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

Edward L. Huttlin, Adrian D. Hegeman, Amy C. Harms, Michael R. Sussman

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

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 languageEnglish (US)
Pages (from-to)392-398
Number of pages7
JournalJournal of Proteome Research
Volume6
Issue number1
DOIs
StatePublished - Jan 2007

Keywords

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

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