Patents by Inventor Gavin Derek PARNABY

Gavin Derek PARNABY has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11853396
    Abstract: The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.
    Type: Grant
    Filed: January 13, 2023
    Date of Patent: December 26, 2023
    Assignee: Illumina, Inc.
    Inventors: Eric Jon Ojard, Abde Ali Hunaid Kagalwalla, Rami Mehio, Nitin Udpa, Gavin Derek Parnaby, John S Vieceli
  • Publication number: 20230385991
    Abstract: The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.
    Type: Application
    Filed: May 8, 2023
    Publication date: November 30, 2023
    Inventors: Eric Jon OJARD, Rami MEHIO, Gavin Derek PARNABY, Nitin UDPA, John S. VIECELI
  • Publication number: 20230368866
    Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can configure a field programmable gate array (FPGA) or other configurable processor to implement a neural network and train the neural network using the configurable processor by modifying certain network parameters of a subset of the neural network’s layers. For instance, the disclosed systems can configure a configurable processor on a computing device to implement a base-calling-neural network (or other neural network) that includes different sets of layers. Based on a set of images of oligonucleotide clusters or other datasets, the neural network generates predicted classes, such as by generating nucleobase calls for oligonucleotide clusters. Based on the predicted classes, the disclosed systems subsequently modify certain network parameters for a subset of the neural network’s layers, such by modifying parameters for a set of top layers.
    Type: Application
    Filed: May 10, 2023
    Publication date: November 16, 2023
    Inventor: Gavin Derek Parnaby
  • Publication number: 20230343415
    Abstract: This disclosures describes embodiments of methods, systems, and non-transitory computer readable media that accurately and efficiently estimate the effects of phasing and pre-phasing for a particular cluster of oligonucleotides and determining a cluster-specific-phasing correction for the cluster. For instance, the disclosed systems can dynamically identify clusters of oligonucleotides exhibiting error-inducing sequences that frequently cause phasing or pre-phasing. When the disclosed systems detect signals during cycles at read positions following such an error-inducing sequence, the disclosed systems can generate cluster-specific-phasing coefficients and correct the signals according to such cluster-specific-phasing coefficients. For instance, the disclosed system can utilize a linear equalizer, decision feedback equalizer, or a maximum likelihood sequence estimator to generate cluster-specific-phasing coefficients.
    Type: Application
    Filed: November 28, 2022
    Publication date: October 26, 2023
    Inventors: Eric Jon Ojard, John S. Vieceli, Gavin Derek Parnaby, Bo Lu, Rami Mehio
  • Publication number: 20230343414
    Abstract: We disclose a computer-implemented method of base calling. The technology disclosed accesses a time series sequence of a read. Respective time series elements in the time series sequence represent respective bases in the read. Then, a composite sequence for the read is generated based on respective aggregate transformations of respective sliding windows of time series elements in the time series sequence. A subject composite element in the composite sequence is generated based on an aggregate transformation of a corresponding window of time series elements in the time series sequence. Then, the composite sequence is processed as an aggregate and generates a base call sequence that has respective base calls for the respective bases in the read.
    Type: Application
    Filed: March 24, 2023
    Publication date: October 26, 2023
    Inventors: Gery Vessere, Anindita Dutta, Gavin Derek Parnaby
  • Publication number: 20230298339
    Abstract: The technology disclosed relates to state-based base calling. In particular, the technology disclosed relates to incorporating state information about data from previous sequencing cycles into the analysis of data from a current sequencing cycle when generating a base call for the current sequencing cycle. For example, when generating a base call for an Nth sequencing cycle, the technology disclosed can incorporate into the base calling logic state information about data from sequencing cycles 1 to N?1.
    Type: Application
    Filed: September 14, 2022
    Publication date: September 21, 2023
    Applicants: Illumina, Inc., Illumina Software, Inc.
    Inventors: Gavin Derek PARNABY, Eric Jon OJARD, Dorna KASHEFHAGHIGHI
  • Publication number: 20230259588
    Abstract: The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.
    Type: Application
    Filed: January 13, 2023
    Publication date: August 17, 2023
    Inventors: Eric Jon OJARD, Abde Ali Hunaid KAGALWALLA, Rami MEHIO, Nitin UDPA, Gavin Derek PARNABY, John S. VIECELI
  • Patent number: 11694309
    Abstract: The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.
    Type: Grant
    Filed: November 9, 2021
    Date of Patent: July 4, 2023
    Assignee: Illumina, Inc.
    Inventors: Eric Jon Ojard, Rami Mehio, Gavin Derek Parnaby, Nitin Udpa, John S. Vieceli
  • Publication number: 20230207050
    Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine learning model to recalibrate nucleotide base calls (e.g., variant calls) of a call generation model. For instance, the disclosed systems can train and utilize a call recalibration machine learning model to generate a set of predicted variant call classifications based on sequencing metrics associated with a sample nucleotide sequence. Leveraging the set of variant call classifications, the disclosed systems can further update or modify nucleotide base calls (e.g., variant calls) corresponding to genomic coordinates, such as multiallelic genomic coordinates, haploid genomic coordinates, and genomic coordinates indicated (by the call generation model) to exhibit homozygous reference genotypes.
    Type: Application
    Filed: December 28, 2021
    Publication date: June 29, 2023
    Inventor: Gavin Derek Parnaby
  • Publication number: 20230087698
    Abstract: The technology disclosed includes a system. The system includes a spatial convolutional neural network configured to process sequencing images of clusters, and produce spatially convolved features, a filtering logic configured to select, from the spatially convolved features, a subset of spatially convolved features that contain centers of the clusters, a compression logic configured to compress the subset of spatially convolved features into a set of compressed features, a contextualization logic configured to access state information for compressed features in the set of compressed features, a temporal convolutional neural network configured to process the set of stateful compressed features, and produce temporally convolved stateful features, and a base calling logic configured to generate base calls for the clusters based on the temporally convolved stateful features.
    Type: Application
    Filed: September 14, 2022
    Publication date: March 23, 2023
    Applicants: Illumina, Inc., Illumina Software, Inc.
    Inventors: Gavin Derek PARNABY, Eric Jon OJARD, Dorna KASHEFHAGHIGHI
  • Patent number: 11593595
    Abstract: The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.
    Type: Grant
    Filed: May 24, 2022
    Date of Patent: February 28, 2023
    Inventors: Eric Jon Ojard, Abde Ali Hunaid Kagalwalla, Rami Mehio, Nitin Udpa, Gavin Derek Parnaby, John S. Vieceli
  • Publication number: 20230041989
    Abstract: A method of base calling using at least two base callers is disclosed. The method includes executing at least a first base caller and a second base caller on sensor data generated for sensing cycles in a series of sensing cycles; generating, by the first base caller, first classification information associated with the sensor data, based on executing the first base caller on the sensor data; and generating, by the second base caller, second classification information associated with the sensor data, based on executing the second base caller on the sensor data. In an example, based on the first classification information and the second classification information, a final classification information is generated, where the final classification information includes one or more base calls for the sensor data.
    Type: Application
    Filed: July 28, 2022
    Publication date: February 9, 2023
    Applicant: ILLUMINA SOFTWARE, INC.
    Inventors: Gavin Derek PARNABY, Mark David HAHM
  • Publication number: 20230021577
    Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine learning model to recalibrate nucleotide-base calls (e.g., variant calls) of a call-generation model. For instance, the disclosed systems can train and utilize a call-recalibration-machine-learning model to generate a set of predicted variant-call classifications based on sequencing metrics associated with a sample nucleotide sequence. Leveraging the set of variant-call classifications, the disclosed systems can further update or modify nucleotide-base calls (e.g., variant calls) corresponding to genomic coordinates.
    Type: Application
    Filed: July 23, 2021
    Publication date: January 26, 2023
    Inventors: Gavin Derek Parnaby, Arun Visvanath, Antoine Jean Dejong
  • Publication number: 20230018469
    Abstract: We disclose a system. The system comprises a memory and a runtime logic. The memory stores a plurality of specialist signal profilers. Each specialist signal profiler in the plurality of specialist signal profilers is trained to maximize signal-to-noise ratio of sequenced signals in a particular signal profile detected for analytes in a particular analyte class and characterized in a particular training data set. The runtime logic, having access to the memory, is configured to execute a base calling operation by applying respective specialist signal profilers in the plurality of specialist signal profilers to sequenced signals in respective signal profiles detected for analytes in respective analyte classes during the base calling operation.
    Type: Application
    Filed: June 13, 2022
    Publication date: January 19, 2023
    Applicant: ILLUMINA SOFTWARE, INC.
    Inventors: Abde Ali Hunaid KAGALWALLA, Eric Jon OJARD, Rami MEHIO, Gavin Derek PARNABY, Nitin UDPA, John S. VIECELI
  • Publication number: 20230015945
    Abstract: The technology disclosed extracts intensities from sequencing images for base calling target clusters and attenuates spatial crosstalk from neighboring clusters. The technology disclosed accesses a particular section from a plurality of sections of an image output by a sensor, the particular section of the image including at least one pixel depicting intensity emission values from a target cluster and neighboring clusters located across the sensor, and convolves the particular section of the image with a corresponding convolution kernel in a plurality of convolution kernels, to generate a feature map comprising a plurality of feature values. The technology disclosed further assigns a corresponding feature value to the target cluster based on feature values in the plurality of feature values adjoining a center of the target cluster, and processes the corresponding feature value assigned to the target cluster, to base call the target cluster.
    Type: Application
    Filed: September 2, 2022
    Publication date: January 19, 2023
    Applicant: ILLUMINA SOFTWARE, INC.
    Inventors: Abde Ali Hunaid KAGALWALL, Eric Jon OJARD, Rami MEHIO, Gavin Derek PARNABY, Nitin UDPA, Bo LU, John S. VIECELI
  • Publication number: 20220319641
    Abstract: Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently detect when bubbles impact nucleic-acid-sequencing runs based on data captured during (or derived from) base calls during sequencing runs. In particular, in one or more embodiments, the disclosed systems receive data identifying nucleobase calls and data identifying quality metrics for the nucleobase calls during sequencing cycles. Based on particular nucleobase calls and threshold markers for the quality metrics, the disclosed system utilizes a machine-learning-model to detect a presence of a bubble in a nucleotide-sample slide. Beyond simply detecting the presence of a bubble, the disclosed system can also classify different detected bubbles, such as air bubbles, oil bubbles, or ghost bubbles, or other outputs during sequencing.
    Type: Application
    Filed: March 23, 2022
    Publication date: October 6, 2022
    Inventors: BRANDON TYLER WESTERBERG, JUNQI YUAN, ROBERT EZRA LANGLOIS, MARK DAVID HAHM, GAVIN DEREK PARNABY, THOMAS GROS
  • Patent number: 11455487
    Abstract: The technology disclosed attenuates spatial crosstalk from sequencing images for base calling. The technology disclosed accesses a section of an image output by a biosensor, where the section of the image includes a plurality of pixels depicting intensity emission values from a plurality of clusters within the biosensor and from locations within the biosensor that are adjacent to the plurality of clusters. The plurality of clusters includes a target cluster. The section of the image is convolved with a convolution kernel, to generate a feature map comprising a plurality of features having a corresponding plurality of feature values. A weighted feature value is assigned to the target cluster, where the weighted feature value is based on one or more features values of the plurality of feature values of the feature map. The weighted feature value assigned to the target cluster is processed, to base call the target cluster.
    Type: Grant
    Filed: October 26, 2021
    Date of Patent: September 27, 2022
    Assignee: Illumina Software, Inc.
    Inventors: Abde Ali Hunaid Kagalwalla, Eric Jon Ojard, Rami Mehio, Gavin Derek Parnaby, Nitin Udpa, Bo Lu, John S. Vieceli
  • Publication number: 20220300811
    Abstract: A method of quantizing parameters of a neural network includes grouping a plurality of parameters of a neural network in a plurality of groups. Each group of the plurality of groups includes corresponding two or more parameters of the plurality of parameters. In an example, for each group, a corresponding quantization format is selected from a plurality of available quantization formats, such that a first quantization format selected for at least a first group is different from a second quantization format selected for at least a second group. For each group, individual parameters within the corresponding group are quantized using the quantization format selected for the corresponding group. The quantized parameters of the plurality of groups are stored in a memory.
    Type: Application
    Filed: March 4, 2022
    Publication date: September 22, 2022
    Applicant: ILLUMINA SOFTWARE, INC.
    Inventor: Gavin Derek PARNABY
  • Publication number: 20220300772
    Abstract: The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.
    Type: Application
    Filed: May 24, 2022
    Publication date: September 22, 2022
    Applicant: ILLUMINA, INC.
    Inventors: Eric Jon Ojard, Abde Ali Hunaid Kagalwalla, Rami Mehio, Nitin Udpa, Gavin Derek Parnaby, John S. Vieceli
  • Publication number: 20220301657
    Abstract: A system for base calling includes memory storing a topology of a neural network, a plurality of weights sets, and sensor data for a series of sensing cycles. Sequencing events span temporal progression of the base calling operation through subseries of sensing cycles, and spatial progression of the base calling operation through locations on a biosensor. A configurable processor is configured to load the topology on the configurable processor, select a weight set in dependence upon a subject subseries of sensing cycles and/or a subject location on the biosensor, load subject sensor data for the subject subseries of sensing cycles and the subject location on the processing elements, configure the topology using the selected weight set, and cause the neural network to process the subject sensor data to produce base call classification data for the subject subseries and the subject location.
    Type: Application
    Filed: March 4, 2022
    Publication date: September 22, 2022
    Applicants: Illumina, Inc., Illumina Software, Inc.
    Inventors: Gavin Derek PARNABY, Mark David HAHM, Andrew Christopher DU PREEZ, Dorna KASHEFHAGHIGHI, Kishore JAGANATHAN