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In any high-throughput scientific study, it is often essential to estimate the percent of findings that are actually incorrect. This percentage is called the false discovery rate (abbreviated "FDR"), and it is an invariant (albeit, often unknown) quantity for any well-formed study. In proteomics, it has become common practice to incorrectly conflate the protein FDR (the percent of identified proteins that are actually absent) with protein-level target-decoy, a particular method for estimating the protein-level FDR. In this manner, the challenges of one approach have been used as the basis for an argument that the field should abstain from protein-level FDR analysis altogether or even the suggestion that the very notion of a protein FDR is flawed. As we demonstrate in simple but accurate simulations, not only is the protein-level FDR an invariant concept, when analyzing large data sets, the failure to properly acknowledge it or to correct for multiple testing can result in large, unrecognized errors, whereby thousands of absent proteins (and, potentially every protein in the FASTA database being considered) can be incorrectly identified.

Solution to Statistical Challenges in Proteomics Is More Statistics, Not Less