Abstract: A method and system for the identification and/or characterisation of properties of a sample using mass spectrometry. The method involves producing a measured data set from a sample using a mass spectrometer, deconvoluting the measured data set by Bayesian inference to produce a family of plausible deconvoluted data sets, inferring an underlying deconvoluted data set from the family of plausible deconvoluted data sets and using the underlying deconvoluted data set to identify and/or characterise the sample.

Abstract: A method of mass spectrometry is disclosed wherein a signal output from an ion detector is digitized by an Analogue to Digital Converter and is then deconvoluted to determine one or more ion arrival times and one more ion arrival intensities. The process of deconvoluting the ion signal involves determining a point spread function characteristic of an ion arriving at and being detected by the ion detector. A distribution of ion arrival times which produces a best fit to the digitised signal is then determined given that each ion arrival is assumed to produce a response given by the point spread function. A plurality of ion arrival times are then combined to produce a composite ion arrival time-intensity spectrum.

Abstract: A method and system for the identification and/or characterization of properties of a sample using mass spectrometry. The method involves producing a measured spectrum of data from a sample using a mass spectrometer, deconvoluting the measured spectrum of data by Bayesian inference to produce a family of plausible deconvoluted spectra of data, inferring an underlying spectrum of data from the family of plausible deconvoluted spectra of data and using the underlying spectrum of data to identify and/or characterize the sample.

Abstract: A method and system for the identification and/or characterisation of properties of a sample using mass spectrometry. The method involves producing a measured spectrum of data from a sample using a mass spectrometer, deconvoluting the measured spectrum of data by Bayesian inference to produce a family of plausible deconvoluted spectra of data, inferring an underlying spectrum of data from the family of plausible deconvoluted spectra of data and using the underlying spectrum of data to identify and/or characterise the sample.

Abstract: A method of mass spectrometry is disclosed wherein a signal output from an ion detector is digitised by an Analogue to Digital Converter and is then deconvoluted to determine one or more ion arrival times and one more ion arrival intensities. The process of deconvoluting the ion signal involves determining a point spread function characteristic of an ion arriving at and being detected by the ion detector. A distribution of ion arrival times which produces a best fit to the digitised signal is then determined given that each ion arrival is assumed to produce a response given by the point spread function. A plurality of ion arrival times are then combined to produce a composite ion arrival time-intensity spectrum.

Abstract: A mass spectrometer and method of mass spectrometry are disclosed wherein two separate samples are mass analysed and then the relative intensity, concentration or expression level of one or more components, molecules or analytes in a first sample is quantitated relative to the intensity, concentration or expression level of one or more components, molecules or analytes in a second sample. The relative quantitation is performed probabilistically without the need to resort to using internal calibrants.

Abstract: A mass spectrometer and a method of mass spectrometry are disclosed wherein periodic background noise is effectively filtered out from the mass spectral data. An overall mass window is superimposed upon the mass spectral data. The overall mass window preferably comprises 21 nominal mass windows each preferably having a width of 1.0005 amu. Each nominal mass window preferably comprises 20 channels. An intensity distribution relating to all the first channels of the 21 nominal mass windows is determined. An intensity quantile is determined from the intensity distribution. The intensity quantile is taken to represent the background intensity in the first channel of the central nominal mass window. This process is repeated for the other channels so that the background intensity across the whole of the central nominal mass window is estimated and then subtracted from the raw mass spectral data comprising the central nominal mass window.

Abstract: A mass spectrometer and method of mass spectrometry are disclosed wherein two separate samples are mass analysed and then the relative intensity, concentration or expression level of one or more components, molecules or analytes in a first sample is quantitated relative to the intensity, concentration or expression level of one or more components, molecules or analytes in a second sample. The relative quantitation is performed probabilistically without the need to resort to using internal calibrants.

Abstract: A method of identifying a protein, polypeptide or peptide by means of mass spectrometry and especially by tandem mass spectrometry is disclosed. The method preferably models the fragmentation of a peptide or protein in a tandem mass spectrometer to facilitate comparison with an experimentally determined spectrum. A fragmentation model is used which takes account of all possible fragmentation pathways which a particular sequence of amino acids may undergo. A peptide or protein may be identified by comparing an experimentally determined mass spectrum with spectra predicted using such a fragmentation model from a library of known peptides or proteins. Alternatively, a de novo method of determining the amino acid sequence of an unknown peptide using such a fragmentation model may be used.

Abstract: An apparatus for identifying a protein, polypeptide or peptide by means of mass spectrometry and especially by tandem mass spectrometry is disclosed. The apparatus preferably functions to model the fragmentation of a peptide or protein in a tandem mass spectrometer to facilitate comparison with an experimentally determined spectrum. A fragmentation model is used which takes account of all possible fragmentation pathways which a particular sequence of amino acids may undergo. A peptide or protein may be identified by comparing an experimentally determined mass spectrum with spectra predicted using such a fragmentation model from a library of known peptides or proteins.

Abstract: A mass spectrometer and a method of mass spectrometry are disclosed wherein periodic background noise is effectively filtered out from the mass spectral data. An overall mass window is superimposed upon the mass spectral data. The overall mass window preferably comprises 21 nominal mass windows each preferably having a width of 1.0005 amu. Each nominal mass window preferably comprises 20 channels. An intensity distribution relating to all the first channels of the 21 nominal mass windows is determined. An intensity quantile is determined from the intensity distribution. The intensity quantile is taken to represent the background intensity in the first channel of the central nominal mass window. This process is repeated for the other channels so that the background intensity across the whole of the central nominal mass window is estimated and then subtracted from the raw mass spectral data comprising the central nominal mass window.

Abstract: A method of identifying a protein, polypeptide or peptide by means of mass spectrometry and especially by tandem mass spectrometry is disclosed. The method preferably models the fragmentation of a peptide or protein in a tandem mass spectrometer to facilitate comparison with an experimentally determined spectrum. A fragmentation model is used which takes account of all possible fragmentation pathways which a particular sequence of amino acids may undergo. A peptide or protein may be identified by comparing an experimentally determined mass spectrum with spectra predicted using such a fragmentation model from a library of known peptides or proteins. Alternatively, a de novo method of determining the amino acid sequence of an unknown peptide using such a fragmentation model may be used.

Abstract: A method of identifying a protein, polypeptide or peptide by means of mass spectrometry and especially by tandem mass spectrometry is disclosed. The method preferably models the fragmentation of a peptide or protein in a tandem mass spectrometer to facilitate comparison with an experimentally determined spectrum. A fragmentation model is used which takes account of all possible fragmentation pathways which a particular sequence of amino acids may undergo. A peptide or protein may be identified by comparing an experimentally determined mass spectrum with spectra predicted using such a fragmentation model from a library of known peptides or proteins. Alternatively, a de novo method of determining the amino acid sequence of an unknown peptide using such a fragmentation model may be used.

Abstract: A method of determining the sequence of amino acids that constitute peptides, polypeptides or proteins by mass spectrometry and especially by tandem mass spectrometry is disclosed without the use of any additional data concerning the nature of the peptide and without any limit to the number of possible sequences considered. The method can be implemented on a personal computer typically used for data acquisition on the tandem mass spectrometer even in the case of peptides comprising 10 or more amino acids. The method does not rely on exhaustive comparison of the spectra predicted from every possible amino acid sequence with any molecular weight constraint, but instead uses mathematical techniques to simulate the effect of such a complete search without actually carrying it out.

Abstract: The accuracy of the elemental analysis of a sample by mass spectrometry is enhanced by providing plural estimates of the likely mass spectrum of the sample. These estimates are compared with experimentally obtained spectra to generate trial sets of possible compositions of the sample. The generated trial sets are subsequently employed to obtain mean values for the amount of each constituent of the sample.