FIGURE 1.

Schematics of NanoReviser. (A) Structure of NanoReviser model building. (B) Structure of the main model. The preprocessed raw electrical signals (on the right) were passed through Identity Block and joined with the results of the preprocessed read input (on the left), passing through two bidirectional long short-term memory (Bi-LSTM) layers, and then the combination of the raw electrical signal features and read features were fed into the following Bi-LSTM layers. Finally, after the formation of two fully connected layers, the model gave a probability distribution of called bases. (C) Structure of Residential Block. Residential Block consisted of two convolutional layers and two batch normalization layers, which were used to accelerate the training speed. Conv stands for a convolutional layer and 1 × 3 was the size of the kernel used by the convolutional layer. (D) Structure of Identity Block. Identity Block consisted of three Residential Blocks. Conv stands for a convolutional layer and BN is the abbreviation for Batch Normalization.