The mummichog software was initially published in 2013, as a computational approach to match patterns in metabolomics data to known biochemical networks, without the requirement of upfront metabolite identification. This approach enables rapid generation of biological hypotheses from untargeted data, and has gained considerable popularity, which also creates urgent needs to upgrade the software itself. This proposal aims to add a rich user interface, and better support of LC-MS, LC- MS/MS, IMS/MS and GC-MS. Furthermore, this work will make a conceptual leap to establish a framework of network alignment as a vehicle to interpret metabolomics data by integrating multiple layers of information. The new development will be integrated into XCMS Online and MetaboAnalyst, and will be made freely available as modular software tools.
Metabolomics is viewed as a key technology to support deep phenotyping in patients and in experimental systems, which will fill an important gap between genomics and precision medicine. There is tremendous potential of using metabolomics to identify diagnostic markers, to understand disease pathobiology and to design new therapeutic interventions. A key challenge in this field is how to interpret high-throughput metabolomics data quickly and reliably in the biological context. This project of continued development of the mummichog software will provide an important solution. In turn, it will accelerate the translational impacts of metabolomics to biomedicine and human health.
