Fig. 3.

Schematic overview of the algorithms underlying nanopore base callers. a Nanocall uses a Hidden Markov Model (HMM) for base calling. b DeepNano was the first base caller to use Recurrent Neural Networks (RNN). h1–h3 represent three hidden layers in the RNN. c BasecRAWller uses two RNNs, one to segment the raw measurements and one to infer k-mer probabilities. d Chiron makes use of a Convolutional Neural Network (CNN) to detect patterns in the data, followed by an RNN to predict k-mer probabilities, which are evaluated by a Connectionist Temporal Classification (CTC) decoder. LSTM long-short-term memory