Nanopore sequencing base calling
AssigneeThe Regents of the University of California
InventorsMarcus Stoiber, James Brown
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Abstract
Disclosed herein are systems and methods for nanopore sequencing basecalling. In one embodiment, the method can include: receiving raw nanopore sequencing data comprising a plurality of continuous data acquisition (DAC) values corresponding to a biomolecule; normalizing the raw nanopore sequencing data to generate normalized nanopore sequencing data comprising a plurality of normalized DAC values; generating, using a first neural network (NN) and a normalized DAC value of the plurality of normalized DAC values, a vector of transformed probability values, segmenting the plurality of normalized DAC values into a plurality of discrete events; generating, using a second neural network and the event vector, an element determination of the biomolecule.
Claims24
A system for nanopore sequencing basecalling, comprising:
- a membrane defining a pore wherethrough a biomolecule is threaded to generate raw nanopore sequencing data;
- a computer-readable memory storing executable instructions;
- and
- one or more hardware-based processors programmed by the executable instructions to perform steps comprising:
- receiving the raw nanopore sequencing data comprising a plurality of continuous data acquisition (DAC) values corresponding to the biomolecule;
- normalizing the raw nanopore sequencing data to generate normalized nanopore sequencing data comprising a plurality of normalized DAC values in a streaming manner;
- generating a vector of transformed probability values using a first neural network (NN) and a normalized DAC value of the plurality of normalized DAC values, wherein the first NN comprises a first recurrent neural network (RNN), wherein the first RNN comprises a first plurality of long short term memory (LSTM) layers, wherein a LSTM layer of the first plurality of LSTM layers comprises a first unidirectional LSTM layer, wherein the first unidirectional LSTM is followed by one or more fully connected layers;
- and
- determining a base in the biomolecule using a second NN and the generated vector of transformed probability vectors, wherein the second NN comprises a second RNN, wherein the second RNN comprises a second plurality of LSTM layers, and wherein a LSTM layer of the second plurality of LSTM layers comprises a second unidirectional LSTM layer, wherein the second unidirectional LSTM is followed by one or more fully connected layers.
A method of nanopore sequence basecalling, comprising:
- providing a membrane defining a pore wherethrough a biomolecule is threaded to generate raw nanopore sequencing data;
- receiving the raw nanopore sequencing data comprising a plurality of continuous data acquisition (DAC) values corresponding to the biomolecule;
- normalizing the raw nanopore sequencing data to generate normalized nanopore sequencing data comprising a plurality of normalized DAC values in a streaming manner;
- generating a vector of transformed probability values using a first neural network (NN), and a normalized DAC value of the plurality of normalized DAC values, wherein the first NN comprises a first recurrent neural network (RNN), wherein the first RNN comprises a first plurality of long short term memory (LSTM) layers, wherein a LSTM layer of the first plurality of LSTM layers comprises a first unidirectional LSTM layer, wherein the first unidirectional LSTM is followed by one or more fully connected layers;
- and
- determining a base in the biomolecule using a second NN and the generated vector of transformed probability vectors, wherein the second NN comprises a second RNN, wherein the second RNN comprises a second plurality of long short term memory (LSTM) layers, and wherein a LSTM layer of the second plurality of LSTM layers comprises a second unidirectional LSTM layer, wherein the second unidirectional LSTM is followed by one or more fully connected layers.
Dependencies are inferred from the claim text.