Abstract: A two-step alignment method for temporally aligning LC-MS data files representative of three-dimensional chromatographic surfaces is disclosed. The method includes a pre-alignment step whereby the data files are roughly aligned using a transformation-based correlation analysis of base peak information. The pre-aligned files are used as input to a full alignment step, in which a correlation matrix is computed from the full MS scan information, and an optimal path is traced through the resultant correlation matrix to identify corresponding scan numbers. The use of the pre-alignment step substantially reduces the computational expense of the full alignment step and improves the accuracy and reliability of the alignment.

Abstract: In one aspect of the present invention, the less “useful” spectral data is disregarded from the spectral data resulting from the fragmentation by ETD and candidate charge states for the “useful” data assigned. Knowledge of the first order ion product charge state reduces the subset of comparison data hence aiding in the eventual identification of the precursor ion, and thus aiding in peptide sequence database searching capabilities. Such capabilities include, but are not limited to, computational requirements for database search and data storage, CPU time, the volume taken up on the hard disk to store results, visualization and dissemination of data, and overall improvement in the confidence in the precursor identification. Thus determination of the peptide sequence can be resolved in less time, costing less money, and requiring less computer power.

Abstract: In one aspect of the present invention, the less “useful” spectral data is disregarded from the spectral data resulting from the fragmentation by ETD and candidate charge states for the “useful” data assigned. Knowledge of the first order ion product charge state reduces the subset of comparison data hence aiding in the eventual identification of the precursor ion, and thus aiding in peptide sequence database searching capabilities. Such capabilities include, but are not limited to, computational requirements for database search and data storage, CPU time, the volume taken up on the hard disk to store results, visualization and dissemination of data, and overall improvement in the confidence in the precursor identification. Thus determination of the peptide sequence can be resolved in less time, costing less money, and requiring less computer power.

Abstract: In one aspect of the present invention a new database search methodology is provided that provides a probability that spectral data from a non-ergodic reaction via mass spectrometry matches a candidate sequence from a set of sequences in a database by random. The methodology comprises two parts. The first part pre-processes the spectral data and retains only the most relevant data for the database search. The second part comprises searching a database using the pre-processed spectrum to assign a probability or expectation that the spectrum matches a candidate sequence from a set of sequences in a database by random. The search methodology uses a new probability model, a compound distribution based on the number of product ion mass-to-charge ratios and the number of intensity values that are shared between the product ion spectral data and the sequence database, to accurately predict the probability of the peptide identification being a correct match, and not a random event.

Abstract: In one aspect of the present invention a new database search methodology is provided that provides a probability that spectral data from a non-ergodic reaction via mass spectrometry matches a candidate sequence from a set of sequences in a database by random. The methodology comprises two parts. The first part pre-processes the spectral data and retains only the most relevant data for the database search. The second part comprises searching a database using the pre-processed spectrum to assign a probability or expectation that the spectrum matches a candidate sequence from a set of sequences in a database by random. The search methodology uses a new probability model, a compound distribution based on the number of product ion mass-to-charge ratios and the number of intensity values that are shared between the product ion spectral data and the sequence database, to accurately predict the probability of the peptide identification being a correct match, and not a random event.