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.

This disclosure provides systems and methods for sample processing and data analysis. Sample processing may include nucleic acid sample processing and subsequent sequencing. Some or all of a nucleic acid sample may be sequenced to provide sequence information, which may be stored or otherwise maintained in an electronic storage location. The sequence information may be analyzed with the aid of a computer processor, and the analyzed sequence information may be stored in an electronic storage location that may include a pool or collection of sequence information and analyzed sequence information generated from the nucleic acid sample. Methods and systems of the present disclosure can be used, for example, for the analysis of a nucleic acid sample, for producing one or more libraries, and for producing biomedical reports. Methods and systems of the disclosure can aid in the diagnosis, monitoring, treatment, and prevention of one or more diseases and conditions.

Embodiments of a method and/or system can include generating a set of quality control template (QCT) molecules; determining a set of QCT sequence read clusters based on the set of QCT molecules, such as based on variation regions of the set of QCT molecules; and based on the set of QCT sequence read clusters, determining a sequencing-related parameter, such as a contamination parameter and/or molecule count parameter, associated with the at least one of sequencing library preparation and sequencing.

Embodiments of a method and/or system can include generating a set of quality control template (QCT) molecules; determining a set of QCT sequence read clusters based on the set of QCT molecules, such as based on variation regions of the set of QCT molecules; and based on the set of QCT sequence read clusters, determining a sequencing-related parameter, such as a contamination parameter and/or molecule count parameter, associated with the at least one of sequencing library preparation and sequencing.

Disclosed herein include systems, devices, and methods for determining a variable number tandem repeat (VNTR) status. Haplotypes of a VNTR can be determined using long sequence reads of reference samples aligned to the VNTR in a reference. Short reads of a test sample of a test subject can be aligned to the haplotypes determined using the long sequence reads to determine a VNTR status (e.g., one or more haplotypes or a genotype of the test subject) of the test subject based on the probability indications of the haplotypes.

Disclosed herein include systems, devices, and methods for determining a variable number tandem repeat (VNTR) status. Haplotypes of a VNTR can be determined using long sequence reads of reference samples aligned to the VNTR in a reference. Short reads of a test sample of a test subject can be aligned to the haplotypes determined using the long sequence reads to determine a VNTR status (e.g., one or more haplotypes or a genotype of the test subject) of the test subject based on the probability indications of the haplotypes.

In one embodiment, a method includes accessing a sample of genetic material associated with a first animal, wherein the sample of genetic material comprises raw genotypes, generating phased haplotypes based on the raw genotypes, generating local assignments for genetic populations for the phased haplotypes by machine learning algorithms based on comparisons between the phased haplotypes and a reference panel comprising reference haplotypes associated with reference populations, and sending instructions to a user device for presenting an output associated with the first animal to a user, wherein the output is generated based on the local assignments for the genetic populations.

The present disclosure provides methods and systems for screening or detecting a colorectal cancer or following colorectal disease progression that may be applied to cell-free nucleic acids such as cell-free DNA. The method may use detection of methylation signals within a single sequencing read in identified genomic regions as input features to train a machine learning model and generate a classifier useful for stratifying populations of individuals. The method may comprise extracting DNA from a cell-free sample obtained from a subject, converting the DNA for methylation sequencing, generating sequencing reads, and detecting colon proliferative cell disorder-associated signals in the sequencing information and training a machine learning model to provide a discriminator capable of distinguishing groups in a subject population such as healthy, cancer or distinguishing disease subtype or stage. The method may be used for, e.g., predicting, prognosticating, and/or monitoring response to treatment, tumor load, relapse, or colorectal cancer development.

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