Abstract: 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.

Abstract: The present disclosure relates to an improved method for analyzing sequencing data to detect cross-sample contamination in a test sample. Determining cross-contamination in a test sample can be informative for determining that the test sample will be less likely to correctly identify the presence of cancer in the subject. Pre-determined single nucleotide polymorphisms selected from: an allele present in a select database or a genotyping SNP associated with a sample type are used to identify. A sample is determined to be contaminated using the determined contamination probabilities of the one or more pre-determined SNPs.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method for generating a tumor fraction estimate from a DNA sample of a subject is disclosed. The method may include receiving a dataset of methylation sequence reads from the sample of the subject. The method may also include dividing the dataset into a plurality of variants. The method may further include determining methylation states of the plurality of variants. The method may further include filtering the plurality of variants based on a bank of reference sequence reads to generate a filtered subset of variants. The bank may include reads generated from non-cancer samples and biopsy samples of a plurality of tissues of reference individuals. The counts of the methylation states of variants in the filtered subset are determined and input to a model that is trained based on recurrence rates of the variants in the reference sequence reads. The tumor fraction estimate may be generated by the model.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for processing sequencing data of ribonucleic acid (RNA) molecules from a test sample include obtaining a plurality of sequence reads each derived from a RNA molecule obtained from the test sample, filtering the plurality of sequence reads, identifying one or more candidate variants from the filtered plurality of sequence reads, determining a quality score for each of the identified one or more candidate variants, the quality score indicating a likelihood that the candidate variant is a false positive detection of a mutation in the RNA molecule, and outputting the one or more candidate variants having a quality score greater than a threshold quality score.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system, method and computer program product for analyzing data of high dimensionality (e.g., sequence reads of nucleic acid samples in connection with a disease condition) are provided.