Machine learning enabled pulse and base calling for sequencing devices
AssigneeQuantum-Si Incorporated
InventorsJonathan M. Rothberg, Michael Meyer, Umut Eser
Life of this patent
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Abstract
A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.
Claims24
A method for identifying nucleotides of a nucleic acid, the method comprising:
- using at least one computer hardware processor to perform:
- accessing data obtained from detected light emissions by luminescent labels associated with nucleotides during nucleotide incorporation events for the nucleic acid, wherein the light emissions are responsive to a series of light pulses and the data includes numbers of photons detected after each of at least some of the light pulses;
- and
- providing the data as input to a trained deep learning model to obtain output identifying nucleotides of the nucleic acid, wherein providing the data as input to the trained deep learning model comprises arranging the data into a data structure having columns, wherein:
- a first column holds a number of photons in each of a first and second time interval in a first time period after a first light pulse in the series of light pulses;
- and
- a second column holds a number of photons in each of a first and second time interval in a second time period after a second light pulse in the series of light pulses.
A system for identifying nucleotides of a nucleic acid, the system comprising:
- at least computer hardware processor;
- and
- at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
- accessing data obtained from detected light emissions by luminescent labels associated with nucleotides during nucleotide incorporation events for the nucleic acid, wherein the light emissions are responsive to a series of light pulses and the data includes numbers of photons detected after each of at least some of the light pulses;
- and
- providing the data as input to a trained deep learning model to obtain output identifying nucleotides of the nucleic acid, wherein providing the data as input to the trained deep learning model comprises arranging the data into a data structure having columns, wherein:
- a first column holds a number of photons in each of a first and second time interval in a first time period after a first light pulse in the series of light pulses;
- and
- a second column holds a number of photons in each of a first and second time interval in a second time period after a second light pulse in the series of light pulses.
At least one non-transitory computer-readable storage medium storing instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform:
- accessing data obtained from detected light emissions by luminescent labels associated with nucleotides during nucleotide incorporation events for the nucleic acid, wherein the light emissions are responsive to a series of light pulses and the data includes numbers of photons detected after each of at least some of the light pulses;
- and
- providing the data as input to a trained deep learning model to obtain output identifying nucleotides of the nucleic acid, wherein providing the data as input to the trained deep learning model comprises arranging the data into a data structure having columns, wherein:
- a first column holds a number of photons in each of a first and second time interval in a first time period after a first light pulse in the series of light pulses;
- and
- a second column holds a number of photons in each of a first and second time interval in a second time period after a second light pulse in the series of light pulses.
A method for identifying nucleotides of a nucleic acid, the method comprising:
- using at least one computer hardware processor to perform:
- accessing data obtained from detected light emissions by luminescent labels associated with nucleotides, the light emissions responsive to a series of light pulses, the data including, for each of at least some of the light pulses, a respective number of photons detected in each of a plurality of intervals in a time period after the light pulse;
- and
- providing the data as input to a trained machine learning model to obtain output identifying nucleotides of the nucleic acid, wherein providing the data as input to the trained machine learning model comprises arranging the data into a data structure having columns, wherein:
- a first column holds a number of photons in each of a first and second time interval in a first time period after a first light pulse in the series of light pulses;
- and
- a second column holds a number of photons in each of a first and second time interval in a second time period after a second light pulse in the series of light pulses.
Dependencies are inferred from the claim text.