This disclosure relates to methods and systems for processing of sequencing data streams. The system receives sequences from a sequencer and stores them as data records on a database. The sequences are associated with a counter indicative of a number of times the associated sequence has been sequenced. The system progressively receives a further sequence as streaming data from the sequence. While receiving the further sequence, the system matches the streaming data against the stored sequences to determine a matching score. Upon the matching score exceeding a matching threshold for one of the multiple sequences in the database, the system selects the one of the sequences in the database based on the matching score and stores the further sequence on non-volatile memory where the counter value associated with the selected sequence is below a saturation threshold. The system also terminates the receiving where the counter value is above the saturation threshold.

The present invention relates to an artificial intelligence-based chromosomal abnormality detection method, and more specifically, to an artificial intelligence-based chromosomal abnormality detection method using a method that involves: extracting nucleic acids from a biological sample to generate vectorized data on the basis of DNA fragments arranged by acquiring sequence information; and then comparing a reference value and a value calculated by inputting the vectorized data into a trained artificial intelligence model. Rather than using each of values related to reads as an individual normalized value as in existing schemes, which use a step for determining the amount of a chromosome on the basis of a read count, or existing detection methods using the distance concept between arranged reads, the artificial intelligence-based chromosomal abnormality detection method according to the present invention generates vectorized data and analyzes the data using an AI algorithm, and thus is useful in that a similar effect can be exhibited even when read coverage is low.

The present invention relates to an artificial intelligence-based chromosomal abnormality detection method, and more specifically, to an artificial intelligence-based chromosomal abnormality detection method using a method that involves: extracting nucleic acids from a biological sample to generate vectorized data on the basis of DNA fragments arranged by acquiring sequence information; and then comparing a reference value and a value calculated by inputting the vectorized data into a trained artificial intelligence model. Rather than using each of values related to reads as an individual normalized value as in existing schemes, which use a step for determining the amount of a chromosome on the basis of a read count, or existing detection methods using the distance concept between arranged reads, the artificial intelligence-based chromosomal abnormality detection method according to the present invention generates vectorized data and analyzes the data using an AI algorithm, and thus is useful in that a similar effect can be exhibited even when read coverage is low.

A method for identifying a base in nucleic acid, a computer-readable storage medium, a computer program product, and a system. The method for identifying a base in nucleic acid comprises: mapping a coordinate of each bright spot in a bright spot set corresponding to a template onto an image to be inspected, and determining the position of a corresponding coordinate on said image (S11); determining the intensity of a signal at the position of the corresponding coordinate on said image, the intensity being a corrected intensity (S21); and comparing the intensity of the signal at the position of the corresponding coordinate on said image with the size of a first preset value, and determining a base type corresponding to the position on the basis of the comparison result, so as to achieve base calling (S31). The method may quickly and accurately identify a base, and achieve the determination of an order of nucleotides/bases of at least part of a sequence of a template.

A method for identifying a base in nucleic acid, a computer-readable storage medium, a computer program product, and a system. The method for identifying a base in nucleic acid comprises: mapping a coordinate of each bright spot in a bright spot set corresponding to a template onto an image to be inspected, and determining the position of a corresponding coordinate on said image (S11); determining the intensity of a signal at the position of the corresponding coordinate on said image, the intensity being a corrected intensity (S21); and comparing the intensity of the signal at the position of the corresponding coordinate on said image with the size of a first preset value, and determining a base type corresponding to the position on the basis of the comparison result, so as to achieve base calling (S31). The method may quickly and accurately identify a base, and achieve the determination of an order of nucleotides/bases of at least part of a sequence of a template.

A method of progressively training a base caller is disclosed. The method includes initially training a base caller, and generating labelled training data using the initially trained base caller; and (i) further training the base caller with analyte comprising organism base sequences, and generating labelled training data using the further trained base caller. The method includes iteratively further training the base caller by repeating step (i) for N iterations, which includes further training the base caller for N1 iterations of the N iterations with analyte comprising a first organism base sequence, and further training the base caller for N2 iterations of the N iterations with analyte comprising a second organism base sequence. A complexity of neural network configurations loaded in the base caller monotonically increases with the N iterations, and labelled training data generated during an iteration is used to train the base caller during an immediate subsequent iteration.

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.

Provided herein are methods, processes and apparatuses for non-invasive assessment of genetic variations.

Provided herein are methods, processes and apparatuses for non-invasive assessment of genetic variations.

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.