical Significance of Peptide Identifications from Shotgun Proteomics Experiments

Abstract

We present a wrapper-based approach to estimate and control the false discovery rate for peptide identifications using the outputs from multiple commercially available MS/MS search engines. Features of the approach include the flexibility to combine output from multiple search engines with sequence and spectral derived features in a flexible classification model to produce a score associated with correct peptide identifications. This classification model score from a reversed database search is taken as the null distribution for estimating p-values and false discovery rates using a simple and established statistical procedure. Results from 10 analyses of rat sera on an LTQ-FT mass spectrometer indicate that the method is well calibrated for controlling the proportion of false positives in a set of reported peptide identifications while correctly identifying more peptides than rule-based methods using one search engine alone.

Proteome-Wide Identification of Proteins and Their Modifications with Decreased Ambiguities and Improved False Discovery Rates Using Unique Sequence Tags

“…1,2 Peptide assignments are typically accomplished using automated database searching algorithms (e.g., SEQUEST, 2 MASCOT 3 , X!Tandem 4 ) that compare tandem mass spectra with in silico generated model spectra derived from candidate peptide sequences, using scoring schemes to determine relative confidence levels. [5][6][7] low FDRs (e.g.,<1%) have been obtained from conventional precision LC-MS/MS data, 5 the accuracy of such estimates is uncertain. The effectiveness of the identification process decreases as the size of the peptide candidates increases, 8 and thus proteome coverage is decreased if the FDR is to be held constant.…”

Multidimensional Protein Identification Technology

“…Parameters were set as follows: a mass tolerance of 2 Da for precursors and 0.7 Da for fragment ions, two missed cleavage sites allowed for trypsin, carbamidomethyl cysteine as fixed modification, and oxidized methionine as optional modification. The q-value represents peptide false identification rate and was calculated by a recently published method [18] which incorporates Sequest and X!Tandem results in addition to a number of other relevant factors such as Δ [M+H]+ and charge state. Protein identifications were assigned to one of four groups as discussed previously [19] with priority 1 identifications (IDs) regarded as high-confidence and requiring ≥2 unique peptides and a q-value ≤0.1.…”

Proteomic Characterization of Plasma Cells from Patients with Multiple Myeloma

“…Data collection was performed in the "Triple-Play" mode (MS scan, Zoom scan, and MS/MS scan). Acquired data was filtered and analyzed by a previously published proprietary algorithm developed by Higgs et al [27,28].…”

“…Peptides with peptide ID confidence <75% were filtered out of this study. The estimation of the confidence levels, which is based on a random forest recursive partition supervised learning algorithm was described previously [28].…”

“…In this study, we applied an ion intensity-based label-free protein quantification technology [27,28] to analyze global protein expression profiles of plasma cells from MM patients and healthy controls. This method is high-throughput and sensitive enough to detect and quantify thousands of proteins in complex biological samples, allowing potential MM protein biomarkers with high discriminate ability to be identified.…”