US 12,437,184 B2
Machine learning analysis of nanopore measurements
Timothy Lee Massingham, Oxford (GB); and Joseph Edward Harvey, Oxford (GB)
Assigned to Oxford Nanopore Technologies PLC, Oxford (GB)
Appl. No. 16/610,897
Filed by Oxford Nanopore Technologies PLC, Oxford (GB)
PCT Filed May 4, 2018, PCT No. PCT/GB2018/051208
§ 371(c)(1), (2) Date Nov. 4, 2019,
PCT Pub. No. WO2018/203084, PCT Pub. Date Nov. 8, 2018.
Claims priority of application No. 1707138 (GB), filed on May 4, 2017.
Prior Publication US 2020/0309761 A1, Oct. 1, 2020
Prior Publication US 2024/0370696 A2, Nov. 7, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/044 (2023.01); C12Q 1/6869 (2018.01); G01N 33/487 (2006.01); G06F 18/22 (2023.01); G06N 7/01 (2023.01)
CPC G06N 3/044 (2023.01) [G01N 33/48721 (2013.01); G06F 18/22 (2023.01); G06N 7/01 (2023.01); C12Q 1/6869 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of high-rate sequencing of polymers using a nanopore measurement and analysis system, the method comprising:
placing a polymer into the nanopore measurement and analysis system; and
sequencing the polymer using the nanopore measurement and analysis system at least in part by:
translocating at least a portion of the polymer through a nanopore of the nanopore measurement and analysis system at a sequencing rate in the range of 10-1000 polymer units per second, wherein the sequencing rate reflects a rate at which the polymer translocates through the nanopore;
measuring, using the nanopore measurement and analysis system, electrical signals generated during the translocating of the polymer through the nanopore, at a sampling rate greater than or equal to the sequencing rate, to generate a series of measurements;
estimating, using a convolutional neural network comprising a convolutional layer and a recurrent neural network comprising a bidirectional recurrent layer, the bidirectional recurrent layer comprising a plurality of long short-term memory (LSTM) units, a series of polymer units within the polymer at least in part by:
converting the series of measurements into a series of feature vectors by applying the convolutional neural network comprising the convolutional layer to a series of groups of measurements derived from the series of measurements, wherein the series of feature vectors include a first feature vector corresponding to a first group of measurements in the series of groups of measurements;
processing the series of feature vectors using the recurrent neural network comprising the bidirectional recurrent layer to obtain respective outputs including a first output corresponding to a first feature vector in the series of feature vectors, wherein the first output represents, in respect of different respective historical sequences of polymer units corresponding to measurements obtained prior or subsequent to the first group of measurements, posterior probabilities of plural different changes to the respective historical sequences of polymer units; and
generating the estimate of the series of polymer units within the polymer using the respective outputs obtained using the recurrent neural network comprising the bidirectional recurrent layer.