Abstract
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.
Original language | English (US) |
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Pages (from-to) | 392-398 |
Number of pages | 7 |
Journal | Journal of Proteome Research |
Volume | 6 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2007 |
Keywords
- Data Analysis
- Decoy Database
- False Discovery Rate
- False-Positive Rate
- Mass Spectrometry
- Peptide Identification
- Proteomics
- Reversed Database