Abstract: The present teachings provide for identification of peptides using small sequence tags to focus computational resources on searching regions of a protein database that are the most likely to yield correct identifications. They allow for the incorporation of modifications and in doing so focuses the search to peptides with a precursor mass match. Additionally, probability or relevance factors can be used to determine peptide hypotheses. Various embodiments are presented that search for peptides when a single precursor is selected or when multiple precursors are simultaneously fragmented.

Abstract: Singular spectrum analysis is used to detect a feature from mass spectrometry data. A plurality of scans of a sample is performed producing mass spectrometry data using a spectrometer. A singular spectrum analysis is performed on the mass spectrometry data using a fixed window width in which one or more components other than the highest ranked component are grouped in a set and the one or more components grouped in the set are summed producing reconstructed data using the processor. A feature of the mass spectrometry data is detected by analyzing an aspect of the reconstructed data using the processor. Analyzing an aspect of the reconstructed data includes using pairs of zero crossings in the reconstructed data to detect bounds on a location of the feature in the mass spectrometry data.

Abstract: Computer-implementable methods consistent with the present teachings can be used in the design of selective- and multiple-reaction monitoring experiments for the identification of proteins and peptides of interest. By determining candidate parent-daughter ion transition pairs and scoring the pairs based on their ability to be detected experimental design time can be reduced and with a more focused list of transition pairs more experiments can be run successfully in a given period of time.