From: From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy

Term | Description | Reference(s) |
---|---|---|

Beam search |
A heuristic search algorithm. In Chiron, the beam search decoder with beam width W maintains a list of the W most probable sequences up to position i and constructs the probabilities of all possible sequence extensions for i + 1.
| [32] |

Connectionist Temporal Classification (CTC) decoder | A type of neural network output and scoring for labeling sequence data with RNNs. It does not require presegmented training data and postprocessed outputs. | [56] |

Convolutional Neural Network (CNN) | A type of neural network often used for image analysis. It can recognize patterns by applying different filters to an image. | [57] |

Forward algorithm |
An algorithm that computes the probability P(x) of a sequence x given a certain HMM.
| [58] |

Hidden Markov Model (HMM) | A stochastic model that models a sequence of unobserved events underlying a sequence of observations. HMMs assume that an event only depends on the previous event. | [58, 59] |

Long-short-term memory (LSTM) unit | A type of RNN that can be used as a building block in bigger networks. It has specific input, output, and forgot gates that allow it to retain or discard information that was passed on from a previous state. | [60, 61] |

Partial Order Alignment (POA) graph | A graph representation of a multiple alignment that allows each base in the alignment to have multiple predecessors. Different paths through the graph represent different alignments. | [62] |

Recurrent Neural Network (RNN) | A type of neural network that takes information passed on from previous states into account. | [63] |

Training data | A dataset that is used to optimize (i.e., train) the parameters of a model. Training is required for both HMMs and RNNs. The training dataset thus determines the performance of the model. | [58, 63] |

Viterbi decoding | An algorithm that finds the most likely sequence of events given a certain HMM. | [58] |