I’m pleased to announce that Luca Bianco, Conrad Bessant and myself have had our latest research paper accepted by the Journal of Proteome Research (JPR). The work to appear soon shows the affects of different database designs on false positive rate using an automated data analysis pipeline. To get a sneak preview click here.
Gist of the work:
Recently, standard MS/MS datasets have become available via public proteomic data repositories. When such datasets are analysed by an automated proteomic pipeline, the resulting protein identifications can be compared to the known protein content of the sample to determine the false positive rate (FPR). Assuming the list of constituents is correct & contaminants have been avoided, this approach can accurately demonstrate the reliability of the search method applied. This study describes the application of standard MS/MS datasets to determine the protein identification performance of the Genome Annotating Proteomic Pipeline (Cranfield’s proteomic data analysis platform) & includes an investigation of the effect of different decoy database designs on FPR.