Abstract: The invention disclosed herein generally relates to methods of assessing microsatellite instability in a subject. In an aspect, the present disclosure provides a computer-implemented method of assessing microsatellite instability of a subject, comprising: obtaining a quantitative measure of a plurality of microsatellite repeat elements from a blood sample of a subject; processing the plurality of quantitative measures to obtain a statistical measure of deviation of the plurality of quantitative measures; and detecting a presence of the microsatellite instability (MSI) of the subject when the statistical measure of deviation of the plurality of quantitative measures satisfies a predetermined criterion, or detecting an absence of the microsatellite instability (MSI) of the subject when the statistical measure of deviation of the plurality of quantitative measures does not satisfy the predetermined criterion.

Abstract: The present disclosure provides methods for fingerprinting of biological samples of a subject.

Abstract: According to one aspect, systems and processes for assaying a plurality of nucleic acid samples are provided. In an exemplary process, a matrix is generated including pools and samples using a pooling scheme with decoding capability equal to a number D. Matrix organization includes assigning one pool in a set of pools per row by one sample in a set of samples per column. Sample assignment creates a known pattern of pools, wherein each sample in the set of pools is assigned a total number of D+1 times and any two pools have at most one sample in common. Samples are pooled based on a pooling scheme, where pooled samples are assayed. Positive pools are determined and one or more positive samples are identified. The matrix is displayed as a visual pattern representing the known pattern of pools, the identified positive samples, and the determined positive pools.

Abstract: Processes and systems for reading variants from a genome sample relative to a reference genomic sequence are provided. An exemplary process includes collecting a set reads and generating a k-mer graph from the reads. For example, the k-mer graph can be constructed to represent all possible substrings of the collected reads. The k-mer graph may be reduced to a contiguous graph, and a set of possible haplotypes generated from the contiguous graph. The process may further generate, the error table providing a filter for common sequencer errors. The process may then generate a set of diplotypes based on the set of haplotypes and the generated error table and score the set of diplotypes to identify variants from the reference genome. Scoring the diplotypes may include determining a posterior probability for each of the diplotypes, with the highest scoring diplotype(s) reported as the result.