Described herein are methods of determining segregation dynamics of mitochondrial DNA herein. Also described herein are methods of diagnosing, prognosing, and/or monitoring a mitochondrial disease.

Combined ultrasensitive sequencing of matched white blood cells and cell free DNA (cfDNA) identified bona fide tumor-specific alterations that predict clinical outcome after preoperative treatment and resection.

Combined ultrasensitive sequencing of matched white blood cells and cell free DNA (cfDNA) identified bona fide tumor-specific alterations that predict clinical outcome after preoperative treatment and resection.

Methods and systems for determining copy number variants are disclosed. An example method can comprise applying a sample grouping technique to select reference coverage data, normalizing sample coverage data comprising a plurality of genomic regions, and fitting a mixture model to the normalized sample coverage data based on the selected reference coverage data. An example method can comprise identifying one or more copy number variants (CNVs) according to a Hidden Markov Model (HMM) based on the normalized sample coverage data and the fitted mixture model. An example method can comprise outputting the one or more copy number variants.

Methods and systems for determining copy number variants are disclosed. An example method can comprise applying a sample grouping technique to select reference coverage data, normalizing sample coverage data comprising a plurality of genomic regions, and fitting a mixture model to the normalized sample coverage data based on the selected reference coverage data. An example method can comprise identifying one or more copy number variants (CNVs) according to a Hidden Markov Model (HMM) based on the normalized sample coverage data and the fitted mixture model. An example method can comprise outputting the one or more copy number variants.

Provided herein is a method for sequence analysis that comprises analyzing PCR reactions that each contain different portions of the same sample, wherein at least some of the primer pairs are in more than one PCR reaction and at least one of the PCR reactions contains some but not all of the primer pairs of the other reaction(s).

Methods and systems for deconvoluting tumor ecosystems for personalized cancer therapy are disclosed. Generally, human cancers exhibit large variation in behavior between and within patients, which is in large part related to cellular composition. Identifying cell types can identify specific types of tumors and/or cancers present in an individual. Further embodiments generally describe identifying therapies from clinical trials to which the tumor or cancer ecotypes respond, thus providing personalized therapies based on the identified cancer or tumor type.

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 system and method for accurate determination of sequence variants from noisy sequencing data, including single nucleotide variants and structural variants of the internal tandem duplication type. This system expands the utility of inexpensive sequencing instruments which stream relatively high-error output sequences in real time, such that they may be used in high-stakes contexts, such as clinical cancer care. An example application is Acute Myeloid Leukemia (AML), where healthcare providers may need to make decisions in hours, is provided.