US 11,783,917 B2
Artificial intelligence-based base calling
Kishore Jaganathan, San Francisco, CA (US); John Randall Gobbel, Brisbane, CA (US); and Amirali Kia, San Mateo, CA (US)
Filed by Illumina, Inc., San Diego, CA (US)
Filed on Mar. 20, 2020, as Appl. No. 16/826,126.
Claims priority of provisional application 62/821,602, filed on Mar. 21, 2019.
Claims priority of provisional application 62/821,618, filed on Mar. 21, 2019.
Claims priority of provisional application 62/821,681, filed on Mar. 21, 2019.
Claims priority of provisional application 62/821,724, filed on Mar. 21, 2019.
Claims priority of provisional application 62/821,766, filed on Mar. 21, 2019.
Prior Publication US 2020/0302297 A1, Sep. 24, 2020
Int. Cl. G06V 10/82 (2022.01); G06K 9/62 (2022.01); G06N 3/08 (2023.01); G16B 40/00 (2019.01); G06N 3/04 (2023.01); G06F 16/907 (2019.01); G06N 3/084 (2023.01); G06N 7/00 (2023.01); G06V 10/75 (2022.01); G06N 5/046 (2023.01)
CPC G06K 9/6218 (2013.01) [G06F 16/907 (2019.01); G06K 9/628 (2013.01); G06K 9/6222 (2013.01); G06K 9/6232 (2013.01); G06K 9/6256 (2013.01); G06K 9/6262 (2013.01); G06K 9/6267 (2013.01); G06K 9/6277 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06N 7/005 (2013.01); G06V 10/751 (2022.01); G06V 10/82 (2022.01); G16B 40/00 (2019.02); G06N 5/046 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method of base calling clusters, including:
processing input data through a neural network and producing an alternative representation of the input data,
wherein the input data includes (i) per-cycle data for each of one or more sequencing cycles of a sequencing run and (ii) supplemental distance information,
wherein the per-cycle data comprises pixels that depict intensity emissions indicative of one or more clusters and of a surrounding background captured at a respective one of the one or more sequencing cycles,
wherein the per-cycle data is accompanied with the supplemental distance information that identifies distances between the pixels of the per-cycle data;
wherein, during the processing of the pixels of the per-cycle data by the neural network, the supplemental distance information supplies additive bias that conveys to the neural network which of the pixels of the per-cycle data contain centers of clusters and which of the pixels of the per-cycle data are separated from the centers of the clusters;
processing the alternative representation through an output layer and producing an output; and
base calling one or more of the clusters at the one or more sequencing cycles based on the output.