Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate

Abstract

Decoy database search with target-decoy competition (TDC) provides an intuitive, easy-to-implement method for estimating the false discovery rate (FDR) associated with spectrum identifications from shotgun proteomics data. However, the procedure can yield different results for a fixed data set analyzed with different decoy databases, and this decoy-induced variability is particularly problematic for smaller FDR thresholds, data sets, or databases. The average TDC (aTDC) protocol combats this problem by exploiting multiple independently shuffled decoy databases to provide an FDR estimate with reduced variability. We provide a tutorial introduction to aTDC, describe an improved variant of the protocol that offers increased statistical power, and discuss how to deploy aTDC in practice using the Crux software toolkit.