Oxford Nanopore Technologies Limited

Timothy Lee Massingham

Joseph Edward Harvey

A series of measurements taken from a polymer during translocation through a nanopore is analysed using a machine learning technique using a recurrent neural network (RNN). The RNN may derive posterior probability matrices each representing, in respect of different respective historical sequences of polymer units corresponding to measurements prior to the respective measurement, posterior probabilities of plural different changes to the respective historical sequence of polymer units giving rise to a new sequence of polymer units. Alternatively, the RNN may output decisions on the identity of successive polymer units of the series of polymer units, wherein the decisions are fed back into the recurrent neural network. The analysis may comprise performing convolutions of groups of consecutive measurements using a trained feature detector such as a convolutional neural network to derive a series of feature vectors, on which the RNN operates.

Machine learning analysis of nanopore measurements

Techniques for estimating a polymer sequence of a polymer based on a signal produced as a result of translocation of the polymer through a nanopore are described. The techniques may analyze portions of the signal to estimate whether there was a transition in the polymer sequence during each respective portion and which units of the sequence the transition was between. The techniques may comprise operation of one or more neural networks into which data from the signal may be input. The techniques may include generating a plurality of weights for a portion of the signal, wherein each weight is associated with a transition between labeled units of the polymer. The weights may be indicative of a likelihood that a transition occurred between a first of the labeled units to a second of the labeled units within the portion of the signal.

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Oxford, United Kingdom

Timothy Lee Massingham