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.
Keywords: Peptide Identification • False-Positive Rate • False
Discovery Rate • Proteomics • Data Analysis • Mass Spectrometry
• Reversed Database • Decoy Database