In contrast, IsoDec and FLASHDeconv had the highest percentage of fragments matched. These two tools produced between 40-60% fragments matched, while TopFD yielded between 25-40% (Figure ). Interestingly, the 8-bin and the 4-bin model of IsoDec performed very similarly on these metrics. Overall, IsoDec exhibited a balanced combination of sequence coverage and fragments matched, demonstrating its ability to accurately assign isotopic envelopes and do so without loss of sensitivity. Importantly, IsoDec achieves this balance across the different fragmentation types and mass analyzers represented in these spectra. One important question is whether this improvement in accuracy came from the neural network charge prediction or from the filtering that was performed after assignment. To test this, we implemented THRASH-like charge assignment in Python that replaced only the neural network piece of the IsoDec workflow and repeated the same evaluation discussed above. THRASH uses a combination of Fourier and Patterson charge maps to assign charges to isotope clusters, and it is the basis for TopFD. Although the fragments matched was consistently slightly lower, the sequence coverage and percentage of fragments matched were largely when replacing the neural network with Fourier/Patterson charge assignment (Figure ). However, sequence coverage and percentage of fragments matched are imperfect metrics because not all true isotope clusters are from terminal fragments of the protein. There can be true isotope clusters from contaminants or non-terminal fragments that are real but do not match a theoretical terminal fragment. To address this limitation, we performed manual annotation of the selected spectra so that a precision-recall curve, as presented by McIlwain et al., could be constructed to more thoroughly investigate performance. A clear difference in performance between neural network models and THRASH charge assignment can be observed in the precision-recall curve (Figure ). IsoDec with the 8-and 4bin models had optimized F1 values of 0.69 and 0.67, respectively. Under optimal parameters the 8-bin model produced precision and recall values of 0.77 and 0.63, and the 4-bin model yielded 0.74 and 0.60. Here, it is clearer that the 8-bin model outperforms the 4-bin model. Both outperform IsoDec with THRASH-like charge prediction, which had an optimal F1 value of 0.64, with a precision of 0.72 and a recall of 0.58. These results show that there is an advantage to using the neural net for charge assignment over existing strategies.