Rapid Metabolite Identification using Advanced Algorithms for Mass Spectral Interpretation
Abstract
Recognizing differences between related LC/MS data sets is the basic premise for the determination of potential metabolites in drug development. Finding small differences between two or more datasets requires a deep and rigorous analysis of each data set to extract and determine those m/z values that give rise to chromatographic peaks. The major challenge is that signal-to-noise ratio decreases as the limit of detection is approached so the number of false positive peaks that populate the output increases exponentially. In searching for relevant and important potential metabolites it is critical that as many false positive potential metabolite features can be eliminated from the output to reduce the human investment of time and energy in reviewing the results. Using a unique algorithm for componentization of LC/MS data, we have developed software that uses extracted ion chromatogram peaks and as many mass spectral identifiers as possible to ensure that all features relevant to a chemical species are identified. Using the multiple information redundancy approach to data extraction, we determined with good accuracy the important and relevant components within a series of data sets.
Recognizing differences between related LC/MS data sets is the basic premise for the determination of potential metabolites in drug development. Finding small differences between two or more datasets requires a deep and rigorous analysis of each data set to extract and determine those m/z values that give rise to chromatographic peaks. The major challenge is that signal-to-noise ratio decreases as the limit of detection is approached so the number of false positive peaks that populate the output increases exponentially. In searching for relevant and important potential metabolites it is critical that as many false positive potential metabolite features can be eliminated from the output to reduce the human investment of time and energy in reviewing the results. Using a unique algorithm for componentization of LC/MS data, we have developed software that uses extracted ion chromatogram peaks and as many mass spectral identifiers as possible to ensure that all features relevant to a chemical species are identified. Using the multiple information redundancy approach to data extraction, we determined with good accuracy the important and relevant components within a series of data sets.
