Abstract: A system, method and apparatus for executing a sequence analysis pipeline on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits are arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the sequence analysis pipeline on the reads of genomic data. Each subset of the hardwired digital logic circuits is formed in a wired configuration to perform the one or more steps in the sequence analysis pipeline.

Abstract: A system, method and apparatus for executing a sequence analysis pipeline on genetic sequence data includes a integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits are arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the sequence analysis pipeline on the reads of genomic data. Each subset of the hardwired digital logic circuits is formed in a wired configuration to perform the one or more steps in the sequence analysis pipeline.

Abstract: A system, method and apparatus include one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for hardware-accelerated execution of a genomic data processing pipeline based on one or more user-selectable options presented via a graphical user interface. The operations include obtaining first data representing a selection of one or more of a plurality of user-selectable options submitted via the graphical user interface. One or more of the plurality of user-selectable options identify a particular genomic data processing pipeline. The operations further include configuring, using the application programming interface executed by the one or more computers, an integrated circuit to perform one or more hardware accelerated steps of a primary, secondary, and/or tertiary processing protocol of the particular genomic data processing pipeline.

Abstract: Methods, systems, and apparatus for hardware-accelerated generation of a K-mer graph using a programmable logic device. In one aspect, a method includes actions of obtaining a first set of nucleic acid sequences, generating a K-mer graph using the obtained first set of nucleic acid sequences and using a plurality of non-pipelined hardware logic units of a programmable logic device, and periodically updating, with a control machine, graph description data for the K-mer graph after performance of the one or more operations by each hardware logic unit.

Abstract: This disclosure describes methods, non-transitory-computer readable media, and systems that can accurately genotype one or more human leukocyte antigen (HLA) alleles from a genomic sample by using alignment-score-based filtering and read-support-equivalence grouping of reads for genotype inference. To genotype HLA alleles, the disclosed systems extract a genomic sample's reads corresponding to an HLA genomic region and align the extracted reads with HLA-allele-reference sequences. The disclosed systems further select a subset of read alignments for the extracted reads based on alignment scores for alignments between the extracted reads and the HLA-allele-reference sequences. Based on the selected subset of read alignments, the disclosed systems group individual reads into HLA equivalence classes and determine candidate HLA alleles for the genome sample at one or more HLA loci.

Abstract: Methods, systems, and apparatuses, including computer programs for identifying a gene fusion in a biological sample are disclosed. The method can include actions of obtaining first data that represents a plurality of aligned reads, identifying a plurality of fusion candidates included within the obtained first data, filtering the plurality of fusion candidates to determine a filtered set of fusion candidates, for each particular fusion candidate of the filtered set of fusion candidates: generating, by one or more computers, input data for input to a machine learning model that includes extracted feature data that to represents the particular fusion candidate, providing the generated input data as an input to the machine learning model that has been trained to generate output data representing a likelihood that a fusion candidate is a valid gene fusion, and determining whether the particular fusion candidate corresponds to a valid gene fusion based on the output data.

Abstract: Methods, systems, and apparatuses, including computer programs for identifying a gene fusion in a biological sample are disclosed. The method can include actions of obtaining first data that represents a plurality of aligned reads, identifying a plurality of fusion candidates included within the obtained first data, filtering the plurality of fusion candidates to determine a filtered set of fusion candidates, for each particular fusion candidate of the filtered set of fusion candidates: generating, by one or more computers, input data for input to a machine learning model that includes extracted feature data that to represents the particular fusion candidate, providing the generated input data as an input to the machine learning model that has been trained to generate output data representing a likelihood that a fusion candidate is a valid gene fusion, and determining whether the particular fusion candidate corresponds to a valid gone fusion based on the output data.

Abstract: A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline.

Abstract: A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline.

Abstract: A system, method and apparatus for executing a sequence analysis pipeline on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits are arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the sequence analysis pipeline on the reads of genomic data. Each subset of the hardwired digital logic circuits is formed in a wired configuration to perform the one or more steps in the sequence analysis pipeline.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that implement improved mapping and alignment of nucleotide reads with genomic regions of a reference genome. For instance, the disclosed systems can identify, for one or more candidate alignments between nucleotide reads from a genomic sample with a primary contiguous sequence at respective genomic regions of a reference genome, allele-variant differences between the primary contiguous sequence and population haplotypes within the respective genomic regions to generate alignment score adjustments for each population haplotype.

Abstract: This disclosure describes embodiments of methods, non-transitory computer readable media, and systems that can utilize one or more machine learning models to predict insert lengths of a sample genomic sequence from which nucleotide read pairs are sequenced. For example, the disclosed systems can generate predictions for insert lengths based on cluster metrics from primary analysis on a sequencing device, such as signal intensity. By applying a machine-learning-based insert length prediction model to process the cluster metrics, the disclosed systems generate a predicted insert length (e.g., a distribution or a mean). To determine cluster metrics, the disclosed systems can analyze data from oligonucleotide clusters and/or from a sample genomic sequence used to sequence nucleotide read pairs during primary analysis.

Abstract: This disclosure describes methods, non-transitory computer-readable media, and systems that can (i) identify reads that align with at least some portion of alternative contiguous sequences representing structural variant haplotypes within a structural variant reference genome and (ii) generate a structural-variant-alignment tag within an alignment file for such read alignments to guide identifying candidate structural-variant locations. In addition to employing structural-variant-alignment tags, the disclosed systems identify read fragments that align or overlap with portions of alternate contiguous sequences representing an insertion (or other structural variant) and further masks such insertion-overlapping read fragments as part of an alignment file.

Abstract: A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit that may be connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits may be arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the bioinformatics analysis on the reads of genomic data. Each subset of the hardwired digital logic circuits may be formed in a wired configuration to perform the one or more steps in the bioinformatics analysis.

Abstract: A system, method and apparatus for executing an HMM analysis on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit that may be connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits may be arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the HMM analysis on the reads of genomic data. Each subset of the hardwired digital logic circuits may be formed in a wired configuration to perform the one or more steps in the HMM analysis.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can generate genotype calls from a combined pipeline for processing nucleotide reads from multiple read types/sources for robust, accurate genotype calls. For example, the disclosed systems can train and/or utilize a genotype-call-integration machine-learning model to generate predictions for genotype calls based on data associated with a first type of nucleotide reads (e.g., short reads) and a second type of nucleotide reads (e.g., long reads). As disclosed, the disclosed systems can determine sequencing metrics and can utilize a genotype-call-integration machine-learning model to generate predictions (e.g., genotype probabilities, variant call classifications) for generating output genotype calls based on the sequencing metrics.

Abstract: Methods, systems, and apparatuses, including computer programs for generating and using a hash table configured to improve mapping of reads are disclosed that include obtaining a first seed of K nucleotides from a reference sequence, generating a seed extension tree having a nodes, wherein each node of the nodes corresponds to (i) an extended seed that is an extension of the first seed and has a nucleotide length of K* and (ii) one or more locations, in a seed extension table, that include data describing reference sequence locations that match the extended seed, and for each node: storing interval information at a location of the hash table that corresponds to an index key for the extended seed, wherein the interval information references one or more locations in the seed extension table that include reference sequence locations that match the extended seed associated with the node.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for efficiently identifying and selecting split groups corresponding to one or more nucleotide reads. Generally, split groups comprise chains of fragments forming split-alignments of one read. The disclosed system utilizes dynamic programming to generate and evaluate candidate split groups. The disclosed system can generate split group scores for each of the candidate split groups. To generate the split group scores, the disclosed system considers fragment alignment scores and geometries of fragment alignments within the candidate split groups. The disclosed systems select a predicted split group from the candidate split groups based on the split group scores.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can determine allele likelihoods of a genomic region exhibiting certain haplotype alleles using one or both of consolidated computations and data exchanges across specialized hardware. For instance, the disclosed systems can determine an intermediate allele likelihood of a genomic region comprising a haplotype allele by running a single-pass-concurrent-multiplication operation. In some cases, the disclosed systems determine and store subsets of intermediate allele likelihoods corresponding to marker-variant groups and extemporaneously generate sets of intermediate allele likelihoods for a set of marker variants by using the intermediate-allele-likelihood subsets as hot-start points.

Abstract: A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit that may be connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits may be arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the bioinformatics analysis on the reads of genomic data. Each subset of the hardwired digital logic circuits may be formed in a wired configuration to perform the one or more steps in the bioinformatics analysis.