A research team has created a computational workflow specifically for handling large data sets within the field of metabolomics, which involves studying small molecules present in cells, biofluids, tissues, and ecosystems.
At the University of California, Riverside, a group of scientists led by assistant professor Daniel Petras has introduced a computational workflow designed for the analysis of extensive data sets in metabolomics, which is the exploration of small molecules within various biological contexts.
Recently, the team utilized this innovative computational tool to investigate pollutants in seawater along the Southern California coast. This tool allowed the researchers to promptly gather the chemical profiles of coastal areas and identify possible pollutant sources.
“We want to understand how these pollutants enter the ecosystem,” explained Petras. “Determining the significant molecules in ocean health is challenging due to the ocean’s vast chemical complexity. The workflow we created accelerates this process, allowing us to address ocean pollution issues more rapidly through improved data organization.”
Petras and his team have published their findings in the journal Nature Protocols, emphasizing that their workflow is useful not only for seasoned professionals but also for educational settings, making it an excellent resource for students and those starting their scientific careers. The computational approach is supplemented by a user-friendly web application that makes metabolomics data analysis accessible to those with limited experience, enabling quick statistical data insights.
“This tool caters to a wide audience of researchers, from complete novices to seasoned professionals, and is specifically designed to work with our molecular networking software,” added coauthor Mingxun Wang, an assistant professor of computer science and engineering at UCR. “For newcomers, the guidelines and code we provide simplify understanding common data processing and analysis tasks. For experts, it streamlines reproducible data analysis, allowing the sharing of workflows and results more effectively.”
Petras highlighted the distinctiveness of their research paper, which acts as a comprehensive educational resource organized by a virtual research group termed Virtual Multiomics Lab (VMOL). This open-access community involves over 50 scientists from around the globe and aims to make chemical analysis easy to access for researchers irrespective of their backgrounds or resources.
“I feel immense pride in how this initiative has grown to have a significant impact, engaging experts and students from around the world,” remarked Abzer Pakkir Shah, a Ph.D. student in Petras’ group and the paper’s lead author. “By eliminating physical and economic obstacles, VMOL offers training in computational mass spectrometry and data science while aiming to foster virtual research projects as a new collaborative research modality.”
All of the software developed by the team is freely available to the public. This software development began during a summer school focused on non-targeted metabolomics in 2022, held at the University of Tübingen, where they also established VMOL.
Petras believes that the protocol will be particularly beneficial for environmental researchers and those in biomedical fields, including researchers conducting clinical microbiome studies.
“Our protocol’s versatility spans many areas and sample types, such as combinatorial chemistry, doping analysis, and the detection of trace contaminants in food, pharmaceuticals, and other industrial products,” he stated.
Petras obtained his master’s degree in biotechnology from the University of Applied Sciences Darmstadt and earned his doctorate in biochemistry from the Technical University of Berlin. He conducted postdoctoral research at UC San Diego, focusing on large-scale environmental metabolomics methods. In 2021, he established the Functional Metabolomics Lab at the University of Tübingen and joined UCR in January 2024, where his lab concentrates on developing and utilizing mass spectrometry techniques to visualize and assess chemical interactions within microbial communities.
