Abstract: 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 gaped 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 gaped spatial representation, and a representation of an alternate amino acid coated by the nucleotide variant at the particular position.

Abstract: Described herein are technologies for classifying a protein structure (such as technologies for classifying the pathogenicity of a protein structure related to a nucleotide variant). Such a classification is based on two-dimensional images taken from a three-dimensional image of the protein structure. With respect to some implementations, described herein are multi-view convolutional neural networks (CNNs) for classifying a protein structure based on inputs of two-dimensional images taken from a three-dimensional image of the protein structure. In some implementations, a computer-implemented method of determining pathogenicity of variants includes accessing a structural rendition of amino acids, capturing images of those parts of the structural rendition that contain a target amino acid from the amino acids, and, based on the images, determining pathogenicity of a nucleotide variant that mutates the target amino acid into an alternate amino acid.

Abstract: The technology disclosed relates to inter-model prediction score recalibration. In one implementation, the technology disclosed relates to a system including a first model that generates, based on evolutionary conservation summary statistics of amino acids in a target protein sequence, a first pathogenicity score-to-rank mapping for a set of variants in the target protein sequence; and a second model that generates, based on epistasis expressed by amino acid patterns spanning the target protein sequence and a plurality of non-target protein sequences aligned in multiple sequence alignment, a second pathogenicity score-to-rank mapping for the set of variants. The system also includes a reassignment logic that reassigns pathogenicity scores from the first set of pathogenicity scores to the set of variants based on the first and second score-to-rank mappings, and an output logic to generate a ranking of the set of variants based on the reassigned scores.

Abstract: A first reference genome is segmented into a plurality of bins and high-quality sequenced reads are mapped on a bin-by-bin basis to the plurality of bins in the first reference genome, and a second reference genome is segmented into a plurality of bins and high-quality sequenced reads are mapped on a bin-by-bin basis to the plurality of bins in the second reference genome. A best-mapped bin is identified in the second reference genome based on the greatest degree of match between the best-mapped bin in the second reference genome and a corresponding bin in the first reference genome.

Abstract: The technology disclosed relates to generating species-differentiable evolutionary profiles using a weighting logic. In particular, the technology disclosed relates to determining a weighted summary statistic for a given residue category at a given position in a multiple sequence alignment based on one or more weights of one or more sequences in the multiple sequence alignment that have a residue of the given residue category at the given position.

Abstract: The technology disclosed relates to accessing a multiple sequence alignment that aligns a query residue sequence to a plurality of non-query residue sequences, applying a set of periodically-spaced masks to a first set of residues at a first set of positions in the multiple sequence alignment, and cropping a portion of the multiple sequence alignment that includes the set of periodically-spaced masks at the first set of positions, and a second set of residues at a second set of positions in the multiple sequence alignment to which the set of periodically-spaced masks is not applied. The first set of residues includes a residue-of-interest at a position-of-interest in the query residue sequence.

Abstract: The technology disclosed relates to determining feasibility of using a reference genome of a non-target species for variant calling a sample of a target species. In particular, the technology disclosed relates to mapping sequenced reads of a sample of a target species to a reference genome of a non-target species to detect a first set of variants in the sequenced reads of the sample of the target species, and mapping the sequenced reads of the sample of the target species to a reference genome of a pseudo-target species to detect a second set of variants in the sequenced reads of the sample of the target species.

Abstract: 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.

Abstract: The technology disclosed relates to a system for inter-model prediction score recalibration. The system includes a first model that generates, based on evolutionary conservation summary statistics of amino acids in a reference protein sequence, a first set of pathogenicity scores with rankings for variants that mutate the reference sequence to alternate protein sequences. The system further includes a second model that generates, based on epistasis expressed by amino acid patterns spanning a multiple sequence alignment aligning the reference sequence to non-target sequences, a second set of pathogenicity scores with rankings for the variants. The system further includes a rank loss determination logic that determines a rank loss parameter by comparing the two sets of rankings, a loss function reconfiguration logic that reconfigures a loss function based on the rank loss parameter, and a training logic that uses the reconfigured loss function to train the first model.

Abstract: A system comprises chunking logic that chunks (or splits) a multiple sequence alignment (MSA) into chunks, first attention logic that attends to a representation of the chunks and produces a first attention output, first aggregation logic that produces a first aggregated output that contains those features in the first attention output that correspond to masked residues in the plurality of masked residues, mask revelation logic that produces an informed output based on the first aggregated output and a Boolean mask, second attention logic that attends to the informed output and produces a second attention output based on masked residues revealed by the Boolean mask, second aggregation logic that produces a second aggregated output that contains those features in the second attention output that correspond to masked residues concealed by the Boolean mask, and output logic that produces identifications of the masked residues based on the second aggregated output.

Abstract: The technology disclosed relates to training a pathogenicity predictor.

Abstract: 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, 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, determining an evolutionary conservation at the particular position of respective amino acids of respective amino acid classes based at least in part on the gapped spatial representation, and based at least in part on the evolutionary conservation of the respective amino acids, determining a pathogenicity of respective nucleotide variants that respectively substitute the particular amino acid with the respective amino acids in alternate representations of the protein.

Abstract: The technology disclosed describes determination of which elements of a sequence are nearest to uniformly spaced cells in a grid, where the elements have element coordinates, and the cells have dimension-wise cell indices and cell coordinates. The determination includes generating an element-to-cells mapping that maps, to each of the elements, a subset of the cells. The subset of the cells mapped to a particular element in the sequence includes a nearest cell in the grid and one or more neighborhood cells in the grid, and the nearest cell is selected based on matching element coordinates of the particular element to the cell coordinates. The determination further includes generating a cell-to-elements mapping that maps, to each of the cells, a subset of the elements, and using the cell-to-elements mapping to determine, for each of the cells, a nearest element in the sequence.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The technology disclosed relates to efficiently determining which atoms in a protein are nearest to voxels in a grid. The atoms have three-dimensional (3D) atom coordinates, and the voxels have 3D voxel coordinates. The technology disclosed generates an atom-to-voxels mapping that maps, to each of the atoms, a containing voxel selected based on matching 3D atom coordinates of a particular atom of the protein to the 3D voxel coordinates in the grid. The technology disclosed generates a voxel-to-atoms mapping that maps, to each of the voxels, a subset of the atoms. The subset of the atoms mapped to a particular voxel in the grid includes those atoms in the protein that are mapped to the particular voxel by the atom-to-voxels mapping. The technology disclosed includes using the voxel-to-atoms mapping to determine, for each of the voxels, a nearest atom in the protein.