Battelle Memorial Institute

Kristin H Jarman

Karen L Wahl

Don Simone Daly

Kevin K Anderson

Alan R Willse

Jon H Wahl

Kenneth D Jarman

Alejandro Heredia-Langner

William R Cannon

Peptides are identified from a list of candidates using collision-induced dissociation tandem mass spectrometry data. A probabilistic model for the occurrence of spectral peaks corresponding to frequently observed partial peptide fragment ions is applied. As part of the identification procedure, a probability score is produced that indicates the likelihood of any given candidate being the correct match. The statistical significance of the score is known without necessarily having reference to the actual identity of the peptide. In one form of the invention, a genetic algorithm is applied to candidate peptides using an objective function that takes into account the number of shifted peaks appearing in the candidate spectrum relative to the test spectrum.

Peptide identification

A method and apparatus using a spectral analysis technique are disclosed. In one form of the invention, probabilities are selected to characterize the presence (and in another form, also a quantification of a characteristic) of peaks in an indexed data set for samples that match a reference species, and other probabilities are selected for samples that do not match the reference species. An indexed data set is acquired for a sample, and a determination is made according to techniques exemplified herein as to whether the sample matches or does not match the reference species. When quantification of peak characteristics is undertaken, the model is appropriately expanded, and the analysis accounts for the characteristic model and data. Further techniques are provided to apply the methods and apparatuses to process control, cluster analysis, hypothesis testing, analysis of variance, and other procedures involving multiple comparisons of indexed data.

Model for spectral and chromatographic data

Embodiments of the present invention provide methods of identifying a feature in an indexed dataset. Such embodiments encompass selecting an initial subset of indices, the initial subset of indices being encompassed by an initial window-of-interest and comprising at least one beginning index and at least one ending index; computing an intensity weighted measure of dispersion for the subset of indices using a subset of responses corresponding to the subset of indices; and comparing the intensity weighted measure of dispersion to a dispersion critical value determined from an expected value of the intensity weighted measure of dispersion under a null hypothesis of no transient feature present. Embodiments of the present invention also encompass equipment configured to perform the methods of the present invention.

Identification of features in indexed data and equipment therefore

The present invention is a method of identifying features in indexed data, especially useful for distinguishing signal from noise in data provided as a plurality of ordered pairs. Each of the plurality of ordered pairs has an index and a response. The method has the steps of: (a) providing an index window having a first window end located on a first index and extending across a plurality of indices to a second window end; (b) selecting responses corresponding to the plurality of indices within the index window and computing a measure of dispersion of the responses; and (c) comparing the measure of dispersion to a dispersion critical value. Advantages of the present invention include minimizing signal to noise ratio, signal drift, varying baseline signal and combinations thereof.

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Richland, WA, United States of America

Kristin H Jarman