  In their recent article, Madej et al. 1 proposed an original way to solve the
recurrent issue of controlling for the false discovery rate (FDR) in
peptide-spectrum-match (PSM) validation. Briefly, they proposed to derive a
single precise distribution of decoy matches termed the Common Decoy
Distribution (CDD) and to use it to control for FDR during a target-only
search. Conceptually, this approach is appealing as it takes the best of two
worlds, i.e., decoy-based approaches (which leverage a large-scale collection
of empirical mismatches) and decoy-free approaches (which are not subject to
the randomness of decoy generation while sparing an additional database
search). Interestingly, CDD also corresponds to a middle-of-the-road approach
in statistics with respect to the two main families of FDR control procedures:
Although historically based on estimating the falsepositive distribution, FDR
control has recently been demonstrated to be possible thanks to competition
between the original variables (in proteomics, target sequences) and their
fictional counterparts (in proteomics, decoys). Discriminating between these
two theoretical trends is of prime importance for computational proteomics. In
addition to highlighting why proteomics was a source of inspiration for
theoretical biostatistics, it provides practical insights into the improvements
that can be made to FDR control methods used in proteomics, including CDD.

Genomics;Quantitative Methods