Illumina Corporation

Illumina Cambridge Limited

Affymetrix, Inc.

Hong Gao

Kai-How Farh

Laksshman Sundaram

Jeremy Francis McRae

Milan Karangutkar

Tobias Hamp

Jorg Hakenberg

Inderjit Singh

Donavan Cheng

Pam Cheng

Daniel Roche

James Casey Geaney

Eugene Bolotin

John Shon

Samskruthi Reddy Padigepati

Teresa A Webster

Mei-Mei Shen

Sam Ng

Wenwu Cui

Ali Pirani

Joshua Goodwin Jon Mcmaster-schraiber

Jeremy Francis Mcrae

Hendrikus Jasper Gierman

The technology disclosed relates to variant calling of sequenced reads of a sample of a target species against a reference genome of a pseudo-target species. Low-quality variants are identified as false positive variants that are present in the second set of variants but absent from the first set of variants.

Quality detection of variant calling using a machine learning classifier

A system includes at least a voxelizer, an alternative allele encoder, an evolutionary conservation encoder, and a convolutional neural network. The voxelizer accesses a three-dimensional structure of a reference amino acid sequence of a protein and fits a three-dimensional grid of voxels on atoms in the three-dimensional structure on an amino acid-basis to generate amino acid-wise distance channels. The alternative allele encoder encodes an alternative allele sequence to each voxel in the three-dimensional grid of voxels. The evolutionary conservation encoder encodes an evolutionary conservation sequence to each voxel in the three-dimensional grid of voxels. The convolutional neural network applies three-dimensional convolutions to a tensor that includes the amino acid-wise distance channels encoded with the alternative allele sequence and respective evolutionary conservation sequences and determines a pathogenicity of a variant nucleotide based at least in part on the tensor.

Multi-channel protein voxelization to predict variant pathogenicity using deep convolutional neural networks

The technology disclosed relates to determining pathogenicity of variants. In particular, the technology disclosed relates to generating amino acid-wise distance channels for a plurality of amino acids in a protein. Each of the amino acid-wise distance channels has voxel-wise distance values for voxels in a plurality of voxels. A tensor includes the amino acid-wise distance channels and at least an alternative allele of the protein expressed by a variant. A deep convolutional neural network determines a pathogenicity of the variant based at least in part on processing the tensor. The technology disclosed further augments the tensor with supplemental information like a reference allele of the protein, evolutionary conservation data about the protein, annotation data about the protein, and structure confidence data about the protein.

Artificial intelligence-based analysis of protein three-dimensional (3D) structures

Systems, computer-implemented methods, and non-transitory computer readable media are provided for sharing medical data. The disclosed systems may be configured to create a first workgroup having a first knowledgebase. This first knowledgebase may be federated with a common knowledgebase, and with a second knowledgebase of a second workgroup. At least one of the first knowledgebase, common knowledgebase, and second knowledgebase may be configured to store data items comprising associations, signs, and evidence. The signs may comprise measurements and contexts, and the associations may describe the relationships between the measurements and contexts. The evidence may support these associations. The disclosed systems may be configured to receive a request from a user in the first workgroup, retrieve matching data items, and optionally then output to the user at least some of the retrieved matching data items. The request may comprise at least one of a first association and a first measurement.

Federated systems and methods for medical data sharing

The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.

Semi-supervised learning for training an ensemble of deep convolutional neural networks

The technology disclosed relates to determining pathogenicity of nucleotide variants. In particular, the technology disclosed relates to specifying a particular amino acid at a particular position in a protein as a gap amino acid, and specifying remaining amino acids at remaining positions in the protein as non-gap amino acids. The technology disclosed further relates to generating a gapped spatial representation of the protein that includes spatial configurations of the non-gap amino acids, and excludes a spatial configuration of the gap amino acid, and determining a pathogenicity of a nucleotide variant based at least in part on the gapped spatial representation, and a representation of an alternate amino acid created by the nucleotide variant at the particular position.

Protein structure-based protein language models

Deep convolutional neural networks to predict variant pathogenicity using three-dimensional (3D) protein structures

Recurrent neural network-based variant pathogenicity classifier

Deep convolutional neural networks for variant classification

Methods for processing data using information gained from examining biological materials identifies and characterized probes for Single Nucleotide Polymorphisms and identifies Off Target Variants.

Systems and methods for SNP characterization and identifying off target variants

The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neutral network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.

Deep learning-based techniques for training deep convolutional neural networks

The technology disclosed includes systems and methods to reduce overfitting of neural network-implemented models that process sequences of amino acids and accompanying position frequency matrices. The system generates supplemental training example sequence pairs, labelled benign, that include a start location, through a target amino acid location, to an end location. A supplemental sequence pair supplements a pathogenic or benign missense training example sequence pair. It has identical amino acids in a reference and an alternate sequence of amino acids. The system includes logic to input with each supplemental sequence pair a supplemental training position frequency matrix (PFM) that is identical to the PFM of the benign or pathogenic missense at the matching start and end location. The system includes logic to attenuate the training influence of the training PFMs during training the neural network-implemented model by including supplemental training example PFMs in the training data.

Deep learning-based techniques for pre-training deep convolutional neural networks

Systems, computer-implemented methods, and non-transitory computer readable media are provided for determining related ontological data. The disclosed systems may be configured to receive a first ontology and a second ontology, the first ontology and the second ontology comprising hierarchically organized ontological data. The disclosed systems may also be configured to receive an indication that a first ontological datum in the first ontology is equivalent to a second ontological datum in the second ontology, and a query for ontological data related to a third ontological datum subordinate to the first ontological datum. The disclosed systems may be configured to determine a first semantic distance between the third ontological datum and a fourth ontological datum in the second ontology satisfies a semantic distance criterion, and output the fourth ontological datum based on the determination that the first semantic distance satisfies the semantic distance criterion.

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Palo Alto, CA, United States of America

Hong Gao