This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine learning model to recalibrate nucleotide-base calls (e.g., variant calls) of a call-generation model. For instance, the disclosed systems can train and utilize a call-recalibration-machine-learning model to generate a set of predicted variant-call classifications based on sequencing metrics associated with a sample nucleotide sequence. Leveraging the set of variant-call classifications, the disclosed systems can further update or modify nucleotide-base calls (e.g., variant calls) corresponding to genomic coordinates. Indeed, the disclosed systems can generate an initial nucleotide-base call based on sequencing metrics for nucleotide reads of a sample sequence utilizing a call-generation model and further utilize a call-recalibration-machine-learning model to generate classification predictions for updating or recalibrating the initial nucleotide-base call from a subset of the same sequencing metrics or other sequencing metrics.

The disclosure provides gene fusions, gene variants, and novel associations with disease states, as well as kits, probes, and methods of using the same.

Systems and methods of storing and retrieving K-mer data in a data structure are provided. In certain embodiments, the K-mer data is stored as an integer value that defines an address of a slot in the data structure. In many embodiments, each slot in the data structure stores the remaining portion of the K-mer that is not part of the prefix. Additional embodiments are directed to genetic or genomic analysis using a data structure for storing K-mer data.

Systems and methods of storing and retrieving K-mer data in a data structure are provided. In certain embodiments, the K-mer data is stored as an integer value that defines an address of a slot in the data structure. In many embodiments, each slot in the data structure stores the remaining portion of the K-mer that is not part of the prefix. Additional embodiments are directed to genetic or genomic analysis using a data structure for storing K-mer data.

The present disclosure provides methods and systems of identifying a fibrotic disease in a subject using a DeepLearning model. The DeepLearning model may be used to predict, treat, monitor, and/or prevent the fibrotic disease in the subject, as well as to characterize a subtype of the fibrotic disease.

The present disclosure provides methods and systems of identifying a fibrotic disease in a subject using a DeepLearning model. The DeepLearning model may be used to predict, treat, monitor, and/or prevent the fibrotic disease in the subject, as well as to characterize a subtype of the fibrotic disease.

The present disclosure provides compositions and methods for the generation of an antibody or immunogenic composition, such as a vaccine, through epitope focusing by variable effective antigen surface concentration. Generally, the composition and methods of the disclosure comprise three steps: a “design process” comprising one or more in silico bioinformatics steps to select and generate a library of potential antigens for use in the immunogenic composition; a “formulation process”, comprising in vitro testing of potential antigens, using various biochemical assays, and further combining two or more antigens to generate one or more immunogenic compositions; and an “administering” step, whereby the immunogenic composition is administered to a host animal, immune cell, subject or patient. Further steps may also be included, such as the isolation and production of antibodies raised by host immune response to the immunogenic composition.

The present disclosure provides compositions and methods for the generation of an antibody or immunogenic composition, such as a vaccine, through epitope focusing by variable effective antigen surface concentration. Generally, the composition and methods of the disclosure comprise three steps: a “design process” comprising one or more in silico bioinformatics steps to select and generate a library of potential antigens for use in the immunogenic composition; a “formulation process”, comprising in vitro testing of potential antigens, using various biochemical assays, and further combining two or more antigens to generate one or more immunogenic compositions; and an “administering” step, whereby the immunogenic composition is administered to a host animal, immune cell, subject or patient. Further steps may also be included, such as the isolation and production of antibodies raised by host immune response to the immunogenic composition.

The invention provides oncogenomic methods for detecting tumors by identifying circulating tumor DNA. A patient-specific reference directed acyclic graph (DAG) represents known human genomic sequences and non-tumor DNA from the patient as well as known tumor-associated mutations. Sequence reads from cell-free plasma DNA from the patient are mapped to the patient-specific genomic reference graph. Any of the known tumor-associated mutations found in the reads and any de novo mutations found in the reads are reported as the patient's tumor mutation burden.

The invention provides oncogenomic methods for detecting tumors by identifying circulating tumor DNA. A patient-specific reference directed acyclic graph (DAG) represents known human genomic sequences and non-tumor DNA from the patient as well as known tumor-associated mutations. Sequence reads from cell-free plasma DNA from the patient are mapped to the patient-specific genomic reference graph. Any of the known tumor-associated mutations found in the reads and any de novo mutations found in the reads are reported as the patient's tumor mutation burden.