Abstract: This disclosure describes methods, non-transitory-computer readable media, and systems that can accurately generate genotypes for tandem-repeat regions of a genomic sample by utilizing an expectation-maximization (EM) algorithm and a stutter model. The disclosed system can extract spanning nucleotide reads that comprise whole tandem-repeat regions. The disclosed system may perform an expectation stage of an EM algorithm and utilize a stutter model to predict expected genotype probabilities of tandem-repeat genotypes given a distribution of spanning reads. In some implementations, the disclosed system further performs a maximization stage of the EM algorithm to adjust parameters of the stutter model based on the expected genotype probabilities to maximize a total probability of the expected genotype probabilities. The disclosed system can repeat the expectation and maximization stages until the total probability of the expected genotype probabilities converges.

Abstract: The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women a plurality of circulating RNA (C-RNA) molecules.

Abstract: The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women a plurality of circulating RNA (C-RNA) molecules.

Abstract: The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women a plurality of circulating RNA (C-RNA) molecules.

Abstract: The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women a plurality of circulating RNA (C-RNA) molecules.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can use a computationally efficient model to determine a corrected methylation-level value for a specific sample nucleotide sequence. For instance, the disclosed systems determine a false positive rate and a false negative rate at which a given methylation sequencing assay converts cytosine bases. Based on the determined false positive rate and false negative rate, the disclosed systems determine a corrected methylation-level value that corrects for a bias of the given methylation sequencing assay.

Abstract: The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women circulating RNA (C-RNA) molecules associated with preeclampsia.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can use a machine-learning to determine factors or scores indicating an error level with which a given methylation assay detects methylation of cytosine bases. For instance, the disclosed systems use a machine-learning model to generate a bias score indicating a degree to which a given methylation assay errs in detecting cytosine methylation when specific sequence contexts surround such cytosines compared to other sequence contexts. The machine-learning model may take various forms of models, including a decision-tree model, a neural network, or a combination of a decision-tree model and a neural network. In some cases, the disclosed system combines or uses bias scores from multiple machine-learning models to generate a consensus bias score.

Abstract: The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women a plurality of circulating RNA (C-RNA) molecules.

Abstract: The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women a plurality of circulating RNA (C-RNA) molecules.

Abstract: A method for detecting different analytes includes mixing different analytes with sensing probes, wherein at least some of the sensing probes are specific to respective ones of the analytes. The analytes respectively are captured by the sensing probes that are specific to those analytes. Fluorophores respectively are coupled to sensing probes that captured respective analytes. The sensing probes are mixed with beads, wherein the beads are specific to respective ones of the sensing probes, and wherein the beads include different codes identifying the analytes to which those sensing probes are specific. The sensing probes respectively are coupled to beads that are specific to those sensing probes. The beads are identified that are coupled to the sensing probes that captured analytes using at least fluorescence from the fluorophores coupled to those sensing probes. The analytes that are captured are identified.

Abstract: The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women a plurality of circulating RNA (C-RNA) molecules.

Abstract: The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women a plurality of circulating RNA (C-RNA) molecules.