Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy.
In recent years, a variety of approaches have been developed using decoy databases to empirically assess the error associated with peptide identifications from large-scale proteomics experiments. We have developed an approach for calculating the expected uncertainty associated with false-positive rate determination using concatenated reverse and forward protein sequence databases. After explaining the theoretical basis of our model, we compare predicted error with the results of experiments characterizing a series of mixtures containing known proteins. In general, results from characterization of known proteins show good agreement with our predictions. Finally, we consider how these approaches may be applied to more complicated data sets, as when peptides are separated by charge state prior to false-positive determination.
Huttlin
Edward L
EL
Department of Biochemistry, University of Wisconsin, 433 Babcock Drive, Madison, Wisconsin 53706, USA.
Hegeman
Adrian D
AD
Harms
Amy C
AC
Sussman
Michael R
MR
D000465:Algorithms; D000818:Animals; D002417:Cattle; D030562:Databases, Protein; D005189:False Positive Reactions; D006736:Horses; D006801:Humans; D007091:Image Processing, Computer-Assisted; D013058:Mass Spectrometry / Q000379:methods; D010455:Peptides / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D013552:Swine
D010455:Peptides