Natera, Inc.

Grail, Inc.

Onur Sakarya

Matthew Rabinowitz

Bernhard Zimmermann

George Gemelos

Styrmir Sigurjonsson

Huseyin Eser Kirkizlar

Joshua Babiarz

Lane A Eubank

Tudor Pompiliu Constantin

Zachary Demko

Joerg Bredno

Matthew Micah Hill

Milena Banjevic

Allison Ryan

Johan Baner

Arash Jamshidi

Samuel S Gross

Oliver Claude Venn

M Cyrus Maher

Catalin Barbacioru

Seyedmehdi Shojaee

Alexander P Fields

Qinwen Liu

Bernhard Zimmerman

Tudor Popiliu Constantin

Christopher-James A V Yakym

John Lamping

Jan Schellenberger

John F Beausang

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

Detecting mutations and ploidy in chromosomal segments

Detecting cross-contamination between test samples used for determining cancer in a subject is beneficial. To detect cross-contamination, test sequences including at least one single nucleotide polymorphism are prepared using genome sequencing techniques. Some of the test sequences can be filtered to improve accuracy and precision. A prior contamination probability for each test sequence is determined based on a minor allele frequency. A contamination model including a likelihood test is applied to a test sequence. The likelihood test obtains a current contamination probability representing the likelihood that the test sample is contaminated. The contamination model can also determine a likelihood that the sample includes loss of heterozygosity representing the likelihood that the test sequence is contaminated. Test samples that are contaminated are removed. A source for the contaminated test sample can be found by comparing contaminated test sequences to other test sequences.

Detecting cross-contamination in sequencing data

A method for training a convolutional neural net for contamination analysis is provided. A training dataset is obtained comprising, for each respective training subject in a plurality of subjects, a variant allele frequency of each respective single nucleotide variant in a respective plurality of single nucleotide variants, and a respective contamination indication. First and second subsets of the plurality of training subjects have first and second contamination indication values, respectively. A corresponding first channel comprising a first plurality of parameters that include a respective parameter for a single nucleotide variant allele frequency of each respective single nucleotide variant in a set of single nucleotide variants in a reference genome is constructed for each respective training subject. An untrained or partially trained convolutional neural net is trained using, for each respective training subject, at least the corresponding first channel of the respective training subject as input against the respective contamination indication.

Systems and methods for using a convolutional neural network to detect contamination

The invention provides methods for simultaneously amplifying multiple nucleic acid regions of interest in one reaction volume as well as methods for selecting a library of primers for use in such amplification methods. The invention also provides library of primers with desirable characteristics, such as minimal formation of amplified primer dimers or other non-target amplicons.

Methods for simultaneous amplification of target loci

Cross-contamination of a test sample used to determine cancer is identified using gene sequencing data. Each test sample includes a number of test sequences that may include a single nucleotide polymorphism (SNP) that can be indicative of cancer. The test sequences are be filtered to remove or negate at least some of the SNPs from the test sequences. Negating the test sequences allows more test sequences to be simultaneously analyzed to determine cross-contamination. Cross-contamination is determined by modeling the variant allele frequency for the test sequences as a function of minor allele frequency, contamination level, and background noise. In some cases, the variant allele frequency is based on a probability function including the minor allele frequency. Cross-contamination of the test sample is determined if the determined contamination level is above a threshold and statistically significant.

Detecting cross-contamination in sequencing data using regression techniques

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Redwood City, CA, United States of America

Onur Sakarya