Research collaboration seeks to improve data management, workflows in NMR spectroscopy

Posted: June 24, 2016

Developed in the 1940s and 1950s, nuclear magnetic resonance (NMR) spectroscopy measures physical and chemical properties of atoms or molecules by measuring change in the magnetic resonance of the nuclei of atoms. The process is used by scientists for a variety of applications, such as substance identification. In biomolecular science, NMR supports discovery and identification of new drugs, disease and metabolic research, study of structural biology, and more.

Advances in computational applications and data-sharing tools have opened new doors for use of information gleaned from NMR spectroscopy, but new challenges have emerged as well. To make possible its varied applications, myriad software tools are employed from a range of sources and using a variety of semantic approaches. This complicates data management, inhibiting dissemination and reproduction of important findings.

A research team based at the iSchool at Illinois, the University of Wisconsin (UW), and the University of Connecticut Health Center (UCONN Health) is working to solve this problem. Their work is executed through the newly-formed Center for Biomolecular NMR Data Processing and Analysis at UCONN Health with grant support from the National Institutes of Health.

Michael Gryk, iSchool doctoral student and research scientist, is codirecting the second of three technology research and development phases (TRD2) of this effort. In TRD2, Gryk and his colleagues will develop a set of tools to support data management, workflow analysis, and dissemination of NMR research.

Their work will take place within a platform called NMRbox, which was developed during the first phase of technology research and development. Data and workflow management capabilities will be expanded and integrated with the resources of the Biological Magnetic Resonance Data Bank, a repository for NMR spectroscopy data at UW.

With the extension of this data model and platform, scientists using NMR will be able to capture all data and metadata needed to reproduce or analyze the steps taken in a given NMR experiment. From these analyses scientists can optimize workflows by drawing on the most effective methods.

Gryk is no stranger to the science behind NMR spectroscopy. In addition to studying workflows and data processes at the iSchool, he is an associate professor in the Department of Molecular Biology and Biophysics at UCONN Health and holds degrees in biophysics and chemistry. Gryk was among a team of three researchers who conceptualized the precursor to NMRbox, called CONNJUR.

Filed Under: Data Curation, Design and Evaluation of Information Systems and Services, Science Processes, student news