Grail, Inc.

Matthew H Larson

Nick Eattock

Xiao Yang

M Cyrus Maher

Darya Filippova

Robert Abe Paine Calef

Monica Portela Dos Santos Pimentel

Hyunsung John Kim

Arash Jamshidi

Anton Valouev

Virgil Nicula

H John Kim

Collin A Melton

Wenying Pan

Collin Melton

Pranav Parmjit Singh

Nicholas Eattock

Eric Michael Scott

Mohini Jangi Desai

Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

Convolutional neural network systems and methods for data classification

Aspects of the invention relate to methods for preparing and analyzing a sequencing library from a mixed cell-free DNA (cfDNA) sample, wherein the mixed sample includes double-stranded DNA (dsDNA), damaged dsDNA (e.g., nicked dsDNA), and single-stranded DNA (ssDNA) molecules. The subject methods facilitate the collection of information from dsDNA, ssDNA and damaged DNA (e.g., nicked DNA) molecules in a sample, thereby providing enhanced diagnostic information as compared to sequencing libraries that are prepared from dsDNA alone.

Methods of preparing and analyzing cell-free nucleic acid sequencing libraries

Systems and methods for determining a cancer class of a subject are provided in which a plurality of sequence reads, in electronic form, are obtained from a biological sample of the subject. The sample comprises a plurality of cell-free DNA molecules including respective DNA molecules longer than a threshold length of less than 160 nucleotides. The plurality of sequence reads excludes sequence reads of cell-free DNA molecules in the plurality of cell-free DNA molecules longer than the threshold length. The plurality of sequence reads is used to identify a relative copy number at each respective genomic location in a plurality of genomic locations in the genome of the subject. The genetic information about the subject obtained from the sample and the genetic information consisting of the identification of the relative copy number at each respective genomic location, is applied to a classifier that determines the cancer class of the subject.

Systems and methods for enriching for cancer-derived fragments using fragment size

Methods for measuring subpopulations of ribonucleic acid (RNA) molecules are provided. In some embodiments, methods of generating a sequencing library from a plurality of RNA molecules in a test sample obtained from a subject are provided, as well as methods for analyzing the sequencing library to detect, e.g., the presence or absence of a disease.

Methods for detecting disease using analysis of RNA

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Alignment free filtering for identifying fusions

In various aspects, the present disclosure provides methods, compositions, reactions mixtures, kits, and systems for sequencing both RNA and DNA from a single source sample. In some embodiments, RNA is treated so as to differentiate RNA sequences from DNA sequences derived from the same sample. In some embodiments, the RNA and DNA are cell-free polynucleotides.

Differential tagging of RNA for preparation of a cell-free DNA/RNA sequencing library

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San Francisco, CA, United States of America

Matthew H Larson