pValid: Validation Beyond the Target-Decoy Approach for Peptide Identification in Shotgun Proteomics

As
the de facto validation method in mass spectrometry-based proteomics,
the target-decoy approach determines a threshold to estimate the false
discovery rate and then filters those identifications beyond the threshold.
However, the incorrect identifications within the threshold are still
unknown and further validation methods are needed. In this study,
we characterized a framework of validation and investigated a number
of common and novel validation methods. We first defined the accuracy
of a validation method by its false-positive rate (FPR) and false-negative
rate (FNR) and, further, proved that a validation method with lower
FPR and FNR led to identifications with higher sensitivity and precision.
Then we proposed a validation method named pValid that incorporated
an open database search and a theoretical spectrum prediction strategy
via a machine-learning technology. pValid was compared with four common
validation methods as well as a synthetic peptide validation method.
Tests on three benchmark data sets indicated that pValid had an FPR
of 0.03% and an FNR of 1.79% on average, both superior to the other
four common validation methods. Tests on a synthetic peptide data
set also indicated that the FPR and FNR of pValid were better than
those of the synthetic peptide validation method. Tests on a large-scale
human proteome data set indicated that pValid successfully flagged
the highest number of incorrect identifications among all five methods.
Further considering its cost-effectiveness, pValid has the potential
to be a feasible validation tool for peptide identification.