Abstract: Provided herein are methods for determining the microsatellite instability status of samples. In one aspect, the methods include quantifying a number of different repeat lengths present at each of a plurality of microsatellite loci from sequence information to generate a site score for each of the plurality of the microsatellite loci. The methods also include comparing the site score of a given microsatellite locus to a site specific trained threshold for the given microsatellite locus for each of the plurality of the microsatellite loci and calling the given microsatellite locus as being unstable when the site score of the given microsatellite locus exceeds the site specific trained threshold for the given microsatellite locus to generate a microsatellite instability score, which includes a number of unstable microsatellite loci from the plurality of the microsatellite loci.

Abstract: Provided herein are methods for determining the microsatellite instability status of samples. In one aspect, the methods include quantifying a number of different repeat lengths present at each of a plurality of microsatellite loci from sequence information to generate a site score for each of the plurality of the microsatellite loci. The methods also include comparing the site score of a given microsatellite locus to a site specific trained threshold for the given microsatellite locus for each of the plurality of the microsatellite loci and calling the given microsatellite locus as being unstable when the site score of the given microsatellite locus exceeds the site specific trained threshold for the given microsatellite locus to generate a microsatellite instability score, which includes a number of unstable microsatellite loci from the plurality of the microsatellite loci.

Abstract: The disclosure relates to computer technology for precision diagnosis of various states of genetic material such as a gene sequenced from cell-free DNA in a sample. The state may include a somatic homozygous deletion, a somatic heterozygous deletion, a copy number variation, or other states. A computer system may generate competing probabilistic models that each output a probability that the genetic material is in a certain state. Each model may be trained on a training sample set to output a probability that the genetic material is in a respective state. In some embodiments, the computer system may use various probabilistic distributions to generate the models. For example, the computer system may use a beta-binomial distribution, a binomial distribution, a normal (also referred to as “Gaussian”) distribution, or other type of probabilistic modeling techniques.

Abstract: A method for nucleic acid sequencing includes receiving a plurality of observed or measured signals indicative of a parameter observed or measured for a plurality of defined spaces; determining, for at least some of the defined spaces, whether the defined space comprises one or more sample nucleic acids; processing, for at least some of the defined spaces, the observed or measured signal to improve a quality of the observed or measured signal; generating, for at least some of the defined spaces, a set of candidate sequences of bases for the defined space using one or more metrics adapted to associate a score or penalty to the candidate sequences of bases; and selecting the candidate sequence leading to a highest score or a lowest penalty as corresponding to the correct sequence for the one or more sample nucleic acids in the defined space.

Abstract: A method for nucleic acid sequencing includes receiving nucleic acid sequencing data from a sequencing instrument that receives and processes a sample nucleic acid in a sequencing-by-synthesis process. The method also includes generating a set of candidate sequences of bases for the observed or measured nucleic acid sequencing data by determining a predicted signal for candidate sequences using a simulation framework. The simulation framework incorporates an estimated carry forward rate (CFR), an estimated incomplete extension rate (IER), an estimated droop rate (DR), an estimated reactivated molecules rate (RMR), and an estimated termination failure rate (TFR), the RMR being greater than or equal to zero and the TFR being lesser than one. The method also includes identifying, from the set of candidate sequences of bases, a candidate sequence as corresponding to the sequence for the sample nucleic acid.

Abstract: Provided herein are methods for determining the microsatellite instability status of samples. In one aspect, the methods include quantifying a number of different repeat lengths present at each of a plurality of microsatellite loci from sequence information to generate a site score for each of the plurality of the microsatellite loci. The methods also include comparing the site score of a given microsatellite locus to a site specific trained threshold for the given microsatellite locus for each of the plurality of the microsatellite loci and calling the given microsatellite locus as being unstable when the site score of the given microsatellite locus exceeds the site specific trained threshold for the given microsatellite locus to generate a microsatellite instability score, which includes a number of unstable microsatellite loci from the plurality of the microsatellite loci.

Abstract: Provided herein are methods for determining the microsatellite instability status of samples. In one aspect, the methods include quantifying a number of different repeat lengths present at each of a plurality of microsatellite loci from sequence information to generate a site score for each of the plurality of the microsatellite loci. The methods also include comparing the site score of a given microsatellite locus to a site specific trained threshold for the given microsatellite locus for each of the plurality of the microsatellite loci and calling the given microsatellite locus as being unstable when the site score of the given microsatellite locus exceeds the site specific trained threshold for the given microsatellite locus to generate a microsatellite instability score, which includes a number of unstable microsatellite loci from the plurality of the microsatellite loci.

Abstract: Provided herein are methods for determining the microsatellite instability status of samples. In one aspect, the methods include quantifying a number of different repeat lengths present at each of a plurality of microsatellite loci from sequence information to generate a site score for each of the plurality of the microsatellite loci. The methods also include comparing the site score of a given microsatellite locus to a site specific trained threshold for the given microsatellite locus for each of the plurality of the microsatellite loci and calling the given microsatellite locus as being unstable when the site score of the given microsatellite locus exceeds the site specific trained threshold for the given microsatellite locus to generate a microsatellite instability score, which includes a number of unstable microsatellite loci from the plurality of the microsatellite loci.

Abstract: Methods for producing a filtered sequencing data set by identifying one or more split sequence reads in a set of test sequence reads obtained from cell-free nucleic acid (cfNA) in a biological sample, wherein each split sequence read comprises at least one breakpoint; and, suppressing, in the set of test sequence reads, (i) at least a portion of one or more of the split sequence reads and/or at least a portion of one or more of the test sequence reads that comprise at least one sequence variant within a selected number of nucleotides from a given breakpoint, (ii) one or more base calls of the split sequence reads and/or one or more base calls of the test sequence reads that comprise at least one sequence variant within a selected number of nucleotides from a given breakpoint, thereby producing the filtered sequence information data set.

Abstract: The disclosure relates to computer technology for precision diagnosis of various states of genetic material such as a gene sequenced from cell-free DNA in a sample. The state may include a somatic homozygous deletion, a somatic heterozygous deletion, a copy number variation, or other states. A computer system may generate competing probabilistic models that each output a probability that the genetic material is in a certain state. Each model may be trained on a training sample set to output a probability that the genetic material is in a respective state. In some embodiments, the computer system may use various probabilistic distributions to generate the models. For example, the computer system may use a beta-binomial distribution, a binomial distribution, a normal (also referred to as “Gaussian”) distribution, or other type of probabilistic modeling techniques.

Abstract: Methods and systems for quantification of a target nucleic acid in a sample are provided. The method includes forming a plurality of discrete sample portions. Each of the plurality of discrete sample portions comprising a portion of the sample, and a reaction mixture. The method further includes amplifying the plurality of discrete sample portions to form a plurality of discrete processed sample portions. At least one discrete processed sample portion containing nucleic acid amplification reaction products. Fluorescence signals are detected from the at least one of the plurality of discrete processed sample portions to determine a presence of the at least one target nucleic acid. The method also includes determining the respective volumes of the plurality of the plurality of discrete processed sample portions, and estimating the number of copies-per-unit-volume of the at least one target nucleic acid in the sample.

Abstract: Systems and method for determining variants can receive mapped reads and determine a distribution of matched-filter residuals distribution from a plurality of reads at a homopolymer region. The distribution of matched-filter residuals can be fit to uni-modal and bi-modal models. Based on the model that best fits the distribution of matched-filter residuals, the heterozygosity of the sample and the absence or presence of an insertion/deletion in the homopolymer can be determined.

Abstract: A method for nucleic acid sequencing includes: receiving a signal comprising measurements of a parameter measured in response to a plurality of nucleotide flows flowed in a space comprising a sample nucleic acid; normalizing the signal to obtain a normalized signal; adaptively normalizing the normalized signal to obtain an adaptively normalized signal; and predicting a sequence of base calls corresponding to the sample nucleic acid using the adaptively normalized signal.

Abstract: The disclosure relates to computer technology for precision diagnosis of various states of genetic material such as a gene sequenced from cell-free DNA in a sample. The state may include a somatic homozygous deletion, a somatic heterozygous deletion, a copy number variation, or other states. A computer system may generate competing probabilistic models that each output a probability that the genetic material is in a certain state. Each model may be trained on a training sample set to output a probability that the genetic material is in a respective state. In some embodiments, the computer system may use various probabilistic distributions to generate the models. For example, the computer system may use a beta-binomial distribution, a binomial distribution, a normal (also referred to as “Gaussian”) distribution, or other type of probabilistic modeling techniques.

Abstract: A method for designing test or control sequences may include identifying, using a variant caller, loci with systematic errors present in a plurality of sequencing runs included in a training set of sequencing runs obtained using sequencing-by-synthesis; and selecting a representative set of loci, including selecting from the identified loci an approximately equal number of loci involving errors in A, T, C, and G homopolymers and selecting from the identified loci an approximately equal number of loci involving homopolymers having a length of two, three, and four.

Abstract: Methods, systems, and compositions for non-invasively detecting and/or monitoring therapeutic nucleic acid constructs in a sample comprising cell-free nucleic acids from a subject. Detection of therapeutic nucleic acid constructs in samples comprising cell-free nucleic acids allows for verifying therapeutic nucleic acid construct administration, determining the persistence or biological efficacy of the therapeutic nucleic acid construct, and/or ascertaining the efficacy of the therapy in the subject.

Abstract: Sequencing nucleic acids can identify variations associated with presence, susceptibility or prognosis of disease. However, the value of such information can be compromised by errors introduced by or before the sequencing process including preparing nucleic acids for sequencing. Blunting single-stranded overhangs on nucleic acids in a sample can introduce deamination-induced sequencing errors. The disclosure provides methods of identifying and correcting for such deamination-induced sequencing errors and distinguishing them from real sequence variations.

Abstract: Methods, systems, and compositions for non-invasively detecting and/or monitoring therapeutic nucleic acid constructs in a sample comprising cell-free nucleic acids from a subject. Detection of therapeutic nucleic acid constructs in samples comprising cell-free nucleic acids allows for verifying therapeutic nucleic acid construct administration, determining the persistence or biological efficacy of the therapeutic nucleic acid construct, and/or ascertaining the efficacy of the therapy in the subject.

Abstract: The disclosure relates to computer technology for precision diagnosis of various states of genetic material such as a gene sequenced from cell-free DNA in a sample. The state may include a somatic homozygous deletion, a somatic heterozygous deletion, a copy number variation, or other states. A computer system may generate competing probabilistic models that each output a probability that the genetic material is in a certain state. Each model may be trained on a training sample set to output a probability that the genetic material is in a respective state. In some embodiments, the computer system may use various probabilistic distributions to generate the models. For example, the computer system may use a beta-binomial distribution, a binomial distribution, a normal (also referred to as “Gaussian”) distribution, or other type of probabilistic modeling techniques.

Abstract: Methods and systems for improving callings of insertions and/or deletions by identifying genetic sequence reads having identical molecular barcodes and sequences among sequence reads from a nucleic acid sequencer, grouping the genetic reads into a family, and processing families comprising split reads to detect the insertion and/or deletion in a sample of polynucleotide molecules.