Title:
METHODS FOR FLOW SPACE QUALITY SCORE PREDICTION BY NEURAL NETWORKS
Document Type and Number:
WIPO Patent Application WO/2019/140146
Kind Code:
A1
Abstract:
An artificial neural network is applied to a plurality of flow predictor features to generate a flow space probability of error for a base call. A base quality value for the base call is determined based on the flow space probability of error. The base call and flow predictor features are based on the flow space signal measurements generated in response to the nucleotide flow to the reaction confinement region. For an array of reaction confinement regions, a plurality of parallel neural networks is applied to produce a probability of error for each reaction confinement region. A given neural network of the parallel neural networks is applied to the plurality of flow predictor features corresponding to a given reaction confinement region in the array to provide the flow space probability of error for the given reaction confinement region.
Inventors:
WANG CHAO (US)
INGERMAN EUGENE (US)
INGERMAN EUGENE (US)
Application Number:
PCT/US2019/013127
Publication Date:
July 18, 2019
Filing Date:
January 11, 2019
Export Citation:
Assignee:
LIFE TECHNOLOGIES CORP (US)
International Classes:
G16B30/00; C12Q1/6869
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VLADIMĂR BOZA ET AL: "DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads", PLOS ONE, vol. 12, no. 6, 5 June 2017 (2017-06-05), pages e0178751, XP055576753, DOI: 10.1371/journal.pone.0178751
RONAGHI ET AL., SCIENCE, vol. 281, 1998, pages 363 - 365
MARGULIES ET AL., NATURE, vol. 437, 2005, pages 376 - 380
BRENT EWING; LADEANA W. HILLIER; MICHAEL C. WENDL; PHIL GREEN: "Base-calling of automated sequencer traces using Phred. I. accuracy assessment", GENOME RESEARCH, vol. 8, no. 3, 28 February 1998 (1998-02-28), pages 175 - 185
ANDERSON ET AL., SENSORS AND ACTUATORS B CHEM., vol. 129, 2008, pages 79 - 86
POURMAND ET AL., PROC. NATL. ACAD. SCI., vol. 103, 2006, pages 6466 - 6470
Attorney, Agent or Firm:
KOENIG, Carolyn (US)
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