IP Library › Granted Patent US 12,443,849
Granted Patent B2
US 12,443,849 · App. 17/180,513 · Granted Oct 14, 2025

Bus network for artificial intelligence-based base caller

Inventors: Anindita Dutta (San Francisco, CA); Gery Vessere (Oakland, CA); Dorna Kashefhaghighi (Menlo Park, CA); Gavin Derek Parnaby (Laguna Niguel, CA); Kishore Jaganathan (San Francisco, CA); Amirali Kia (San Mateo, CA)
Assignee: Illumina, Inc.
G06N3/084G06F18/23G06N3/063G06V10/454G06V10/762G06V10/764G06V10/7715G06V10/82G16B30/20C12Q1/6869
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Quick Facts
Patent No.
US 12,443,849
App. No.
17/180,513
Granted
Oct 14, 2025
Kind
B2
Abstract

The technology disclosed relates to a system that comprises a spatial convolution network and a temporal convolution network. The spatial convolution network is configured to process a window of per-cycle sequencing image sets and generate respective per-cycle spatial feature map sets. Trained coefficients of spatial convolution filters in spatial convolution filter banks of respective sequences of spatial convolution filter banks vary between sequences of spatial convolution layers in respective sequences of spatial convolution layers. The temporal convolution network is configured to process the per-cycle spatial feature map sets on a groupwise basis and generate respective per-group temporal feature map sets. Trained coefficients of temporal convolution filters in respective temporal convolution filter banks vary between temporal convolution filter banks in respective temporal convolution filter banks.

Claims (32)

1. A system, comprising:

a sequencing instrument;

at least one processor; and

a non-transitory computer-readable medium comprising instructions that, when executed by the at least one processor, cause the system to:

capture, by one or more light detectors of the sequencing instrument at imaging events of sequencing cycles of a sequencing run, per-cycle sequencing image sets comprising signals from clusters of oligonucleotides;

process, through a spatial convolution network, a window of per-cycle sequencing image sets for a series of sequencing cycles of the sequencing cycles on a cycle-by-cycle basis by separately processing respective per-cycle sequencing image sets in the window of per-cycle sequencing image sets through respective spatial processing pipelines;

form, utilizing a bus network connected to the spatial convolutional network, skip buses between spatial convolution layers within respective sequences of spatial convolution layers of the respective spatial processing pipelines, the skip buses configured to cause respective per-cycle spatial feature map sets generated by two or more spatial convolution layers processing the respective per-cycle sequencing image sets in a particular sequence of spatial convolution layer for a particular sequencing cycle to combine into a combined per-cycle spatial feature map set, and provide the combined per-cycle spatial feature map set as input to another spatial convolution layer in the particular sequence of spatial convolution layer; and

generate, utilizing an output layer, base call predictions for the clusters of oligonucleotides depicted in the per-cycle sequencing image sets captured by the one or more light detectors of the sequencing instrument based on a final combined per-cycle spatial feature map set generated by the spatial convolution network.

2. The system of claim 1 , wherein the two or more spatial convolution layers include a first spatial convolution layer and a third spatial convolution layer, wherein the first spatial convolution layer generates a first per-cycle spatial feature map set, wherein the first spatial convolution layer provides the first per-cycle spatial feature map set as input to a second spatial convolution layer, wherein the second spatial convolution layer processes the first per-cycle spatial feature map set and generates a second per-cycle spatial feature map set, wherein the second spatial convolution layer provides the second per-cycle spatial feature map set as input to the third spatial convolution layer, and wherein the third spatial convolution layer processes the second per-cycle spatial feature map set and generates a third per-cycle spatial feature map set.

3. The system of claim 2 , wherein the skip buses are further configured to cause the first per-cycle spatial feature map set and the third per-cycle spatial feature map set to combine into the combined per-cycle spatial feature map set.

4. The system of claim 3 , wherein the another spatial convolution layer is a fourth spatial convolution layer that immediately succeeds the third spatial convolution layer in the particular sequence of spatial convolution layer, wherein the fourth spatial convolution layer processes the combined per-cycle spatial feature map set as input.

5. The system of claim 2 , wherein the two or more spatial convolution layers include the first spatial convolution layer and a seventh spatial convolution layer, wherein the third spatial convolution layer provides the third per-cycle spatial feature map set as input to a fourth spatial convolution layer, wherein the fourth spatial convolution layer processes the third per-cycle spatial feature map set and generates a fourth per-cycle spatial feature map set, wherein the fourth spatial convolution layer provides the fourth per-cycle spatial feature map set as input to a fifth spatial convolution layer, and wherein the fifth spatial convolution layer processes the fourth per-cycle spatial feature map set and generates a fifth per-cycle spatial feature map set.

6. The system of claim 5 , wherein the skip buses are further configured to cause the first spatial feature map set and the fifth spatial feature map set to combine into the combined per-cycle spatial feature map set.

7. The system of claim 6 , wherein the another spatial convolution layer is a sixth spatial convolution layer that immediately succeeds the fifth spatial convolution layer in the particular sequence of spatial convolution layer, wherein the sixth spatial convolution layer processes the combined per-cycle spatial feature map set as input.

8. The system of claim 5 , wherein the two or more spatial convolution layers include the first spatial convolution layer, the third spatial convolution layer, and the fifth spatial convolution layer, and wherein the skip buses are further configured to cause the first per-cycle spatial feature map set, the third per-cycle spatial feature map set, and the fifth per-cycle spatial feature map set to combine into the combined per-cycle spatial feature map set.

9. The system of claim 8 , wherein the another spatial convolution layer is a sixth spatial convolution layer that processes the combined per-cycle spatial feature map set as input.

10. The system of claim 2 , wherein the skip buses are further configured to cause a per-cycle sequencing image set for the particular sequencing cycle, provided as input to the first spatial convolution layer, and the third per-cycle spatial feature map set to combine into the combined per-cycle spatial feature map set.

11. The system of claim 10 , wherein the another spatial convolution layer is a fourth spatial convolution layer that processes the combined per-cycle spatial feature map set as input.

12. The system of claim 1 , wherein the bus network is further configured to include dimensionality compatibility logic configured to modify spatial and depth dimensionality of an incoming per-cycle spatial feature map set that is combined with a receiving per-cycle spatial feature map set to generate the combined per-cycle spatial feature map set.

13. The system of claim 1 , wherein a computing device off-site from the sequencing instrument hosts or executes the bus network or the spatial convolutional network.

14. The system of claim 12 , wherein the bus network is further configured to include scaling logic configured to scale feature values of the incoming per-cycle spatial feature map set that is combined with the receiving per-cycle spatial feature map set to generate the combined per-cycle spatial feature map set.

15. The system of claim 1 , further comprising instructions that,

when executed by the at least one processor, cause the system to process, utilizing a temporal convolution network, the per-cycle spatial feature map sets on a groupwise basis by convolving on respective overlapping groups of per-cycle spatial feature map sets in the per-cycle spatial feature map sets using respective temporal convolution filter banks of a first temporal convolution layer to generate respective per-group temporal feature map sets for the respective overlapping groups of per-cycle spatial feature map sets.

16. The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to form, utilizing the temporal convolution network connected to the bus network, skip buses between temporal convolution layers within respective sequences of temporal convolution layers, the skip buses configured to cause respective per-cycle temporal feature map sets generated by two or more temporal convolution layers in a particular sequence of temporal convolution layer for a particular sequencing cycle to combine into a combined per-cycle temporal feature map set, and provide the combined per-cycle temporal feature map set as input to another temporal convolution layer in the particular sequence of temporal convolution layer.

17. An artificial intelligence-based method, including:

capturing, by one or more light detectors of a sequencing instrument at imaging events of sequencing cycles of a sequencing run, per-cycle sequencing image sets comprising signals from clusters of oligonucleotides;

processing, through a spatial convolution network, a window of per-cycle sequencing image sets for a series of sequencing cycles of a sequencing run on a cycle-by-cycle basis by separately processing respective per-cycle sequencing image sets in the window of per-cycle sequencing image sets through respective sequences of spatial convolution layers;

combining, utilizing skip buses within the spatial convolution network, respective per-cycle spatial feature map sets generated by processing the respective per-cycle sequencing image sets with two or more spatial convolution layers in a particular sequence of spatial convolution layer for a particular sequencing cycle into a combined per-cycle spatial feature map set, and providing the combined per-cycle spatial feature map set as input to another spatial convolution layer in the particular sequence of spatial convolution layer; and

generating, utilizing an output layer, base call predictions for the clusters of oligonucleotides based on a final combined per-cycle spatial feature map set generated by the spatial convolution network.

18. The artificial intelligence-based method of claim 17 , wherein the two or more spatial convolution layers include a first spatial convolution layer and a third spatial convolution layer, wherein the first spatial convolution layer generates a first per-cycle spatial feature map set, wherein the first spatial convolution layer provides the first per-cycle spatial feature map set as input to a second spatial convolution layer, wherein the second spatial convolution layer processes the first per-cycle spatial feature map set and generates a second per-cycle spatial feature map set, wherein the second spatial convolution layer provides the second per-cycle spatial feature map set as input to the third spatial convolution layer, and wherein the third spatial convolution layer processes the second per-cycle spatial feature map set and generates a third per-cycle spatial feature map set.

19. The artificial intelligence-based method of claim 18 , wherein the first spatial convolution layer and the third spatial convolution layer are further configured to cause the first per-cycle spatial feature map set and the third per-cycle spatial feature map set to combine into the combined per-cycle spatial feature map set.

20. The artificial intelligence-based method of claim 19 , wherein the another spatial convolution layer is a fourth spatial convolution layer that immediately succeeds the third spatial convolution layer in the particular sequence of spatial convolution layer, wherein the fourth spatial convolution layer processes the combined per-cycle spatial feature map set as input.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 10, 2021
From: DUTTA, ANINDITA; VESSERE, GERY; KASHEFHAGHIGHI, DORNA; PARNABY, GAVIN DEREK; JAGANATHAN, KISHORE; KIA, AMIRALI
To: ILLUMINA, INC.
Reel/Frame 056503/0410 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 10, 2021
From: VESSERE, GERY; DUTTA, ANINDITA; KASHEFHAGHIGHI, DORNA; JAGANATHAN, KISHORE; KIA, AMIRALI
To: ILLUMINA, INC.
Reel/Frame 056503/0600 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 10, 2021
From: PARNABY, GAVIN DEREK
To: ILLUMINA, INC.
Reel/Frame 056503/0663 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 10, 2021
From: VESSERE, GERY; DUTTA, ANINDITA; KASHEFHAGHIGHI, DORNA; JAGANATHAN, KISHORE; KIA, AMIRALI; PARNABY, GAVIN DEREK
To: ILLUMINA, INC.
Reel/Frame 056503/0952 →
Continuity (3)
Provisional Application 62979411 · Feb 20, 2020
Provisional Application 62979399 · Feb 20, 2020
Related Publication 20210264267A1 · Aug 26, 2021
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