New data science tool dramatically accelerates molecular analysis of our environment

A research team led by scientists at the University of California, Riverside, has developed a computational workflow to analyze large datasets in the field of metabolomics, the study of small molecules present in cells, biofluids, tissues and entire ecosystems.

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

“We want to understand how these pollutants enter the ecosystem,” says Daniel Petras, an assistant professor of biochemistry at the University of California, Riverside, who led the research team. “Determining which molecules in the ocean are important for environmental health is not easy because of the ocean’s great chemical diversity. The protocol we developed greatly speeds up this process. More efficient sorting of data means we can understand ocean pollution problems more quickly.”

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 to non-experts and allows them to obtain statistical insights from their data in minutes.

“This tool is accessible to a wide range of researchers, from beginners to experts, and is designed to be used in conjunction with the molecular network software that my group is developing,” said Mingxun Wang, co-author and assistant professor of computer science and engineering at UCR. “For beginners, the guidelines and code we provide make it easier to understand common data processing and analysis steps. For experts, it accelerates reproducible data analysis, allowing them to share their workflows and statistical data analysis results.”

Petras explained that the research paper is unique, serving as a large-scale educational resource organized by a virtual research group called the Virtual Multiomics Lab, or VMOL. With more than 50 participating scientists from around the world, VMOL is an open-access community. Its goal is to simplify and democratize the process of chemical analysis, making it accessible to researchers around the world, regardless of their background or resources.

“I am extremely proud to see how this project has evolved into something impactful, involving experts and students from all over the world,” said Abzer Pakkir Shah, a PhD student in Petras’ group and first author of the paper. “By removing physical and economic barriers, VMOL provides training in computational mass spectrometry and data science and aims to launch virtual research projects as a new form of collaborative science.”

All software developed by the team is free and publicly available. The software development was initiated at a summer school on untargeted metabolomics in 2022 at the University of Tübingen, where the team also launched VMOL.

Petras hopes the protocol will be particularly useful to environmental researchers as well as scientists working in the biomedical field and researchers conducting clinical studies of microbiome science.

“The versatility of our protocol extends to a wide range of fields and sample types, including combinatorial chemistry, doping analysis and trace contamination of foods, pharmaceuticals and other industrial products,” he said.

Petras received his master’s degree in biotechnology from the University of Applied Sciences Darmstadt and his PhD in biochemistry from the Technical University of Berlin. He completed postdoctoral research at UC San Diego, where he focused on developing methods for large-scale environmental metabolomics. In 2021, he launched the Functional Metabolomics Lab at the University of Tübingen. In January 2024, he joined UCR, where his lab focuses on developing and applying mass spectrometry-based methods to visualize and assess chemical exchanges within microbial communities.