Clinical metabolomics using stable isotope resolved metabolomics (SIRM) for pathway tracing represents an important new approach to obtaining metabolic parameters in human cancer subjects in situ. Metabolic data generated by SIRM study are often enriched with isotopolgues. Identification of isotopologues is the key to interpret the metabolic pathways involved in a SIRM study but remains to be the major bottleneck in its data processing pipeline. The high volume of data produced by mass spec instruments requires computational approaches for automated assignment of the spectra and to analyze the data in an accurate, meaningful, and timely fashion. The application tackles this challenges by proposing new algorithms to support accurate and scalable identification of isotopolgues from mass-spectrum data.
Three aims of the proposal are : (1) A new algorithm for accurate and scalable identification of isotopologues from a single mass-spec dataset; (2) a robust alignment algorithm that leverages the recurring isotopologue relationships to in multiple replicates to align them; (3) a rigorous validation approach to test and evaluate the proposed algorithms in both its sensitivity and scalability.
This application proposes to develop a novel set of algorithms to improve the analysis of data generated by ultra-high resolution mass spectra data generated by SIRM studies. The success of this project will bridge the gap in bioinformatics tools to support the elucidation of the molecular pathways or biomarkers.
