SBIR Phase I: Pipeline for Analysis of Metabolomics Data

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The broader impact of this Small Business Innovation Research (SBIR) Phase I project is enhancing the role of metabolite (substances used by cells for growth, reproduction and health) analysis in the growing area of systems biology research. This is expected to lead to faster and less expensive biomarker and drug discovery to allow for more accurate, reproducible, and faster clinical trials, and to accelerate basic scientific research into many areas of cellular and system-wide organismal studies. This will have a significant impact on the bottom line for drug companies and for improving health and reducing health care cost. The innovation will provide customers with a platform and expertise that enable them to increase their ability to develop biomarkers and drugs faster by: (1) allowing more metabolites to be involved in the discovery of new relationships between diseases and metabolites, potentially opening up new areas of basic research; (2) selecting disease-associated metabolites on the basis of not only statistically significant changes in metabolite levels but also correlations of interactions among metabolites in diseased vs. healthy cells; and (3) evaluating the relationships between metabolites and diseases through integration of metabolite analysis with other system-wide analytical methods (i.e. gene expression, protein levels, etc.).The proposed project seeks to develop an innovative cloud-based platform with an interactive modular interface that allows users to easily build customized pipelines for analysis of untargeted metabolomics data. The platform will empower the opportunity to increase the number of annotated analytes and to integrate metabolomics with other omics data, thereby enhancing the involvement of metabolomics in systems biology-based biomarker and drug discovery studies. Despite a large accumulation of metabolomics data acquired over the past several years, effective use of these data for biomarker and drug discovery has been very limited. These challenges are in part due to the lack of effective tools that: (1) accurately determine the identity of disease-associated analytes; (2) help investigate the rewiring and conserved interactions among metabolites in the progression of disease; and (3) integrate multi-omics data to evaluate the relationship between metabolites and diseases at the systems level. This project will advance scientific knowledge by investigating and evaluating innovative computational methods for metabolite annotation, differential analysis of metabolite profiles, and multi-omics data integration. Furthermore, the project will lead to a cloud-based platform that enables users to build their desired data analysis workflow or pipeline by choosing from several innovative modules to analyze metabolomics data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.