Analogous to advances in deep learning, various neural network architectures have been used for de novo sequencing. Although categorizing these methods based on their model architecture is challenging due to the multi-component nature of many models, in what follows, we have identified two broad categories that capture many existing model architectures and have grouped the remaining methods into an "other" category. Additionally, we include two methods that use deep learning to post-process the results from an existing de novo sequencer.