The standard method for evaluating these de novo sequencing methods is to use a gold standard produced via database search. In this approach, mass spectrometry data derived from a single species is searched against the reference proteome for that species, yielding a ranked list of peptide-spectrum matches (PSMs). Including in the peptide database a collection of reversed or shuffled "decoy" peptides provides a rigorous way to set a threshold in this list of PSMs while controlling the false discovery rate (FDR) among the PSMs above the threshold . The resulting set of high-confidence PSMs can be used either to train or evaluate a de novo sequencing model. Some version of the above protocol has been used to develop labeled training and validation data for essentially every published deep learning de novo sequencing method. One exception is methods that use spectra from synthesized peptide sequences for training . However, even in these cases, a gold standard derived from database search is used for evaluation of the method.