Modern analytical methods allow simultaneous detection of hundreds of metabolites, generating
increasingly large and complex data sets. Analysis of metabolomics data is a multi-step process that
requires variety of bioinformatics and statistical tools. One of the biggest challenges in metabolomics is
how alterations in metabolite levels can be linked to specific biological processes that are disrupted
contributing to the development of disease, or reflecting the disease state. To address this challenge, we
propose to develop methods and build computational tools to help researchers interrogate their
metabolomics data and integrate them with other molecular phenotypes to build testable hypotheses and
derive biological knowledge that could help addressing this challenge.
Our team has extensive collaborative experience working together in the Phase I Common Fund-supported
Regional Comprehensive Metabolomics Resource Core (MRC2) and in building computational methods
and tools for the analysis of multi-dimensional `omics data. We propose to build on our past efforts to
develop a novel functional enrichment testing (FET) approach that will not be limited to compounds found
in canonical metabolic pathways and will include both known and unknown metabolites in the analysis. We
will leverage our previously developed methodology for building partial correlation networks that allows
identifying commonalities and differences in network structures derived from different experimental
Exploring relationships between key metabolic changes and alterations in transcript, proteins and other
molecular components can provide additional levels of information and help build biological insights from
experimental data. The overarching goal of this proposal is to develop FET methods that would enable
analysis of multi-condition, multi-layer `omics data sets. To that end, we propose a network-based data
integration strategy that will help uncover relationships both within and between different molecular layers,
identify subnetworks, containing metabolites, transcripts etc., and test their significance. We anticipate that
the application of our methods will lead to better insights into molecular networks affected by many
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