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IL299077B2 - Tile placement and/or cycle-based weight set selection for base naming - Google Patents

Tile placement and/or cycle-based weight set selection for base naming

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Publication number
IL299077B2
IL299077B2 IL299077A IL29907722A IL299077B2 IL 299077 B2 IL299077 B2 IL 299077B2 IL 299077 A IL299077 A IL 299077A IL 29907722 A IL29907722 A IL 29907722A IL 299077 B2 IL299077 B2 IL 299077B2
Authority
IL
Israel
Prior art keywords
weight set
sensor data
weights
neural network
sensing cycles
Prior art date
Application number
IL299077A
Other languages
Hebrew (he)
Other versions
IL299077A (en
IL299077B1 (en
Original Assignee
Illumina Inc
Illumina Software Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US17/687,551 external-priority patent/US20220301657A1/en
Application filed by Illumina Inc, Illumina Software Inc filed Critical Illumina Inc
Priority claimed from PCT/US2022/020460 external-priority patent/WO2022197752A1/en
Publication of IL299077A publication Critical patent/IL299077A/en
Publication of IL299077B1 publication Critical patent/IL299077B1/en
Publication of IL299077B2 publication Critical patent/IL299077B2/en

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/048—Activation functions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0495—Quantised networks; Sparse networks; Compressed networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis

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  • General Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
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  • Bioinformatics & Cheminformatics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biotechnology (AREA)
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  • Computer Vision & Pattern Recognition (AREA)
  • Public Health (AREA)
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  • Analytical Chemistry (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Physiology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
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Claims (20)

1. A system, comprising: a host processor; memory accessible by the host processor storing a topology of a neural network, first, second, and third weight sets for configuring the topology to execute a base calling operation, the first, second, and third weight sets respectively corresponding to first, second, and third subseries of sensing cycles divided from a series of sensing cycles, wherein the first, second, and third weight sets are determined using respective training data generated during the first, second, and third subseries of sensing cycles, and first, second, and third sensor data respectively corresponding to the first, second, and third subseries of sensing cycles; and a configurable processor having access to the memory and configured with data flow logic to load the topology on processing elements of the configurable processor, load the first sensor data on the processing elements, load the first weight set on the processing elements to configure the topology with weights in the first weight set, and cause the neural network to apply the weights in the first weight set on the first sensor data to produce first base call classification data for sensing cycles in the first subseries of sensing cycle, load the second sensor data on the processing elements, load the second weight set on the processing elements to configure the topology with weights in the second weight set, and cause the neural network to apply the weights in the second weight set on the second sensor data to produce second base call classification data for sensing cycles in the second subseries of sensing cycle, and load the third sensor data on the processing elements, load the third weight set on the processing elements to configure the topology with weights in the third weight set, and cause the neural network to apply the weights in the third weight set on the third sensor data to produce third base call classification data for sensing cycles in the third subseries of sensing cycle.
2. The system of claim 1, wherein the memory further stores: fourth, fifth, and subsequent weight sets for configuring the topology to execute a base calling operation, the fourth, fifth, and subsequent weight sets respectively corresponding to fourth, fifth, and subsequent subseries of sensing cycles in the series of sensing cycles; and fourth, fifth, and subsequent sensor data for the fourth, fifth, and subsequent subseries of sensing cycles.
3. The system of claim 1 or 2, wherein the configurable processor is configured with data flow logic to: load the fourth sensor data on the processing elements, load the fourth weight set on the processing elements to configure the topology with weights in the fourth weight set, and cause the neural network to apply the weights in the fourth weight set on the fourth sensor data to produce fourth base call classification data for sensing cycles in the fourth subseries of sensing cycle; load the fifth sensor data on the processing elements, load the fifth weight set on the processing elements to configure the topology with weights in the fifth weight set, and cause the neural network to apply the weights in the fifth weight set on the fifth sensor data to produce fifth base call classification data for sensing cycles in the fifth subseries of sensing cycle; and load the subsequent sensor data, and the subsequent weight set on the processing elements to configure the topology with weights in the subsequent weight set, and to cause the neural network to apply the weights in the subsequent weight set on the subsequent sensor data to produce subsequent base call classification data for sensing cycles in the subsequent subseries of sensing cycle.
4. The system of any of claims 1-3, wherein the topology takes, as input, sensor data from successive sensing cycles, and the topology includes spatial layers that do not combine the sensor data and resulting feature maps between the successive sensing cycles, and temporal layers that combine resulting feature maps between the successive sensing cycles.
5. The system of any of claims 1-4, wherein the first weight set includes first spatial weights for the spatial layers and first temporal weights for the temporal layers, the second weight set includes second spatial weights for the spatial layers and second temporal weights for the temporal layers, and the third weight set includes third spatial weights for the spatial layers and third temporal weights for the temporal layers.
6. The system of any of claims 1-5, wherein the first weight set includes spatial weights for the spatial layers and first temporal weights for the temporal layers, the second weight set includes second temporal weights for the temporal layers, and the third weight set includes third temporal weights for the temporal layers, and wherein the configurable processor is configured with data flow logic to: load the first sensor data on the processing elements, load the spatial weights and the first temporal weights on the processing elements to configure the spatial layers with the spatial weights and the temporal layers with the first temporal weights, and cause the neural network to apply the configured spatial and temporal layers on the first sensor data to produce first base call classification data for sensing cycles in the first subseries of sensing cycle; load the second sensor data on the processing elements, load the second temporal weights on the processing elements to reconfigure the temporal layers with weights in the second temporal weights, without reconfiguring the spatial layers, and cause the neural network to apply the reconfigured temporal layers and the previously configured spatial layers on the second sensor data to produce second base call classification data for sensing cycles in the second subseries of sensing cycle; and load the third sensor data on the processing elements, load the third temporal weights on the processing elements to reconfigure the temporal layers with weights in the third temporal weights, without reconfiguring the spatial layers, and cause the neural network to apply the reconfigured temporal layers and the previously configured spatial layers on the third sensor data to produce third base call classification data for sensing cycles in the third subseries of sensing cycle.
7. The system of any of claims 1-6, wherein weights in the first, second, and third weight sets are quantized using different scaling factors.
8. The system of any of claims 1-7, wherein weights in the first, second, and third weight sets respectively correspond to first, second, and third sequencing chemistries.
9. The system of any of claims 1-8, wherein weights in the first, second, and third weight sets respectively correspond to first, second, and third sequencing assays.
10. The system of any of claims 1-9, wherein weights in the first, second, and third weight sets respectively correspond to first, second, and third sequencing configurations.
11. A computer-implemented method for generating base call classification data, comprising: loading a topology of a neural network on processing elements of a processor, the processor to execute base call operations; storing (i) first sensor data from clusters corresponding to first one or more tile locations of a flow cell, (ii) second sensor data from clusters corresponding to second one or more tile locations of the flow cell, (iii) a first weight set comprising first one or more weights, and (iv) a second weight set comprising second one or more weights, wherein the first sensor data and the second sensor data are generated during a subset of sensing cycles divided from a series of sensing cycles, and wherein the first weight set and the second weight set are determined using respective training data corresponding to the first one or more tile locations and the second one or more tile locations of the flow cell; configuring the topology of the neural network with the first weight set, and causing the neural network configured with the first weight set to process the first sensor data and to produce first base call classification data for the first one or more tiles and for the subset of sensing cycles; and configuring the topology of the neural network with the second weight set, and causing the neural network configured with the second weight set to process the second sensor data and to produce second base call classification data for the second one or more tiles and for the subset of sensing cycles.
12. The method of claim 11, wherein the subset of sensing cycles is a first subset of sensing cycles, and wherein the method further comprises: storing (i) third sensor data from clusters within the first one or more tiles, (ii) fourth sensor data from clusters within the second one or more tiles, (iii) a third weight set, and (iv) a fourth weight set, wherein the third sensor data and the fourth sensor data are generated during a second subset of sensing cycles in the series of sensing cycles, the second subset of sensing cycles subsequent to the first subset of sensing cycles in the series of sensing cycles; configuring the topology of the neural network with the third weight set, and causing the neural network configured with the third weight set to process the third sensor data and to produce third base call classification data for the first one or more tiles and for the second subset of sensing cycles; and configuring the topology of the neural network with the fourth weight set, and causing the neural network configured with the fourth weight set to process the fourth sensor data and to produce fourth base call classification data for the second one or more tiles and for the second subset of sensing cycles.
13. The method of claim 11 or 12, wherein: the first one or more tiles are within a first area of the flow cell; and the second one or more tiles are within a second area of the flow cell.
14. The method of any of claims 11-13, wherein: the first one or more tiles are edge tiles of the flow cell; and the second one or more tiles are non-edge tiles of the flow cell.
15. The method of any of claims 11-14, further comprising: generating the first weight set by training the neural network on sensor data generated solely from edge tiles; and generating the second weight set by training the neural network on sensor data generated solely from non-edge tiles.
16. A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause a system to: load a topology of a neural network on processing elements of a processor, the processor to execute base call operations; store (i) first sensor data from clusters corresponding to first one or more tile locations of a flow cell, (ii) second sensor data from clusters corresponding to second one or more tile locations of the flow cell, (iii) a first weight set comprising first one or more weights, and (iv) a second weight set comprising second one or more weights, wherein the first sensor data and the second sensor data are generated during a subset of sensing cycles divided from a series of sensing cycles, and wherein the first weight set and the second weight set are determined using respective training data corresponding to the first one or more tile locations and the second one or more tile locations of the flow cell; configure the topology of the neural network with the first weight set, and cause the neural network configured with the first weight set to process the first sensor data and to produce first base call classification data for the first one or more tiles and for the subset of sensing cycles; and configure the topology of the neural network with the second weight set, and cause the neural network configured with the second weight set to process the second sensor data and to produce second base call classification data for the second one or more tiles and for the subset of sensing cycles.
17. The non-transitory computer readable storage medium recited in claim 16, wherein the subset of sensing cycles is a first subset of sensing cycles, and further comprising instructions, that when executed by the at least one processor, cause the system to: store (i) third sensor data from clusters within the first one or more tiles, (ii) fourth sensor data from clusters within the second one or more tiles, (iii) a third weight set, and (iv) a fourth weight set, wherein the third sensor data and the fourth sensor data are generated during a second subset of sensing cycles in the series of sensing cycles, the second subset of sensing cycles subsequent to the first subset of sensing cycles in the series of sensing cycles; configure the topology of the neural network with the third weight set, and cause the neural network configured with the third weight set to process the third sensor data and to produce third base call classification data for the first one or more tiles and for the second subset of sensing cycles; and configure the topology of the neural network with the fourth weight set, and cause the neural network configured with the fourth weight set to process the fourth sensor data and to produce fourth base call classification data for the second one or more tiles and for the second subset of sensing cycles.
18. The non-transitory computer readable storage medium recited in claim 16 or 17, wherein: the first one or more tiles are within a first area of the flow cell; and the second one or more tiles are within a second area of the flow cell.
19. The non-transitory computer readable storage medium recited in claim 16-18, wherein: the first one or more tiles are edge tiles of the flow cell; and the second one or more tiles are non-edge tiles of the flow cell.
20. The non-transitory computer readable storage medium recited in claim 16-19, further comprising instructions, that when executed by the at least one processor, cause the system to: generate the first weight set by training the neural network on sensor data generated solely from edge tiles; and generate the second weight set by training the neural network on sensor data generated solely from non-edge tiles.
IL299077A 2021-03-16 2022-03-15 Tile placement and/or cycle-based weight set selection for base naming IL299077B2 (en)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
US202163161896P 2021-03-16 2021-03-16
US202163161880P 2021-03-16 2021-03-16
US17/687,551 US20220301657A1 (en) 2021-03-16 2022-03-04 Tile location and/or cycle based weight set selection for base calling
US17/687,583 US12525320B2 (en) 2021-03-16 2022-03-04 Neural network parameter quantization for base calling
PCT/US2022/020460 WO2022197752A1 (en) 2021-03-16 2022-03-15 Tile location and/or cycle based weight set selection for base calling

Publications (3)

Publication Number Publication Date
IL299077A IL299077A (en) 2023-02-01
IL299077B1 IL299077B1 (en) 2025-07-01
IL299077B2 true IL299077B2 (en) 2025-11-01

Family

ID=85057463

Family Applications (1)

Application Number Title Priority Date Filing Date
IL299077A IL299077B2 (en) 2021-03-16 2022-03-15 Tile placement and/or cycle-based weight set selection for base naming

Country Status (7)

Country Link
EP (2) EP4309080A1 (en)
JP (2) JP7726929B2 (en)
KR (1) KR20230157230A (en)
CN (2) CN115699019A (en)
AU (2) AU2022237501A1 (en)
CA (2) CA3183567A1 (en)
IL (1) IL299077B2 (en)

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
NL2018852B1 (en) 2017-05-05 2018-11-14 Illumina Inc Optical distortion correction for imaged samples
GB201819378D0 (en) * 2018-11-28 2019-01-09 Oxford Nanopore Tech Ltd Analysis of nanopore signal using a machine-learning technique
US11783917B2 (en) 2019-03-21 2023-10-10 Illumina, Inc. Artificial intelligence-based base calling
US11210554B2 (en) * 2019-03-21 2021-12-28 Illumina, Inc. Artificial intelligence-based generation of sequencing metadata
CA3104851A1 (en) 2019-05-16 2020-11-19 Illumina, Inc. Base calling using convolutions

Also Published As

Publication number Publication date
IL299077A (en) 2023-02-01
JP2024510539A (en) 2024-03-08
IL299077B1 (en) 2025-07-01
KR20230157230A (en) 2023-11-16
EP4309179A1 (en) 2024-01-24
AU2022238841A1 (en) 2023-02-02
CN115803815A (en) 2023-03-14
AU2022237501A1 (en) 2023-02-02
CN115699019A (en) 2023-02-03
JP7726929B2 (en) 2025-08-20
CA3183581A1 (en) 2022-09-22
EP4309080A1 (en) 2024-01-24
CA3183567A1 (en) 2022-09-22
JP2025179067A (en) 2025-12-09

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