Abstract: Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

Abstract: Aspects of the invention include methods for preparing sequencing libraries, performing sequencing procedures that can correct for process-related errors, and identifying rare variants that are or may be indicative of cancer.

Abstract: Provided herein are methods of enriching mutated cell free nucleic acids for detection and diagnosis of cancer. Also provided are methods using a CRISPR-Cas system to target and deplete unwanted more abundant cell free nucleic acid sequences thereby enriching for less abundant sequences.

Abstract: Provided herein are methods for enriching a biological sample for a target nucleic acid, and analyzing the nucleic acid. In some cases, a biological sample is enriched for target nucleic acids associated with a cancer or tumor. In some cases, a biological sample is enriched for target nucleic acids, and the target nucleic acids vary in length. In some cases, one or more probes are used to enrich the biological sample for the target nucleic acid. In some cases, one or more probes hybridize to one or more ends of a target nucleic acid.

Abstract: Systems and methods for identifying variant alleles as somatic or germline are provided. Reference and variant alleles for a genomic position are identified. Methylation states and sequences of nucleic acid fragment sequences that map to the genomic position are obtained from a sample of a subject. Using the sequences of nucleic acid fragment sequences, each nucleic acid fragment sequence that has the reference allele is assigned to a reference subset, and each nucleic acid fragment sequence that has the variant allele is assigned to a variant subset. One or more indications of the methylation states across the nucleic acid fragment sequences in the variant subset and an indication of the number of nucleic acid fragment sequences in the reference subset versus the variant subset are applied to a trained binary classifier. An identification of the variant allele at the genomic position as somatic or germline is obtained from the classifier.

Abstract: Aspects of the invention include methods for preparing sequencing libraries, performing sequencing procedures that can correct for process-related errors, and identifying rare variants that are or may be indicative of cancer.

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: Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

Abstract: Described herein are methods of preparing dual-indexed nucleic acid libraries for methylation profiling using bisulfite conversion sequencing. In various embodiments, the methods use a two-step indexing process to tag bisulfite-treated DNA with unique molecular identifiers (UMIs).

Abstract: 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.

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: A method for extracting information from a dataset, e.g., a document, includes: receiving the dataset at an information handling device, optionally, extracting, via optical character recognition implemented by a processor of the information handling device, textual information associated with the dataset, and classifying the dataset into one of a plurality of classes. Classifying the dataset may include computing a similarity score for each of the plurality of classes for each of a plurality of window regions of the dataset, calculating a subset of highest similarity scores for each of the plurality of classes for each of the plurality of window regions, determining overall similarity scores for each of the plurality of classes, and classifying the dataset as corresponding to a class with a highest overall similarity score.

Abstract: Provided herein are methods and systems for identifying chimeric nucleic acid fragments, e.g., organism-pathogen chimeric nucleic acid fragments and chromosomal rearrangement chimeric nucleic acid fragments. Also provided herein are methods and systems relating to determining a pathogen integration profile or a chromosomal rearrangement in a biological sample and determining a classification of pathology based at least in part on a pathogen integration profile or a chromosomal rearrangement in a biological sample. In certain aspects of the present disclosure, cell-free nucleic acid molecules from a biological sample are analyzed.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Abstract: 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.

Abstract: 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.