Illumina Corporation

Other

Broadcom Corporation

Avago Technologies International Sales Pte. Limited

Illumina Software, Inc.

Gavin Derek Parnaby

Eric Jon Ojard

Rami Mehio

John S Vieceli

Nitin Udpa

Abde Ali Hunaid Kagalwalla

Bo Lu

John S Wang

Dorna Kashefhaghighi

Michael Q Le

Vasudevan Parthasarathy

Amirali Kia

Chung-Jue Chen

Kishore Jaganathan

Anindita Dutta

Gery Vessere

The technology disclosed relates to a system that comprises a spatial convolution network and a temporal convolution network. The spatial convolution network is configured to process a window of per-cycle sequencing image sets and generate respective per-cycle spatial feature map sets. Trained coefficients of spatial convolution filters in spatial convolution filter banks of respective sequences of spatial convolution filter banks vary between sequences of spatial convolution layers in respective sequences of spatial convolution layers. The temporal convolution network is configured to process the per-cycle spatial feature map sets on a groupwise basis and generate respective per-group temporal feature map sets. Trained coefficients of temporal convolution filters in respective temporal convolution filter banks vary between temporal convolution filter banks in respective temporal convolution filter banks.

Bus network for artificial intelligence-based base caller

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.

State-based base calling

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.

Intensity extraction for feature values in base calling

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.

Equalizer-based intensity correction for base calling

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.

Inter-cluster intensity variation correction and base calling

Intensity extraction from oligonucleotide clusters for base calling

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.

Intensity extraction and crosstalk attenuation using interpolation and adaptation for base calling

The technology disclosed generates variation correction coefficients on a cluster-by-cluster basis to correct inter-cluster intensity profile variation for improved base calling. An amplification coefficient corrects scale variation. Channel-specific offset coefficients correct shift variation along respective intensity channels. The variation correction coefficients for a target cluster are generated based on combining analysis of historic intensity data generated for the target cluster at preceding sequencing cycles of a sequencing run with analysis of current intensity data generated for the target cluster at a current sequencing cycle of the sequencing run. The variation correction coefficients are then used to correct next intensity data generated for the target cluster at a next sequencing cycle of the sequencing run. The corrected next intensity data is then used to base call the target cluster at the next sequencing cycle.

Systems and methods for per-cluster intensity correction and base calling

The technology disclosed attenuates spatial crosstalk from sequencing images for base calling. In particular, the technology disclosed accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters. The pixels include a center pixel that contains a center of the target cluster. Each pixel in the pixels is divisible into a plurality of subpixels. Depending upon a particular subpixel, in a plurality of subpixels of the center pixel, which contains the center of the target cluster, the technology disclosed selects, from a bank of subpixel lookup tables, a subpixel lookup table that corresponds to the particular subpixel. The selected subpixel lookup table contains pixel coefficients that are configured to maximizes a signal-to-noise ratio. The technology disclosed element-wise multiplies the pixel coefficients with the pixels and determines a weighted sum.

Equalization-based image processing and spatial crosstalk attenuator

Methods, systems, and apparatuses, including electrical circuitry, are described for auto-negotiation. Active cables, active backplanes, and line cards may include one or more instances of electrical circuitry and/or integrated circuits that intercept advertisements of standard auto-negotiation protocol signaling from an initiating device for establishment of communication links with a receiving device. Auto negotiation information in the intercepted signaling may be translated and encoded into signaling in accordance with the capabilities of the receiving device. Active cables and active backplanes may also include one or more connection components between instances of electrical circuitry and/or integrated circuits to provide high-speed transmission of data packets encapsulating the auto-negotiation information in a format that differs from the standard auto-negotiation protocol.

Apparatus, system, and method for auto-negotiation

Methods and apparatuses are described for timing skew mitigation in time-interleaved ADCs (TI-ADCs) that may be performed for any receive signal without any special signals during blind initialization, which may be followed by background calibration. The same gain/skew calibration metrics may be applied to baud sampled and oversampled systems, including wideband receivers and regardless of any modulation, by applying a timing or frequency offset to non-stationary sampled signals during initial training. Skew mitigation is low latency, low power, low area, noise tolerant and scalable. Digital estimation may be implemented with accumulators and multipliers while analog calibration may be implemented with adjustable delays. DC and gain offsets may be calibrated before skew calibration. The slope of the correlation function between adjacent samples may be used to move a timing skew estimate stochastically at a low adaptive rate until the skew algorithm converges.

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Laguna Niguel, CA, United States of America

Gavin Derek Parnaby