Patent · US12217831B2 · B2 · US

Artificial intelligence-based quality scoring

(11) Publication number

US12217831B2

(21) Application number

18/296,125

(22) Filing date

2023-04-05

(30) Priority date

2019-03-21

(43) Publication date

2025-02-04

(45) Date of grant

2025-02-04

(51) IPC

G06F 18/213; G06F 16/907; G06F 18/21; G06F 18/214; G06F 18/23; G06F 18/23211; G06F 18/24; G06F 18/2415; G06F 18/2431; G06N 3/04; G06N 3/08; G06N 3/084; G06N 7/01; G06V 10/44; G06V 10/75; G06V 10/762; G06V 10/764; G06V 10/77; G06V 10/778; G06V 10/82; G06V 10/98; G16B 40/00; G16B 40/20; G06V 10/26; G06V 20/69

(52) CPC

G16B Bioinformatics, i.e. information and communication technology [ICT] specially adapted for genetic or protein-related data processing in computational molecular biology: 40/20, 20/20, 30/10, 30/20, 40/00, 40/10
G06F Electric digital data processing: 16/58, 16/907, 18/214, 18/217, 18/23, 18/23211, 18/24, 18/2415, 18/2431
G06N Computing arrangements based on specific computational models: 3/04, 3/044, 3/045, 3/0455, 3/0464, 3/048, 3/08, 3/084, 3/09, 5/046, 7/01
G06V Image or video recognition or understanding: 10/267, 10/454, 10/751, 10/763, 10/764, 10/7715, 10/7784, 10/82, 10/993, 20/47, 20/69, 2201/03

(73) Assignee

Illumina Inc

(72) Inventors

Kishore JAGANATHAN; John Randall GOBBEL; Amirali KIA

(54) Title

Artificial intelligence-based quality scoring

(57) Abstract

The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.

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Claims (20)

A system comprising: at least one processor; and a non-transitory computer readable medium storing instructions that, when executed by the at least one processor, cause the system to: identify one or more base calls for one or more analytes based on sequencing images captured at one or more sequencing cycles; feed, to an input layer of a neural network, input data representing the sequencing images; generate, based on the input data and utilizing an output layer of the neural network, one or more predicted quality indications for the one or more base calls; and determine, based on the one or more predicted quality indications, one or more quality predictions for the one or more base calls.
The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to feed the input data representing the sequencing images by feeding, to the input layer of the neural network: a first subset of per-cycle image patches depicting intensity emissions from a target cluster and one or more adjacent clusters at a preceding sequencing cycle occurring before a target sequencing cycle; a second subset of per-cycle image patches depicting intensity emissions from the target cluster and the one or more adjacent clusters at the target sequencing cycle; and a third subset of per-cycle image patches depicting intensity emissions from the target cluster and the one or more adjacent clusters at a subsequent sequencing cycle occurring after the target sequencing cycle.
The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to feed the input data representing the sequencing images by feeding, to the input layer of the neural network: supplemental distance information that identifies distances between pixels within the sequencing images; or image channel information identifying one or more image channels corresponding to the sequencing images.
The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to determine the one or more quality predictions for the one or more base calls by determining one or more quality scores for the one or more base calls.
The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: generate the one or more predicted quality indications by generating a first likelihood of a base call of the one or more base calls being a high quality, a second likelihood of the base call being a medium quality, and a third likelihood of the base call being a low quality; and based on the first likelihood, the second likelihood, and the second likelihood, determine the one or more quality predictions for the one or more base calls by classifying a quality of the base call as a high quality, a medium quality, or a low quality.
The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: generate the one or more predicted quality indications by generating quality-score likelihoods of a base call of the one or more base calls being assigned individual quality scores; and based on the quality-score likelihoods, determine the one or more quality predictions for the one or more base calls by assigning the base call a quality score from one of the individual quality scores.
The system of claim 1, wherein the neural network is a convolutional neural network.
The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to generate the one or more predicted quality indications for the one or more base calls by generating, utilizing a regression layer from the output layer, continuous values that identify a quality of the one or more base calls.
The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: feed, to a supplemental input module, one or more quality predictor values for the one or more base calls; and generate, utilizing the output layer of the neural network, the one or more predicted quality indications further based on the one or more quality predictor values.
The system of claim 9, wherein the one or more quality predictor values comprise one or more of online overlap, purity, phasing, start5, hexamer score, motif accumulation, endiness, approximate homopolymer, intensity decay, penultimate chastity, signal overlap with background (SOWB), shifted purity G adjustment, peak height, peak width, peak location, relative peak locations, peak height ration, peak spacing ration, or peak correspondence.
A non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause a system to: identify one or more base calls for one or more analytes based on sequencing images captured at one or more sequencing cycles; feed, to an input layer of a neural network, input data representing the sequencing images; generate, based on the input data and utilizing an output layer of the neural network, one or more predicted quality indications for the one or more base calls; and determine, based on the one or more predicted quality indications, one or more quality predictions for the one or more base calls.
The non-transitory computer readable medium of claim 11, further storing instructions that, when executed by the at least one processor, cause the system to feed the input data representing the sequencing images by feeding, to the input layer of the neural network: a first subset of per-cycle image patches depicting intensity emissions from a target cluster and one or more adjacent clusters at a preceding sequencing cycle occurring before a target sequencing cycle; a second subset of per-cycle image patches depicting intensity emissions from the target cluster and the one or more adjacent clusters at the target sequencing cycle; and a third subset of per-cycle image patches depicting intensity emissions from the target cluster and the one or more adjacent clusters at a subsequent sequencing cycle occurring after the target sequencing cycle.
The non-transitory computer readable medium of claim 11, further storing instructions that, when executed by the at least one processor, cause the system to determine the one or more quality predictions for the one or more base calls by determining one or more quality scores for the one or more base calls.
The non-transitory computer readable medium of claim 13, further storing instructions that, when executed by the at least one processor, cause the system to: generate the one or more predicted quality indications by generating a first likelihood of a base call of the one or more base calls being a high quality, a second likelihood of the base call being a medium quality, and a third likelihood of the base call being a low quality; and based on the first likelihood, the second likelihood, and the second likelihood, determine the one or more quality predictions for the one or more base calls by classifying a quality of the base call as a high quality, a medium quality, or a low quality.
The non-transitory computer readable medium of claim 11, further storing instructions that, when executed by the at least one processor, cause the system to: generate the one or more predicted quality indications by generating quality-score likelihoods of a base call of the one or more base calls being assigned individual quality scores; and based on the quality-score likelihoods, determine the one or more quality predictions for the one or more base calls by assigning the base call a quality score from one of the individual quality scores.
A computer-implemented method comprising: identifying one or more base calls for one or more analytes based on sequencing images captured at one or more sequencing cycles; feeding, to an input layer of a neural network, input data representing the sequencing images; generating, based on the input data and utilizing an output layer of the neural network, one or more predicted quality indications for the one or more base calls; and determining, based on the one or more predicted quality indications, one or more quality predictions for the one or more base calls.
The computer-implemented method of claim 16, wherein the neural network is a convolutional neural network.
The computer-implemented method of claim 16, wherein generating the one or more predicted quality indications for the one or more base calls comprises generating, utilizing a regression layer from the output layer, continuous values that identify a quality of the one or more base calls.
The computer-implemented method of claim 16, further comprising: feeding, to a supplemental input module, one or more quality predictor values for the one or more base calls; and generating, utilizing the output layer of the neural network, the one or more predicted quality indications further based on the one or more quality predictor values.
The computer-implemented method of claim 19, wherein the one or more quality predictor values comprise one or more of online overlap, purity, phasing, start5, hexamer score, motif accumulation, endiness, approximate homopolymer, intensity decay, penultimate chastity, signal overlap with background (SOWB), shifted purity G adjustment, peak height, peak width, peak location, relative peak locations, peak height ration, peak spacing ration, or peak correspondence.

Description

This application claims priority to or the benefit of the following applications:

The priority applications are hereby incorporated by reference for all purposes as if fully set forth herein.

The following are incorporated by reference for all purposes as if fully set forth herein:

US Non-Provisional Applications

S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “WAVENET: A GENERATIVE MODEL FOR RAW AUDIO,” arXiv:1609.03499, 2016;

S. Ö. Arik, M. Chrzanowski, A. Coates, G. Diamos, A. Gibiansky, Y. Kang, X. Li, J. Miler, A. Ng, J. Raiman, S. Sengupta and M. Shoeybi, “DEEP VOICE: REAL-TIME NEURAL TEXT-TOSPEECH,” arXiv:1702.07825, 2017;

Xie, W., et. al., “Microscopy cell counting and detection with fully convolutional regression networks”, Computer methods in biomechanics and biomedical engineering: Imaging & Visualization, 6(3), pp. 283-292, 2018;

Xie, Yuanpu, et al., “Beyond classification: structured regression for robust cell detection using convolutional neural network”, International Conference on Medical Image Computing and Computer - Assisted Intervention. October 2015, 12 pages;

Snuverink, I. A. F., “Deep Learning for Pixelwise Classification of Hyperspectral Images”, Master of Science Thesis, Delft University of Technology, 23 Nov. 2017, 19 pages;

Shevchenko, A., “Kras weighted categorical_crossentropy”, 1 page, [retrieved on 2019-01-15]. Retrieved from the Internet;

van den Assem, D. C. F., “Predicting periodic and chaotic signals using Wavenets”, Master of Science Thesis, Delft University of Technology, 18 Aug. 2017, pages 3-38;

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Record as JSON

{
  "publication_number": "US12217831B2",
  "country": "US",
  "kind": "B2",
  "title": "Artificial intelligence-based quality scoring",
  "abstract": "The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.",
  "claims": [
    "1. A system comprising: at least one processor; and a non-transitory computer readable medium storing instructions that, when executed by the at least one processor, cause the system to: identify one or more base calls for one or more analytes based on sequencing images captured at one or more sequencing cycles; feed, to an input layer of a neural network, input data representing the sequencing images; generate, based on the input data and utilizing an output layer of the neural network, one or more predicted quality indications for the one or more base calls; and determine, based on the one or more predicted quality indications, one or more quality predictions for the one or more base calls.",
    "2. The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to feed the input data representing the sequencing images by feeding, to the input layer of the neural network: a first subset of per-cycle image patches depicting intensity emissions from a target cluster and one or more adjacent clusters at a preceding sequencing cycle occurring before a target sequencing cycle; a second subset of per-cycle image patches depicting intensity emissions from the target cluster and the one or more adjacent clusters at the target sequencing cycle; and a third subset of per-cycle image patches depicting intensity emissions from the target cluster and the one or more adjacent clusters at a subsequent sequencing cycle occurring after the target sequencing cycle.",
    "3. The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to feed the input data representing the sequencing images by feeding, to the input layer of the neural network: supplemental distance information that identifies distances between pixels within the sequencing images; or image channel information identifying one or more image channels corresponding to the sequencing images.",
    "4. The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to determine the one or more quality predictions for the one or more base calls by determining one or more quality scores for the one or more base calls.",
    "5. The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: generate the one or more predicted quality indications by generating a first likelihood of a base call of the one or more base calls being a high quality, a second likelihood of the base call being a medium quality, and a third likelihood of the base call being a low quality; and based on the first likelihood, the second likelihood, and the second likelihood, determine the one or more quality predictions for the one or more base calls by classifying a quality of the base call as a high quality, a medium quality, or a low quality.",
    "6. The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: generate the one or more predicted quality indications by generating quality-score likelihoods of a base call of the one or more base calls being assigned individual quality scores; and based on the quality-score likelihoods, determine the one or more quality predictions for the one or more base calls by assigning the base call a quality score from one of the individual quality scores.",
    "7. The system of claim 1, wherein the neural network is a convolutional neural network.",
    "8. The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to generate the one or more predicted quality indications for the one or more base calls by generating, utilizing a regression layer from the output layer, continuous values that identify a quality of the one or more base calls.",
    "9. The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to: feed, to a supplemental input module, one or more quality predictor values for the one or more base calls; and generate, utilizing the output layer of the neural network, the one or more predicted quality indications further based on the one or more quality predictor values.",
    "10. The system of claim 9, wherein the one or more quality predictor values comprise one or more of online overlap, purity, phasing, start5, hexamer score, motif accumulation, endiness, approximate homopolymer, intensity decay, penultimate chastity, signal overlap with background (SOWB), shifted purity G adjustment, peak height, peak width, peak location, relative peak locations, peak height ration, peak spacing ration, or peak correspondence.",
    "11. A non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause a system to: identify one or more base calls for one or more analytes based on sequencing images captured at one or more sequencing cycles; feed, to an input layer of a neural network, input data representing the sequencing images; generate, based on the input data and utilizing an output layer of the neural network, one or more predicted quality indications for the one or more base calls; and determine, based on the one or more predicted quality indications, one or more quality predictions for the one or more base calls.",
    "12. The non-transitory computer readable medium of claim 11, further storing instructions that, when executed by the at least one processor, cause the system to feed the input data representing the sequencing images by feeding, to the input layer of the neural network: a first subset of per-cycle image patches depicting intensity emissions from a target cluster and one or more adjacent clusters at a preceding sequencing cycle occurring before a target sequencing cycle; a second subset of per-cycle image patches depicting intensity emissions from the target cluster and the one or more adjacent clusters at the target sequencing cycle; and a third subset of per-cycle image patches depicting intensity emissions from the target cluster and the one or more adjacent clusters at a subsequent sequencing cycle occurring after the target sequencing cycle.",
    "13. The non-transitory computer readable medium of claim 11, further storing instructions that, when executed by the at least one processor, cause the system to determine the one or more quality predictions for the one or more base calls by determining one or more quality scores for the one or more base calls.",
    "14. The non-transitory computer readable medium of claim 13, further storing instructions that, when executed by the at least one processor, cause the system to: generate the one or more predicted quality indications by generating a first likelihood of a base call of the one or more base calls being a high quality, a second likelihood of the base call being a medium quality, and a third likelihood of the base call being a low quality; and based on the first likelihood, the second likelihood, and the second likelihood, determine the one or more quality predictions for the one or more base calls by classifying a quality of the base call as a high quality, a medium quality, or a low quality.",
    "15. The non-transitory computer readable medium of claim 11, further storing instructions that, when executed by the at least one processor, cause the system to: generate the one or more predicted quality indications by generating quality-score likelihoods of a base call of the one or more base calls being assigned individual quality scores; and based on the quality-score likelihoods, determine the one or more quality predictions for the one or more base calls by assigning the base call a quality score from one of the individual quality scores.",
    "16. A computer-implemented method comprising: identifying one or more base calls for one or more analytes based on sequencing images captured at one or more sequencing cycles; feeding, to an input layer of a neural network, input data representing the sequencing images; generating, based on the input data and utilizing an output layer of the neural network, one or more predicted quality indications for the one or more base calls; and determining, based on the one or more predicted quality indications, one or more quality predictions for the one or more base calls.",
    "17. The computer-implemented method of claim 16, wherein the neural network is a convolutional neural network.",
    "18. The computer-implemented method of claim 16, wherein generating the one or more predicted quality indications for the one or more base calls comprises generating, utilizing a regression layer from the output layer, continuous values that identify a quality of the one or more base calls.",
    "19. The computer-implemented method of claim 16, further comprising: feeding, to a supplemental input module, one or more quality predictor values for the one or more base calls; and generating, utilizing the output layer of the neural network, the one or more predicted quality indications further based on the one or more quality predictor values.",
    "20. The computer-implemented method of claim 19, wherein the one or more quality predictor values comprise one or more of online overlap, purity, phasing, start5, hexamer score, motif accumulation, endiness, approximate homopolymer, intensity decay, penultimate chastity, signal overlap with background (SOWB), shifted purity G adjustment, peak height, peak width, peak location, relative peak locations, peak height ration, peak spacing ration, or peak correspondence."
  ],
  "description_excerpt": "This application claims priority to or the benefit of the following applications:\n\nThe priority applications are hereby incorporated by reference for all purposes as if fully set forth herein.\n\nThe following are incorporated by reference for all purposes as if fully set forth herein:\n\nUS Non-Provisional Applications\n\nS. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “WAVENET: A GENERATIVE MODEL FOR RAW AUDIO,” arXiv:1609.03499, 2016;\n\nS. Ö. Arik, M. Chrzanowski, A. Coates, G. Diamos, A. Gibiansky, Y. Kang, X. Li, J. Miler, A. Ng, J. Raiman, S. Sengupta and M. Shoeybi, “DEEP VOICE: REAL-TIME NEURAL TEXT-TOSPEECH,” arXiv:1702.07825, 2017;\n\nXie, W., et. al., “Microscopy cell counting and detection with fully convolutional regression networks”, Computer methods in biomechanics and biomedical engineering: Imaging & Visualization, 6(3), pp. 283-292, 2018;\n\nXie, Yuanpu, et al., “Beyond classification: structured regression for robust cell detection using convolutional neural network”, International Conference on Medical Image Computing and Computer - Assisted Intervention. October 2015, 12 pages;\n\nSnuverink, I. A. F., “Deep Learning for Pixelwise Classification of Hyperspectral Images”, Master of Science Thesis, Delft University of Technology, 23 Nov. 2017, 19 pages;\n\nShevchenko, A., “Kras weighted categorical_crossentropy”, 1 page, [retrieved on 2019-01-15]. Retrieved from the Internet;\n\nvan den Assem, D. C. F., “Predicting periodic and chaotic signals using Wavenets”, Master of Science Thesis, Delft University of Technology, 18 Aug. 2017, pages 3-38;",
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  "assignees": [
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  ],
  "inventors": [
    "Kishore JAGANATHAN",
    "John Randall GOBBEL",
    "Amirali KIA"
  ],
  "filing_date": "2023-04-05",
  "publication_date": "2025-02-04",
  "grant_date": "2025-02-04",
  "priority_date": "2019-03-21",
  "application_number": "US-202318296125-A",
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Record 251 of 8,000 in Patents full text (MLC-0201). Request the full dataset.