Perhaps the most obvious question to ask about the 26 deep learning de novo sequencing methods listed in Table is, "Which method works the best?" Unfortunately, answering this question conclusively is challenging for several reasons. First, each study uses an idiosyncratic subset of the performance measures outlined above, and sometimes different measures identify different top-performing methods even within the same study. Second, each study uses different datasets, both for training their models and for evaluating them. These datasets may contain different levels and types of noise in the observed spectra, as well as differing rates and types of errors in the peptide assignments, making direct comparisons of performance measures across datasets challenging. Furthermore, this variety in datasets leads to a third problem: the initial question is not adequately formulated. A more precise framing of the question might be, "Which method works best when all methods are trained using exactly the same training data?" And as this reframing suggests, the answer may vary depending on the training data used. For example, a model with many millions of parameters may perform best when trained on many millions of peptide-spectrum matches (PSMs) and may perform poorly when trained on a relatively small dataset. Additionally, some methods, such as AdaNovo , which focuses on improving PTM prediction, may require corresponding data sets for successful training and representative evaluation. On the other hand, if we insist on asking, "Which method works best?" in an absolute sense, without controlling for the training data, we immediately encounter the difficulty of ensuring that the train and test sets do not overlap at the peptide level.