Estimating the statistical significance of peptide identifications from shotgun proteomics experiments.
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.
Higgs
Richard E
RE
Lilly Research Laboratories, MS 1533, Lilly Corporate Center, Indianapolis, Indiana 46285, USA. higgs@lilly.com
Knierman
Michael D
MD
Freeman
Angela Bonner
AB
Gelbert
Lawrence M
LM
Patil
Sandeep T
ST
Hale
John E
JE
D000328:Adult; D000595:Amino Acid Sequence; D000818:Animals; D002138:Calibration; D005189:False Positive Reactions; D006801:Humans; D008297:Male; D013058:Mass Spectrometry / Q000295:instrumentation / Q000379:methods*; D008875:Middle Aged; D010455:Peptides / Q000737:chemistry / Q000145:classification / Q000235:genetics / Q000378:metabolism; D011110:Polymorphism, Genetic; D040901:Proteomics; D012372:ROC Curve; D051381:Rats
D010455:Peptides