A research team led by scientists at the University of California, Riverside, has developed a computational workflow for analyzing large data sets in the field of metabolomics, the study of small molecules found within cells, biofluids, tissues, and entire ecosystems.

Most recently, the team applied this new computational tool to analyze pollutants in seawater in Southern California. The team swiftly captured the chemical profiles of coastal environments and highlighted potential sources of pollution.

"We are interested in understanding how such pollutants get introduced in the ecosystem," said Daniel Petras, an assistant professor of biochemistry at UC Riverside, who led the research team. "Figuring out which molecules in the ocean are important for environmental health is not straightforward because of the ocean's sheer chemical diversity. The protocol we developed greatly speeds up this process. More efficient sorting of the data means we can understand problems related to ocean pollution faster."

Petras and his colleagues report in the journal Nature Protocols that their protocol is designed not only for experienced researchers but also for educational purposes, making it an ideal resource for students and early-career scientists. This computational workflow is accompanied by an accessible web application with a graphical user interface that makes metabolomics data analysis accessible for non-experts and enables them to gain statistical insights into their data within minutes.

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