Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Provided herein are processes for preparing an oligomer (e.g., a morpholino oligomer). The synthetic processes described herein may be advantageous to scaling up oligomersynthesis while maintaining overall yield and purity of a synthesized oligomer.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.

Abstract: Provided herein are processes for preparing an oligomer (e.g., a morpholino oligomer). The synthetic processes described herein may be advantageous to scaling up oligomer synthesis while maintaining overall yield and purity of a synthesized oligomer.