The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data, and in particular mass spectrometry data obtained by data-independent acquisition. The systems and methods achieve improvements in sequencing accuracy over existing systems and methods and enables complete assembly of novel protein sequences without assisting databases. To sequence peptides from mass spectrometry data obtained by data-independent acquisition, precursor profiles representing intensities of one or more precursor ion signals associated with a precursor retention time and fragment ion spectra representing signals from fragment ions and fragment retention times are fed into a neural network.

A method for mass spectral analysis of molecules based on full mass spectral profile or raw scan mode data, comprising the steps of specifying the basic building blocks for the molecule; estimating initial values including trial numbers of building blocks, charge states, and possible modifications; calculating discrete isotope distributions based on elemental compositions; calculating a profile mode theoretical mass spectrum using a target mass spectrum peak shape function; performing regression analysis between acquired profile mode mass spectrum data and calculated theoretical mass spectrum data and reporting regression statistics; using regression statistics as feedbacks to update initially estimated values including trial numbers of building blocks, charge states, and possible modifications; and repeating selected step to optimize the regression statistics. A mass spectrometer operating in accordance with the method. A medium having computer readable program instructions for causing a mass spectrometer associated with a computer to operate in accordance with the method.

An analysis method includes: performing liquid chromatography using a mobile phase including an adsorption prevention agent for preventing adsorption of a sample including a compound having a phosphate group to metal; and performing mass spectrometry on an eluate of the liquid chromatography. The adsorption prevention agent includes an oxalic acid or a salt of the oxalic acid.

A measurement section (1) performs a mass spectrometric analysis for each micro area within a measurement area on a sample. A dimension reduction processor (23) performs data processing by non-linear dimension reduction using manifold learning on mass spectrometric data for each micro area, to obtain, for each micro area, a set of data reduced to three dimensions from the dimensions corresponding to the number of mass-to-charge-ratio values. A display color determiner (24) determines a color for each of the points corresponding to the data of the micro areas after the dimension reduction, by arranging those points within a three-dimensional space having three axes representing the three dimensions, with three primary colors respectively assigned to the three axes. A segmentation image creator (25) creates a segmentation image corresponding to the measurement area or a partial area in the measurement area, by arranging, on two dimensions, pixels which respectively correspond to the points within the three-dimensional space, where each pixel has a color given to the point corresponding to the pixel and is located according to the position within the measurement area of the micro area corresponding to the point.

A method for assessing drug-resistant microorganism includes the following steps. A model establishing step is performed so as to obtain an antibiotic resistance assessing classifier. A test sample is provided. A sample pre-processing step is performed so as to obtain a processed sample. An analysis step is performed so as to obtain a target mass spectrum data. A spectrum pre-processing step is performed so as to obtain a normalized target mass spectrum data. A feature extraction step is performed so as to obtain a spectrum feature. An assessing step is performed, wherein the spectrum feature is analyzed by the antibiotic resistance assessing classifier so as to output an assessed result of drug-resistant microorganism, and the assessed result of drug-resistant microorganism is for assessing whether the test microorganism is a drug-resistant microorganism or not.

Described are techniques for processing data. Sample analysis is performed generating scans of data. Each scan comprises a set of data elements each associating an ion intensity count with a plurality of dimensions including a retention time dimension and a mass to charge ratio dimension. The scans are analyzed to identify one or more ion peaks. Analyzing includes filtering a first plurality of the scans producing a first plurality of filtered output scans. The filtering including first filtering producing a first filtering output, wherein the first filtering includes executing a plurality of threads in parallel which apply a first filter to the first plurality of scans to produce the first filtering output. Each of the plurality of threads computes at least one filtered output point for at least one corresponding input point included in the plurality of scans. Analyzing includes detecting one or more peaks using the filtered output scans.

Peak determination is executed with respect to the mass spectrum of a sample to generate a peak list. For each of the plurality of peaks contained in the peak list, a Kendrick mass (KM) of a designated monomer is calculated. An RKM is calculated, the RKM being a fractional part of a value obtained by dividing the KM by the integer mass of the monomer, or a remainder of dividing a nominal Kendrick mass (NKM) by the integer mass of the monomer. A plurality of peaks contained in the peak list and satisfying a grouping condition, including the permissible range of the RKM of the starting point peak, are grouped.

A method of ion mobility and/or mass spectrometry is disclosed in which the ion mobility and/or mass to charge ratio of an ion is determined using an algorithm or relationship that relates the transit time or average ion velocity of the ion through an ion separation device in which one or more time-varying electric field is used to separate ions passing therethrough to one or more parameters for the device, the mass to charge ratio of the ion and the ion mobility of the ion.

A method of assessing the analytical performance of a biochemical measured using a multi-analyte assay is described. The method includes analytically validating a measurement of the level of a first biochemical in a sample, wherein the first biochemical has been previously analytically validated for three or more analytical validation conditions; measuring the level of a second biochemical in a sample, wherein the second biochemical is structurally or biochemically related to the first biochemical; and comparing validation parameters of the first biochemical with validation parameters of the second biochemical to determine whether the performance of the second biochemical is acceptable based on the comparison results.

A spectrum acquirer acquires a plurality of mass spectrums. A specific physical quantity calculator calculates a specific physical quantity reflecting an amount of ions with respect to each of the plurality of obtained mass spectrums. A spectrum sorter sorts the plurality of mass spectrums in order of the specific physical quantity calculated with respect to each mass spectrum. A display controller allows a display to display the plurality of sorted mass spectrums. A spectrum selector selects a plurality of mass spectrums having specific physical quantities in a designated range from the plurality of displayed mass spectrums. A post-selection spectrum integrator integrates the plurality of selected mass spectrums. The display controller allows the display to display the post-selection integrated mass spectrum.