Guardant Health, Inc.

Marcin Sikora

Helmy A Eltoukhy

Darya Chudova

Catalin Barbacioru

Andrew Kennedy

Stefanie Ann Ward Mortimer

Diana Abdueva

Ariel Jaimovich

Aliaksandr Artsiomenka

Martina I Lefterova

Stephen Fairclough

Helmy Eltoukhy

Carlo Artieri

Sequencing methods for sequencing populations of nucleic acid molecules in which sequencing reads of amplicons are grouped into families according to the nucleic acid molecule of origin by partitioning, sample indexes and information from the sequencing reads, such as start and end points. The methods described herein provide many advantages over other sequencing analysis methods, including the identification of sequencing reads deriving from the same nucleic acid in the original sample while minimizing the number of aliquots that are processed.

Sequencing methods with partitioning

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.

Microsatellite instability detection in cell-free DNA

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.

Methods for detecting and suppressing alignment errors caused by fusion events

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.

Computational modeling of loss of function based on allelic frequency

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.

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Redwood City, CA, United States of America

Marcin Sikora