Accurately Assigning Peptides to Spectra When Only a Subset of Peptides Are Relevant

The
standard proteomics database search strategy involves searching
spectra against a peptide database and estimating the false discovery
rate (FDR) of the resulting set of peptide-spectrum matches. One assumption
of this protocol is that all the peptides in the database are relevant
to the hypothesis being investigated. However, in settings where researchers
are interested in a subset of peptides, alternative search and FDR
control strategies are needed. Recently, two methods were proposed
to address this problem: subset-search and all-sub. We show that both
methods fail to control the FDR. For subset-search, this failure is
due to the presence of “neighbor” peptides, which are
defined as irrelevant peptides with a similar precursor mass and fragmentation
spectrum as a relevant peptide. Not considering neighbors compromises
the FDR estimate because a spectrum generated by an irrelevant peptide
can incorrectly match well to a relevant peptide. Therefore, we have
developed a new method, “subset-neighbor search” (SNS),
that accounts for neighbor peptides. We show evidence that SNS controls
the FDR when neighbors are present and that SNS outperforms group-FDR,
the only other method that appears to control the FDR relative to
a subset of relevant peptides.