Illumina Corporation

Illumina Cambridge Limited

Amirali Kia

Anindita Dutta

Dorna Kashefhaghighi

Kishore Jaganathan

Molly Min He

Christian Gloeckner

John Randall Gobbel

Trina Faye Osothprarop

Erin Bomati

Ramesh Vaidyanathan

Haiying Li Grunenwald

Scott Kuersten

Darin J Haskins

Gery Vessere

Joshua Burgess

Anupama Khanna

Daniel Schlingman

Kevin L Gunderson

Frank J Steemers

Tarun Kumar Khurana

Craig M Ciesla

Stanley S Hong

Jerome J Jendrisak

Ali Agah

Sasan Amini

Aathavan Karunakaran

Merek C Siu

Niall Anthony Gormley

Kai-How Farh

Igor Yu Goryshin

Jason Richard Betley

Rigo Pantoja

Gavin Derek Parnaby

Dmitry K Pokholok

Fan Zhang

Natalie Morrell

Wouter Meuleman

Rosamond Jackson

Gareth Jenkins

Avgousta Ioannou

Jacqueline Weir

Steven J Norberg

Igor Goryshin

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 compresses a larger, teacher base caller into a smaller, student base caller. The student base caller has fewer processing modules and parameters than the teacher base caller. The teacher base caller is trained using hard labels (e.g., one-hot encodings). The trained teacher base caller is used to generate soft labels as output probabilities during the inference phase. The soft labels are used to train the student base caller.

Knowledge distillation and gradient pruning-based compression of artificial intelligence-based base caller

The technology disclosed processes input data through a neural network and produces an alternative representation of the input data. The input data includes per-cycle image data for each of one or more sequencing cycles of a sequencing run. The per-cycle image data depicts intensity emissions of one or more analytes and their surrounding background captured at a respective sequencing cycle. The technology disclosed processes the alternative representation through an output layer and producing an output and base calls one or more of the analytes at one or more of the sequencing cycles based on the output.

Artificial intelligence-based base calling

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.

Artificial intelligence-based quality scoring

Devices, systems, and methods for non-volatile storage include a well activation device operable to modify one or more wells from a plurality of wells of a flow cell to provide a set of readable wells. Readable wells are configured to allow exposure of a well to substances from nucleotide sequencing fluids, and prevent exposure to other substances and fluids, such as nucleotide synthesizing fluids. The well activation device may also modify wells to provide a set of writeable wells. This set of wells is configured to allow exposure to the nucleotide synthesizing fluids and substances; and prevent exposure to the nucleotide sequencing fluids and substances. There may also be provisions made for risk mitigation for data errors such as generating commands to write specified data to a nucleotide sequence associated with a particular location in a storage device, reading the nucleotide sequence and performing a comparison.

System and method for storage

Presented herein are methods and compositions for tagmentation of nucleic acids. The methods are useful for generating tagged DNA fragments that are qualitatively and quantitatively representative of the target nucleic acids in the sample from which they are generated.

Transposase compositions for reduction of insertion bias

A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.

Deep neural network-based sequencing

The technology disclosed relates to artificial intelligence-based base calling. The technology disclosed relates to accessing a progression of per-cycle analyte channel sets generated for sequencing cycles of a sequencing run, processing, through a neural network-based base caller (NNBC), windows of per-cycle analyte channel sets in the progression for the windows of sequencing cycles of the sequencing run such that the NNBC processes a subject window of per-cycle analyte channel sets in the progression for the subject window of sequencing cycles of the sequencing run and generates provisional base call predictions for three or more sequencing cycles in the subject window of sequencing cycles, from multiple windows in which a particular sequencing cycle appeared at different positions, using the NNBC to generate provisional base call predictions for the particular sequencing cycle, and determining a base call for the particular sequencing cycle based on the plurality of base call predictions.

Artificial intelligence-based many-to-many base calling

The technology disclosed presents a deep learning-based framework, which identifies sequence patterns that cause sequence-specific errors (SSEs). Systems and methods train a variant filter on large-scale variant data to learn causal dependencies between sequence patterns and false variant calls. The variant filter has a hierarchical structure built on deep neural networks such as convolutional neural networks and fully-connected neural networks. Systems and methods implement a simulation that uses the variant filter to test known sequence patterns for their effect on variant filtering. The premise of the simulation is as follows: when a pair of a repeat pattern under test and a called variant is fed to the variant filter as part of a simulated input sequence and the variant filter classifies the called variant as a false variant call, then the repeat pattern is considered to have caused the false variant call and identified as SSE-causing.

Deep learning-based framework for identifying sequence patterns that cause sequence-specific errors (SSEs)

The technology disclosed relates to artificial intelligence-based determination of analyte data for base calling. In particular, the technology disclosed uses input image data that is derived from a sequence of images. Each image in the sequence of images represents an imaged region and depicts intensity emissions indicative of one or more analytes and a surrounding background of the intensity emissions at a respective one of a plurality of sequencing cycles of a sequencing run. The input image data comprises image patches extracted from each image in the sequence of images. The input image data is processed through a neural network to generate an alternative representation of the input image data. The alternative representation is processed through an output layer to generate an output indicating properties of respective portions of the imaged region.

Artificial intelligence-based determination of analyte data for base calling

The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.

Training data generation for artificial intelligence-based sequencing

Embodiments provided herein relate to methods and compositions for preparing an immobilized library of barcoded DNA fragments of a target nucleic acid, identifying genomic variants, determining the contiguity information, phasing information, and methylation status of the target nucleic acid.

Contiguity preserving transposition

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San Diego, CA, United States of America

Amirali Kia