To integrate this charge assignment neural network into a workflow that can assign an entire spectrum, we use an approach similar to THRASH and related algorithms. First, we centroided the data. Then, we selected clusters from the data. Isolated regions were selected around each cluster, similar to the training data. These isotope clusters were then encoded, and the charge state was predicted by the neural network algorithm. We allowed two possible charge states to be returned from the neural network in cases where the second highest position in the prediction vector is within 95% of the maximum position. A full description of parameters is provided in Table .