New data science tool greatly speeds up molecular analysis of our environment
UC Riverside-led team developed the tool through an international virtual research group
University of California - Riverside
image:
Photo shows Daniel Petras.
view moreCredit: Petras lab, UC Riverside.
RIVERSIDE, Calif. -- 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.
“This tool is accessible to a broad range of researchers, from absolute beginners to experts, and is tailored for use in conjunction with the molecular networking software my group is developing,” said coauthor Mingxun Wang, an 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, enabling them to share their statistical data analysis workflows and results.”
Petras explained the research paper is unique, serving as a large educational resource organized through a virtual research group called Virtual Multiomics Lab, or VMOL. With more than 50 scientists participating from around the world, VMOL is a community-driven, open-access community. It aims to simplify and democratize the chemical analysis process, making it accessible to researchers worldwide, regardless of their background or resources.
“I’m incredibly proud to see how this project evolved into something impactful, involving experts and students from across the globe,” said Abzer Pakkir Shah, a doctoral student in Petras’ group and the 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 the team developed is free and publicly available. The software development was initiated during a summer school for non-targeted metabolomics in 2022 at the University of Tübingen, where the team also launched VMOL.
Petras expects the protocol will be especially useful to environmental researchers as well as scientists working in the biomedical field and researchers doing clinical studies in 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 food, pharmaceuticals, and other industrial products,” he said.
Petras received his master’s degree in biotechnology from the University of Applied Science Darmstadt and his doctoral degree in biochemistry from the Technical University Berlin. He did postdoctoral research at UC San Diego, where he focused on the development of large-scale environmental metabolomics methods. In 2021, he launched the Functional Metabolomics Lab at the University of Tübingen. In January 2024 he joined UCR, where his lab focuses on the development and application of mass spectrometry-based methods to visualize and assess chemical exchange within microbial communities.
The title of the paper is “Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.”
The University of California, Riverside is a doctoral research university, a living laboratory for groundbreaking exploration of issues critical to Inland Southern California, the state and communities around the world. Reflecting California's diverse culture, UCR's enrollment is more than 26,000 students. The campus opened a medical school in 2013 and has reached the heart of the Coachella Valley by way of the UCR Palm Desert Center. The campus has an annual impact of more than $2.7 billion on the U.S. economy. To learn more, visit www.ucr.edu.
Journal
Nature Protocols
Method of Research
Data/statistical analysis
Subject of Research
Not applicable
Article Title
Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data
Article Publication Date
20-Sep-2024
COI Statement
J.J.J.v.d.H. is currently a member of the Scientific Advisory Board of Naicons Srl., Milano, Italy, and is consulting for Corteva Agriscience. P.C.D. is a scientific advisor and holds equity to Cybele and a cofounder, advisor, and holds equity in Ometa, Arome and Enveda with prior approval by UC-San Diego and consulted in 2023 for DSM animal health. M.W. is the founder of Ometa Labs. S.H., T.P. and R.S. are cofounders of mzio GmbH.
New tool to help decision makers navigate possible futures of the Colorado River
Penn State
UNIVERSITY PARK, Pa. — The Colorado River is a vital source of water in the Western United States, providing drinking water for homes and irrigation for farms in seven states, but the basin is under increasing pressure from climate change and drought. A new computational tool developed by a research team, led by Penn State scientists, may help the region adapt to a complex and uncertain future.
Their tool, the Framework for Narrative Storylines and Impact Classification (FRNSIC), can help decision-makers explore many plausible futures and identify consequential scenario storylines — or descriptions of what critical futures might look like — to help planners better address the uncertainties and impacts presented by climate change. They reported their findings Sept. 19 in the journal Earth’s Future.
“One of the ways states like Colorado are preparing for the future is by making plans for how things might evolve based on the available science and inputs from various stakeholders,” said Antonia Hadjimichael, assistant professor in the Department of Geosciences at Penn State and lead author of the study. “This scenario planning process recognizes that planning for the future comes with many uncertainties about climate and water needs. So, planners have to consider different possibilities, such as a high-warming or a low-warming scenario.”
Hadjimichael said that both the scientific community and decision makers around the world often turn to scenarios to describe what conditions may look like in the future, but this approach may regard only a few possibilities and discount other alternatives.
These scenario planning approaches often feature a relatively small number of scenarios — for example what drought conditions might look like under different levels of warming — and may fail to capture the complexity of all the factors involved.
Alternatively, scientists use a technique called exploratory modeling, where models simulate thousands to millions of possible futures to discover which are consequential. But this approach is often not practical for use by decision makers, the scientists said.
“We wanted to provide something in the middle,” Hadjimichael said. “We wanted to create something that bridges the two — that considers the complexities but also boils it down to something that’s a little more actionable and a little less daunting.”
Their tool, FRNSIC, uses exploratory modeling first to investigate a large number of hypothesized plausible future conditions. It then uses that data to classify and identify relevant and locally meaningful storylines, the scientists said.
“Our approach essentially explores plausible future impacts and then says, ‘for this stakeholder, this is the storyline that would matter the most — and then for this other stakeholder, there is a different storyline they should be worried about,” Hadjimichael said. “It’s adding a little bit more pluralism and a little bit more nuance into how planning scenarios are established.”
In the Colorado River basin, decision makers face a complex set of factors, including how to supply enough water for growing populations and farmers while ensuring their state is not using more than their allowed share of the river’s flow, Hadjimichael said.
“The problem is there is not a single criterion that captures everybody and what they care about,” she said. “Maybe you have a very large farm, and maybe I have a very small farm. And maybe we grow different things. It’s hard to use a single factor to find out scenarios that would make us all happy, or make us all unhappy.”
The storylines produced by FRNSIC can be used in future work in the Colorado River basin — for example, how drought events are impacted when populations adapt and make changes.
“This allows policymakers to explore different states the world and helps review how different interventions might affect the basin under each storyline,” Hadjimichael said. “These drought scenarios can be used to illuminate potential consequences, and therefore be used in negotiations or when asking stakeholders for their input.”
Also contributing were Patrick Reed, professor at Cornell University; Julianne Quinn, assistant professor at the University of Virginia; and Chris Vernon, geospatial scientist, and Travis Thurber, software engineer, at Pacific Northwest National Laboratory
The U.S. Department of Energy, Office of Science, as part of research in MultiSector Dynamics, in the Earth and Environmental System Modeling Program supported this research.
Journal
Earth's Future
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
Scenario Storyline Discovery for Planning in Multi-Actor Human-Natural Systems Confronting Change
Article Publication Date
19-Sep-2024
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