The first deep learning model for de novo peptide sequencing, DeepNovo , uses an iterative decoding process that employs two parallel models. Each spectrum in the training set is converted to vector format by discretizing the m/z axis, using bins of size 0.1 or 0.01 m/z depending on the resolution of the training data. For the first of the two models, ion-CNN, these vectors are processed along with the predicted prefix to produce a tensor of dimension 128×26×8×10, where 128 is the batch size, 26 is the amino acid alphabet size (including post-translational modifications [PTMs]), 8 is the number of ion types (including b-and y-ions and various neutral losses), and 10 is the number of m/z bins extracted around each target ion. This tensor is then processed by ion-CNN, a two-layer CNN that takes the spectrum and a representation of a predicted peptide prefix as input, aiming to predict the subsequent amino acid. The second model, a type of recurrent neural network (RNN) known as a "long short-term memory" (LSTM) network , is preceded by a twolayer CNN, spectrum-CNN, to detect signals indicative of which amino acids are present in the spectrum. Using the CNN outputs, the LSTM then iteratively predicts amino acids in a manner similar to the ion-CNN model. During decoding, the penultimate layers of these two models, ion-CNN and the LSTM, are concatenated and fed through a single, fully-connected neural network layer, which outputs a 26-dimensional vector of unscaled log probabilities (logits), serving as the per-amino-acid prediction. DeepNovo also employs a dynamic programming post-processor that uses the predicted logits and the knapsack algorithm to ensure that the predicted peptide sequence has a mass within the specified tolerance of the measured precursor mass. As the first deep learning method in the field, the DeepNovo paper is cited by all subsequent de novo sequencing papers (Figure ). DeepNovo-DIA generalizes the DeepNovo model to work with data generated using data-independent acquisition (DIA). Unlike traditional data-dependent acquisition (DDA) mass spectrometry data, where the precursors selected for fragmentation and MS2 characterization are selected in a data-driven way, DIA selects precursors according to a predetermined schedule. Consequently, DIA spectra are more complex than DDA spectra. On the other hand, in DDA each peptide species is typically observed once, whereas the DIA precursor schedule is designed to measure each peptide multiple times. De novo methods for analysis of DIA data must take into account these properties of DIA data. The core of the DeepNovo-DIA model is similar to DeepNovo, including the ion-CNN, spectrum-CNN, and LSTM components. The primary difference is that, because DIA data can be organized along a temporal axis, with multiple adjacent scans containing information about a given analyte, the preprocessing steps in DeepNovo-DIA involve detecting 3D fragment ion features and 2D precursor ion features. In practice, training or applying DeepNovo-DIA requires first processing the DIA MS1 data to detect precursor features using an external tool, after which the model makes a prediction for each such feature. Subsequent work by a different research group described an alternative, active learning strategy for training the DeepNovo-DIA model .