Abstract: A computer-implemented method for Error! Reference source not found. may include (i) generating a reference matrix representing a genetic reference panel in terms of dosages for given reference samples at given loci, (ii) decomposing the reference matrix via non-negative matrix factorization into an ancestral genotype matrix and an ancestral attribution matrix, (iii) resampling the reference matrix, (iv) deriving an ancestral alternate reads matrix that, when multiplied with the ancestral attribution matrix, approximates the resampled reference matrix, (v) deriving an ancestral attribution vector that, when multiplied with the ancestral alternate reads matrix, approximates a vector representing the test sample, and (vi) determining the genetic ancestry of the subject based on the ancestral attribution vector. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A computer-implemented method for inferring genetic ancestry from low-coverage genomic data may include (i) generating a reference matrix representing a genetic reference panel in terms of dosages for given reference samples at given loci, (ii) decomposing the reference matrix via non-negative matrix factorization into an ancestral genotype matrix and an ancestral attribution matrix, (iii) resampling the reference matrix, (iv) deriving an ancestral alternate reads matrix that, when multiplied with the ancestral attribution matrix, approximates the resampled reference matrix, (v) deriving an ancestral attribution vector that, when multiplied with the ancestral alternate reads matrix, approximates a vector representing the test sample, and (vi) determining the genetic ancestry of the subject based on the ancestral attribution vector. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A computer-implemented method for inferring genetic ancestry from low-coverage genomic data may include (i) generating a reference matrix representing a genetic reference panel in terms of dosages for given reference samples at given loci, (ii) decomposing the reference matrix via non-negative matrix factorization into an ancestral genotype matrix and an ancestral attribution matrix, (iii) resampling the reference matrix, (iv) deriving an ancestral alternate reads matrix that, when multiplied with the ancestral attribution matrix, approximates the resampled reference matrix, (v) deriving an ancestral attribution vector that, when multiplied with the ancestral alternate reads matrix, approximates a vector representing the test sample, and (vi) determining the genetic ancestry of the subject based on the ancestral attribution vector. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A computer-implemented method for optimizing performance of a DNA-based noninvasive prenatal screen includes generating a plurality of synthetic sequencing datasets by, for each of the plurality of synthetic sequencing datasets, (i) generating at least one of a plurality of synthetic copy number variants comprising a synthetic number of copies of at least a portion of a region of interest represented by a synthetic number of sequencing reads from one or more segments within the region of interest, and (ii) modifying a real sequencing dataset, which includes genetic sequencing data from a real test sample comprising maternal and fetal cfDNA, by replacing a number of real sequencing reads from the one or more segments within the region of interest in the real test sample with the synthetic number of sequencing reads. Various other methods and systems are also disclosed.

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.