A new paper currently in press at MCP takes a novel approach at calculating the false discovery rates of post translational modifications. The work, from Fu and Qian, researchers at two facilities in Beijing, tries separating out FDR for modified and unmodified peptides.
The paper is really stats heavy. Too many Sigmas for this blogger to be really insightful here. The gist, however, is that both search engines and FDR calculations are pretty rough on PTMs in comparison to their unmodified counterparts. By modeling on a slew of artificial spectra this team demonstrates an increase in high-confidence peptide spectral matches of modified peptides. The FDR calculations are actually linked so the analysis isn't separate, which also seems to improve the results.
In theory it looks really good. I am curious as to how this works in practice. It is one thing to model an engine on perfect in silico fragmented spectra. When you look at the intrinsic noise, variability and inevitably missing (or low intensity) fragment ions in real spectra it can be a completely different animal. A few images with manual validation of real spectra would have been a nice addition to this paper. Hopefully there will be a follow-up. But, hey, any new approaches we can come up for working out this FDR mess is a good thing in my book!
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