IL309786A - Quality score calibration of basecalling systems - Google Patents

Quality score calibration of basecalling systems

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Publication number
IL309786A
IL309786A IL309786A IL30978623A IL309786A IL 309786 A IL309786 A IL 309786A IL 309786 A IL309786 A IL 309786A IL 30978623 A IL30978623 A IL 30978623A IL 309786 A IL309786 A IL 309786A
Authority
IL
Israel
Prior art keywords
sensor data
range
clusters
computer
subset
Prior art date
Application number
IL309786A
Other languages
Hebrew (he)
Original Assignee
Illumina 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 US17/839,387 external-priority patent/US20230029970A1/en
Application filed by Illumina Inc filed Critical Illumina Inc
Publication of IL309786A publication Critical patent/IL309786A/en

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    • 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
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • 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
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing
    • 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
    • G06N3/08—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
    • G06N3/09—Supervised learning
    • 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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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Data Mining & Analysis (AREA)
  • Medical Informatics (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Biotechnology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Epidemiology (AREA)
  • Bioethics (AREA)
  • Public Health (AREA)
  • Analytical Chemistry (AREA)
  • Databases & Information Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Organic Chemistry (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Immunology (AREA)
  • Microbiology (AREA)
  • Signal Processing (AREA)
  • Biochemistry (AREA)
  • Genetics & Genomics (AREA)

Claims (20)

1.Claims 1. A computer-implemented method of generating base calls by a base caller, comprising: receiving, from a plurality of clusters within a region of a flow cell, a plurality of sensor data representing the plurality of clusters incorporating different nucleotide bases with different labels, wherein the plurality of sensor data is within a first range; identifying a second range, such that at least a threshold percentage of the plurality of sensor data are within the second range; mapping at least a subset of the plurality of sensor data, that are within the second range and that represent, from the region of the flow cell, a subset of the plurality of clusters incorporating different nucleotide bases with different labels, to a third range, thereby generating a plurality of normalized sensor data for a target cluster of the plurality of clusters; processing the plurality of normalized sensor data in a base caller, to determine, for the target cluster, a base call and a plurality of quality scores corresponding to the base call; and remapping each of at least a subset of the plurality of quality scores to a remapped quality score for the base call.
2. The computer-implemented method of claim 1, wherein the second range is fully encompassed within the first range.
3. The computer-implemented method of claim 1 or 2, wherein one or more outlier sensor data within the first range are absent from the second range of sensor data.
4. The computer-implemented method of any of claims 1-3, wherein identifying the second range comprises: identifying, within the first range, a low value, such that a lower threshold percentage of the plurality of sensor data have a value that is lower than the low value; and identifying, within the first range, a high value, such that an upper threshold percentage of the plurality of sensor data have a value that is higher than the high value, wherein the second range is defined by the low value and the high value.
5. The computer-implemented method of claim 4, wherein at least one of the lower threshold percentage or the upper threshold percentage is 0.5% or less.
6. The computer-implemented method of claim 4, wherein at least one of the lower threshold percentage or the upper threshold percentage is 1.0% or less.
7. The computer-implemented method of any of claims 4-6, wherein each of the lower threshold percentage and the upper threshold percentage is 0.5% or less.
8. The computer-implemented method of any of claims 4-6, wherein each of the lower threshold percentage and the upper threshold percentage is 1% or less.
9. The computer-implemented method of any of claims 4-8, further comprising: identifying (i) a first outlier sensor data of the plurality of sensor data representing the plurality of clusters incorporating different nucleotide bases with different labels that is lower than the low value and (ii) a second outlier sensor data of the plurality of sensor data representing the plurality of clusters incorporating different nucleotide bases with different labels that is higher than the high value; and prior to the mapping, assigning the low value to the first outlier sensor data, and assigning the high value to the second outlier sensor data, such that the first outlier sensor data and the second outlier sensor data are within the second range subsequent to the assignment.
10. The computer-implemented method of any of claims 4-9, further comprising: identifying (i) a first outlier sensor data of the plurality of sensor data representing the plurality of clusters incorporating different nucleotide bases with different labels that is lower than the low value and (ii) a second outlier sensor data of the plurality of sensor data representing the plurality of clusters incorporating different nucleotide bases with different labels that is higher than the high value; and excluding the first outlier sensor data and the second outlier sensor data from the subset of the plurality of sensor data representing the subset of the plurality of clusters incorporating different nucleotide bases with different labels during the mapping, for being outside the second range, such that the first outlier sensor data and the second outlier sensor data are not mapped to the third range.
11. The computer-implemented method of any of claims 1-10, wherein mapping at least a subset of the plurality of sensor data representing the subset of the plurality of clusters incorporating different nucleotide bases with different labels comprises: mapping a first sensor data within the subset from a first value that is within the second range to a second value that is within the third range; and mapping a second sensor data within the subset from a third value that is within the second range to a fourth value that is within the third range.
12. The computer-implemented method of any of claims 1-11, wherein individual sensor data of the plurality of sensor data representing the plurality of clusters incorporating different nucleotide bases with different labels comprises corresponding intensity of a corresponding section of an image generated from the flow cell.
13. The computer-implemented method of any of claims 1-12, further comprising: processing the plurality of normalized sensor data in a base caller, to assign, the corresponding base called for the target cluster, a first quality score indicating a first probability of the corresponding base being an A, a second quality score indicating a second probability of the corresponding base being a C, a third quality score indicating a third probability of the corresponding base being a T, and a fourth quality score indicating a fourth probability of the corresponding base being a G.
14. The computer-implemented method of claim 13, wherein the plurality of quality scores corresponding to the base call comprise the first quality score, the second quality score, the third quality score, and the fourth quality score.
15. The computer-implemented method of claim 14, further comprising: quantizing each of a plurality of remapped quality scores to a corresponding one of a plurality of quantized remapped quality score.
16. A non-transitory computer readable storage medium comprising computer program instructions that, when executed on a processor, cause a computing device to: receive, from a plurality of clusters within a region of a flow cell, a plurality of sensor data representing the plurality of clusters incorporating different nucleotide bases with different labels, wherein the plurality of sensor data is within a first range; identify a second range, such that at least a threshold percentage of the plurality of sensor data are within the second range; map at least a subset of the plurality of sensor data, that are within the second range and that represent, from the region of the flow cell, a subset of the plurality of clusters incorporating different nucleotide bases with different labels, to a third range, thereby generating a plurality of normalized sensor data for a target cluster of the plurality of clusters; process the plurality of normalized sensor data in a base caller, to determine, for the target cluster, a base call and a plurality of quality scores corresponding to the base call; and remap each of at least a subset of the plurality of quality scores to a remapped quality score for the base call.
17. The non-transitory computer readable storage medium of claim 16, wherein identifying the second range comprises: identifying, within the first range, a low value, such that a lower threshold percentage of the plurality of sensor data have a value that is lower than the low value; and identifying, within the first range, a high value, such that an upper threshold percentage of the plurality of sensor data have a value that is higher than the high value, wherein the second range is defined by the low value and the high value.
18. The non-transitory computer readable storage medium of claim 17, further comprising computer program instructions that, when executed on the processor, cause the computing device to quantize each of a plurality of remapped quality scores to a corresponding one of a plurality of quantized remapped quality score.
19. A system comprising: at least one processor; and a non-transitory computer-readable medium comprising instructions thereon that, when executed by the at least one processor, cause the system to: receive, from a plurality of clusters within a region of a flow cell, a plurality of sensor data representing the plurality of clusters incorporating different nucleotide bases with different labels, wherein the plurality of sensor data is within a first range; identify a second range, such that at least a threshold percentage of the plurality of sensor data are within the second range; map at least a subset of the plurality of sensor data, that are within the second range and that represent, from the region of the flow cell, a subset of the plurality of clusters incorporating different nucleotide bases with different labels, to a third range, thereby generating a plurality of normalized sensor data for a target cluster of the plurality of clusters; process the plurality of normalized sensor data in a base caller, to determine, for the target cluster, a base call and a plurality of quality scores corresponding to the base call; and remap each of at least a subset of the plurality of quality scores to a remapped quality score for the base call.
20. The system of claim 19, further comprising instructions that, when executed by the at least one processor, cause the system to quantize each of a plurality of remapped quality scores to a corresponding one of a plurality of quantized remapped quality score.
IL309786A 2021-07-28 2022-07-28 Quality score calibration of basecalling systems IL309786A (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US202163226707P 2021-07-28 2021-07-28
US17/839,387 US20230029970A1 (en) 2021-07-28 2022-06-13 Quality score calibration of basecalling systems
PCT/US2022/038729 WO2023009758A1 (en) 2021-07-28 2022-07-28 Quality score calibration of basecalling systems

Publications (1)

Publication Number Publication Date
IL309786A true IL309786A (en) 2024-02-01

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IL309786A IL309786A (en) 2021-07-28 2022-07-28 Quality score calibration of basecalling systems

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EP (1) EP4377960A1 (en)
JP (1) JP2024532049A (en)
KR (1) KR20240037882A (en)
AU (1) AU2022319125A1 (en)
CA (1) CA3223746A1 (en)
IL (1) IL309786A (en)
WO (1) WO2023009758A1 (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118053503B (en) * 2024-01-11 2024-12-06 中国农业科学院农业基因组研究所 A method and system for constructing a multi-omics database of invasive organisms

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Publication number Publication date
WO2023009758A1 (en) 2023-02-02
AU2022319125A1 (en) 2024-01-18
EP4377960A1 (en) 2024-06-05
CA3223746A1 (en) 2023-02-02
JP2024532049A (en) 2024-09-05
KR20240037882A (en) 2024-03-22

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