US 12,469,581 B2
Machine learning enabled pulse and base calling for sequencing devices
Jonathan M. Rothberg, Miami Beach, FL (US); Michael Meyer, Guilford, CT (US); and Umut Eser, Lexington, MA (US)
Assigned to Quantum-Si Incorporated, Guilford, CT (US)
Filed by Quantum-Si Incorporated, Guilford, CT (US)
Filed on Dec. 15, 2022, as Appl. No. 18/082,487.
Application 18/082,487 is a continuation of application No. 16/258,299, filed on Jan. 25, 2019, granted, now 11,538,556, issued on Dec. 27, 2022.
Claims priority of provisional application 62/622,754, filed on Jan. 26, 2018.
Prior Publication US 2023/0207062 A1, Jun. 29, 2023
Int. Cl. G01N 33/48 (2006.01); G01N 21/64 (2006.01); G01N 33/50 (2006.01); G06N 3/047 (2023.01); G06N 20/00 (2019.01); G16B 30/10 (2019.01); G16B 30/20 (2019.01); G16B 40/10 (2019.01); G16B 40/20 (2019.01); G16B 45/00 (2019.01); C12Q 1/6869 (2018.01); C12Q 1/6874 (2018.01); G16B 40/30 (2019.01)
CPC G16B 30/10 (2019.02) [G01N 21/6428 (2013.01); G06N 3/047 (2023.01); G06N 20/00 (2019.01); G16B 30/20 (2019.02); G16B 40/10 (2019.02); G16B 40/20 (2019.02); G16B 45/00 (2019.02); C12Q 1/6869 (2013.01); C12Q 1/6874 (2013.01); G16B 40/30 (2019.02)] 19 Claims
OG exemplary drawing
 
1. A method for identifying nucleotides of a nucleic acid molecular composition about a sample, the method comprising:
using at least one computer hardware processor to perform:
accessing data obtained from detected light emissions by the sample after excitation, wherein the light emissions are responsive to a series of excitation light pulses and the data includes numbers of photons detected after each of at least some of the light pulses;
organizing the data as a data structure, wherein the data structure is a matrix or image, wherein each value in the matrix is representative of a number of photons detected within at least one time interval after at least some of the light pulses, and wherein each pixel of the image is representative of a number of photons detected within one of the intervals after one of the at least some of the light pulses;
providing the data structure as input to a trained deep learning model, wherein the deep learning model is trained to:
(a) extract one or more features from the data structure, and
(b) based on the one or more features, determine at least one probability value for a possible class, the possible class comprising data related to molecular composition of the sample, for each feature; and
receiving, from the deep learning model, data related to molecular composition of the sample.