Abstract: Computer methods and systems for quantifying a nucleic acid sample comprising nucleic acid of one or more contributors to: receive nucleic acid sequence reads obtained from the nucleic acid sample and mapped to alleles at polymorphism loci; determine, using the nucleic acid sequence reads, allele counts for each of the alleles at the polymorphism loci; use a probabilistic mixture model that applies a probabilistic mixture model to the allele counts, and that uses probability distributions to model the allele counts at the polymorphism loci; quantify, using the probabilistic mixture model, one or more fractions of nucleic acid of the one or more contributors in the nucleic acid sample; determine a probability that a specific contributor among the one or more contributors has a specific genotype; and call, based on the posterior probability, that the nucleic acid sample includes nucleic acid from the specific contributor.

Abstract: A nucleic acid sequencing technique is described. Sequence data, e.g., generated by a sequencing device, may be analyzed to scan k-mers of a fixed size n in individual reads in the sequence data. Exact matches of the k-mers in the sequence data with reference k-mers are identified. K-mer matching may be used to identify alternative alleles in sequence data with anomalous distribution associated with contamination or other quality issues and to determine a quality metric in real-time.

Abstract: Disclosed herein are systems and methods for multiplex primer design and selection. In one example, a system includes non-transitory memory configured to store executable instructions; and a hardware processor programmed by the executable instructions to receive a plurality of target gene sequences and determine a set of primers for each target gene sequence based on a penalty score associated with the set of primers, wherein the penalty score is based on a non-linear combination of a primer-level penalty score and a set-level penalty score.

Abstract: Computer methods and systems for quantifying a nucleic acid sample comprising nucleic acid of one or more contributors to: receive nucleic acid sequence reads obtained from the nucleic acid sample and mapped to alleles at polymorphism loci; determine, using the nucleic acid sequence reads, allele counts for each of the alleles at the polymorphism loci; use a probabilistic mixture model that applies a probabilistic mixture model to the allele counts, and that uses probability distributions to model the allele counts at the polymorphism loci; quantify, using the probabilistic mixture model, one or more fractions of nucleic acid of the one or more contributors in the nucleic acid sample; determine a probability that a specific contributor among the one or more contributors has a specific genotype; and call, based on the posterior probability, that the nucleic acid sample includes nucleic acid from the specific contributor.

Abstract: A nucleic acid sequencing technique is described. Sequence data, e.g., generated by a sequencing device, may be analyzed to scan k-mers of a fixed size n in individual reads in the sequence data. Exact matches of the k-mers in the sequence data with reference k-mers are identified. The number of exact matches, their distribution in a reference genome, and/or a number of sequence reads in the sequence data that map to different target regions can be used to determine a characteristic of a sample. In one example, the characteristic is a presence of a pathogen in the sample.

Abstract: Methods, systems, and apparatuses, including computer programs for identifying a gene fusion in a biological sample is 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: Disclosed herein are systems and methods for multiplex primer design and selection. In one example, a system includes non-transitory memory configured to store executable instructions; and a hardware processor programmed by the executable instructions to receive a plurality of target gene sequences and determine a set of primers for each target gene sequence based on a penalty score associated with the set of primers, wherein the penalty score is based on a non-linear combination of a primer-level penalty score and a set-level penalty score.