Abstract: The present disclosure describes methods and systems for analyzing mass spectrometry data. The methods and systems can comprise an operation of contacting a plurality of biomolecules with a plurality of surfaces. The methods and systems can further comprise performing mass spectrometry on the plurality of biomolecules, or a portion or derivative thereof. The methods and systems can further comprise classifying a sample based on the mass spectra. The methods and systems of the disclosure may be used for identifying evidence of operational errors in mass spectrometry datasets.

Abstract: In some aspects, the present disclosure provides a method for normalizing and processing mass spectrometry datasets. In some embodiments, the method comprises loading a plurality of mass spectrometry data obtained from a plurality of samples into a memory of a computing node to generate a cached dataset. In some embodiments, the method comprises transmitting a copy of the cached dataset to a plurality of cache memories of a plurality of computing nodes. In some embodiments, the method comprises determining, using the plurality of computing nodes, a plurality of feature values for the plurality of mass spectrometry data. In some embodiments, the method comprises normalizing, using the plurality of computing nodes, across the plurality of mass spectrometry datasets using the plurality of feature values to generate a plurality of normalized mass spectrometry data.

Abstract: An exemplary chromatographic alignment system accesses a target file including data representative of a plurality of chromatographic features detected from a first sample and a reference file including data representative of a plurality of chromatographic features detected from a second sample. The system identifies, based on the target and reference files, a distinct retention time offset value for each chromatographic feature included in a first subset of the plurality of chromatographic features detected from the first sample. The system determines, based on the identified distinct retention time offset values for the chromatographic features included in the first subset and on a machine learning model, a distinct predicted retention time offset value for each chromatographic feature included in a second subset of the plurality of chromatographic features detected from the first sample.

Abstract: An exemplary chromatographic alignment system accesses a target file including data representative of a plurality of chromatographic features detected from a first sample and a reference file including data representative of a plurality of chromatographic features detected from a second sample. The system identifies, based on the target and reference files, a distinct retention time offset value for each chromatographic feature included in a first subset of the plurality of chromatographic features detected from the first sample. The system determines, based on the identified distinct retention time offset values for the chromatographic features included in the first subset and on a machine learning model, a distinct predicted retention time offset value for each chromatographic feature included in a second subset of the plurality of chromatographic features detected from the first sample.

Abstract: A method for analyzing a sample can include obtaining an exclusion list of retention time and mass-to-charge ranges corresponding to high abundance compounds within a sample; separating components of the sample using the chromatographic column; obtaining a first mass data set using the mass analyzer while excluding ions within retention time and mass-to-charge ranges of the exclusion list; generating an inclusion list of features of the first mass data set; fragmenting ions corresponding to features of the inclusion list; obtaining a second mass data set from the fragmented ions; and identifying and/or quantifying low abundance compounds based on the second mass data set.

Abstract: A method for analyzing a sample can include obtaining an exclusion list of retention time and mass-to-charge ranges corresponding to high abundance compounds within a sample; separating components of the sample using the chromatographic column; obtaining a first mass data set using the mass analyzer while excluding ions within retention time and mass-to-charge ranges of the exclusion list; generating an inclusion list of features of the first mass data set; fragmenting ions corresponding to features of the inclusion list; obtaining a second mass data set from the fragmented ions; and identifying and/or quantifying low abundance compounds based on the second mass data set.

Abstract: A method for detecting a list of known biopolymer molecules comprises: calculating, for each biopolymer, a respective list of oligomer molecules predicted to be produced by chemical processing; calculating, for each oligomer molecule, a respective predicted chromatographic elution time period; assigning, for each biopolymer molecule, one or more selected oligomer molecules to be detected, wherein the selecting is performed using weighted selection probabilities determined from the predicted elution times; scheduling a plurality of oligomer detection events of a detection system, wherein each oligomer detection event corresponds to a respective one of the predicted elution time periods; performing the chemical reaction or processing of the sample to generate a processed sample; introducing the processed sample into a chromatographic system; introducing any eluting oligomers into the detection system; and operating the detection system so as to search for each of the selected oligomer molecules in accordance wi

Abstract: A method for detecting a list of known biopolymer molecules comprises: calculating, for each biopolymer, a respective list of oligomer molecules predicted to be produced by chemical processing; calculating, for each oligomer molecule, a respective predicted chromatographic elution time period; assigning, for each biopolymer molecule, one or more selected oligomer molecules to be detected, wherein the selecting is performed using weighted selection probabilities determined from the predicted elution times; scheduling a plurality of oligomer detection events of a detection system, wherein each oligomer detection event corresponds to a respective one of the predicted elution time periods; performing the chemical reaction or processing of the sample to generate a processed sample; introducing the processed sample into a chromatographic system; introducing any eluting oligomers into the detection system; and operating the detection system so as to search for each of the selected oligomer molecules in accordance wi