Abstract: The present disclosure relates to a laboratory execution system that provides for automation of laboratory processes. A centralized data management system may be dynamically updated and used to facilitate management of components of the laboratory execution system, such as an automation system and an analytics results management system that may facilitate complex analytical functions, such as synthesizing raw test data. Potential workflows include the detection of specific molecules of interest.

Abstract: The present disclosure relates to methods for detecting unique genetic signatures derived from markers such as, for example, mutations, somatic or germ-line, in nucleic acids obtained from biological samples. The sensitivity of the methods provides for detection of mutations associated with a disease, e.g., cancer mutations, or with inherited disease, e.g., an autosomal recessive disease, in a noninvasive manner at ultra-low proportions of sequences carrying mutations to sequences carrying normal, e.g., non-cancer sequences, or a reference sequence, e.g., a human reference genome.

Abstract: The present disclosure relates to a laboratory execution system that provides for automation of laboratory processes. A centralized data management system may be dynamically updated and used to facilitate management of components of the laboratory execution system, such as an automation system and an analytics results management system that may facilitate complex analytical functions, such as synthesizing raw test data. Potential workflows include the detection of specific molecules of interest.

Abstract: High-fidelity, high-throughput nucleic acid sequencing enables healthcare practitioners and patients to gain insight into genetic variants and potential health risks. However, previous methods of nucleic acid sequencing often introduces sequencing errors (for example, mutations that arise during the preparation of a nucleic acid library, during amplification, or sequencing). Provided herein are sequencing adapters comprising a nondegenerate or variable length molecular barcode and compositions comprising a plurality of sequencing adapters, which can be useful for sequencing nucleic acids. Further provided are methods of using the sequencing adapters, including methods of sequencing nucleic acids, methods of identifying an error in a nucleic acid sequence, and methods of determining the number of nucleic acid molecules in a library.

Abstract: Fetal maternal samples taken from pregnant women include both maternal cell-free DNA and fetal cell-free DNA. Described herein are methods for determining a chromosomal abnormality of a test chromosome or a portion thereof in a fetus by analyzing a test maternal sample of a woman carrying said fetus, wherein the test maternal sample comprises fetal cell-free DNA and maternal cell-free DNA. The chromosomal abnormality can be, for example, aneuploidy or the presence of a microdeletion. In some embodiments, the chromosomal abnormality is determined by measuring a dosage of the test chromosome or portion thereof in the test maternal sample, measuring a fetal fraction of cell-free DNA in the test maternal sample, and determining an initial value of likelihood that the test chromosome or the portion thereof in the fetal cell-free DNA is abnormal based on the measured dosage, an expected dosage of the test chromosome or portion thereof, and the measured fetal fraction.

Abstract: The present disclosure relates to a laboratory execution system that provides for automation of laboratory processes. A centralized data management system may be dynamically updated and used to facilitate management of components of the laboratory execution system, such as an automation system and an analytics results management system that may facilitate complex analytical functions, such as synthesizing raw test data. Potential workflows include the detection of specific molecules of interest.

Abstract: High-fidelity, high-throughput nucleic acid sequencing enables healthcare practitioners and patients to gain insight into genetic variants and potential health risks. However, previous methods of nucleic acid sequencing often introduces sequencing errors (for example, mutations that arise during the preparation of a nucleic acid library, during amplification, or sequencing). Provided herein are sequencing adapters comprising a nondegenerate or variable length molecular barcode and compositions comprising a plurality of sequencing adapters, which can be useful for sequencing nucleic acids. Further provided are methods of using the sequencing adapters, including methods of sequencing nucleic acids, methods of identifying an error in a nucleic acid sequence, and methods of determining the number of nucleic acid molecules in a library.

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: The present disclosure relates to methods for detecting unique genetic signatures derived from markers such as, for example, mutations, somatic or germ-line, in nucleic acids obtained from biological samples. The sensitivity of the methods provides for detection of mutations associated with a disease, e.g., cancer mutations, or with inherited disease, e.g., an autosomal recessive disease, in a noninvasive manner at ultra-low proportions of sequences carrying mutations to sequences carrying normal, e.g., non-cancer sequences, or a reference sequence, e.g., a human reference genome.

Abstract: The present disclosure relates to methods for detecting unique genetic signatures derived from markers such as, for example, mutations, somatic or germ-line, in nucleic acids obtained from biological samples. The sensitivity of the methods provides for detection of mutations associated with a disease, e.g., cancer mutations, or with inherited disease, e.g., an autosomal recessive disease, in a noninvasive manner at ultra-low proportions of sequences carrying mutations to sequences carrying normal, e.g., non-cancer sequences, or a reference sequence, e.g., a human reference genome.

Abstract: The present disclosure relates to methods for detecting unique genetic signatures derived from markers such as, for example, mutations, somatic or germ-line, in nucleic acids obtained from biological samples. The sensitivity of the methods provides for detection of mutations associated with a disease, e.g., cancer mutations, or with inherited disease, e.g., an autosomal recessive disease, in a noninvasive manner at ultra-low proportions of sequences carrying mutations to sequences carrying normal, e.g., non-cancer sequences, or a reference sequence, e.g., a human reference genome.

Abstract: The present disclosure relates to methods for detecting unique genetic signatures derived from markers such as, for example, mutations, somatic or germ-line, in nucleic acids obtained from biological samples. The sensitivity of the methods provides for detection of mutations associated with a disease, e.g., cancer mutations, or with inherited disease, e.g., an autosomal recessive disease, in a noninvasive manner at ultra-low proportions of sequences carrying mutations to sequences carrying normal, e.g., non-cancer sequences, or a reference sequence, e.g., a human reference genome.

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: Fetal maternal samples taken from pregnant women include both maternal cell-free DNA and fetal cell-free DNA. Described herein are methods for determining a chromosomal abnormality of a test chromosome or a portion thereof in a fetus by analyzing a test maternal sample of a woman carrying said fetus, wherein the test maternal sample comprises fetal cell-free DNA and maternal cell-free DNA. The chromosomal abnormality can be, for example, aneuploidy or the presence of a microdeletion. In some embodiments, the chromosomal abnormality is determined by measuring a dosage of the test chromosome or portion thereof in the test maternal sample, measuring a fetal fraction of cell-free DNA in the test maternal sample, and determining an initial value of likelihood that the test chromosome or the portion thereof in the fetal cell-free DNA is abnormal based on the measured dosage, an expected dosage of the test chromosome or portion thereof, and the measured fetal fraction.

Abstract: High-fidelity, high-throughput nucleic acid sequencing enables healthcare practitioners and patients to gain insight into genetic variants and potential health risks. However, previous methods of nucleic acid sequencing often introduces sequencing errors (for example, mutations that arise during the preparation of a nucleic acid library, during amplification, or sequencing). Provided herein are sequencing adapters comprising a nondegenerate or variable length molecular barcode and compositions comprising a plurality of sequencing adapters, which can be useful for sequencing nucleic acids. Further provided are methods of using the sequencing adapters, including methods of sequencing nucleic acids, methods of identifying an error in a nucleic acid sequence, and methods of determining the number of nucleic acid molecules in a library.

Abstract: Prenatal genetic testing allows early detection of genetic disease in a fetus. Described herein are methods of detecting the presence or absence of a genetic variant in a region of interest in the genome of a fetus in a pregnant woman. The methods are noninvasive, and can use cell-free DNA (cfDNA) present in the plasma of the pregnant woman. A DNA library is constructed from the cfDNA, and DNA molecules comprising the region of interest or portions thereof are enriched and analyzed, for example by sequencing. The methods described herein can also rely on constructing a maternal haplotype to provide even higher resolution fetal genetic variant determination.

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: The present disclosure relates to methods for detecting unique genetic signatures derived from markers such as, for example, mutations, somatic or germ-line, in nucleic acids obtained from biological samples. The sensitivity of the methods provides for detection of mutations associated with a disease, e.g., cancer mutations, or with inherited disease, e.g., an autosomal recessive disease, in a noninvasive manner at ultra-low proportions of sequences carrying mutations to sequences carrying normal, e.g., non-cancer sequences, or a reference sequence, e.g., a human reference genome.

Abstract: Recent developments in cost-effective DNA sequencing allows for individualized genomic screening of a subject for genetic sequence variants. Training a pathogenicity prediction model using semi-supervised training methods produces a better model for predicting the pathogenicity of a test genetic sequence variant. Provided herein are methods for predicting the pathogenicity of a test genetic sequence variant by utilizing a training data set comprising labeled benign genetic sequence variants unlabeled genetic sequence variants, the unlabeled genetic sequence variants comprising a mixture of benign genetic sequence variants and pathogenic genetic sequence variants. The genetic sequences are annotated with one or more features and a machine learning model is trained in a semi-supervised process based on the training data.