Skip to main content

Semi-Automated NMR Metabolite Identification via Machine Learning


EMSL Project ID
60468

Abstract

Current EMSL metabolomics capabilities enable automated Nuclear Magnetic Resonance (NMR) data acquisition, yet downstream processing of these data is manually intensive and time-consuming. The largest bottlenecks among these downstream processes are feature (metabolite) identification and quantification; these two steps typically require an experienced analyst several hours to complete for a single sample. An open-source R package, nmRanalysis, and user interface (UI) are currently under development (EMSL Proposal 60117) for semi-automated metabolite quantification. We propose to further develop the R package and UI to streamline and increase the efficiency of metabolite identification for NMR data, thereby addressing the two main bottlenecks in NMR metabolomics analyses and expanding the capabilities of nmRanalysis.

Project Details

Start Date
2022-10-01
End Date
2024-01-31
Status
Closed

Team

Principal Investigator

Javier Flores
Institution
Pacific Northwest National Laboratory

Co-Investigator(s)

Lisa Bramer
Institution
Pacific Northwest National Laboratory

Team Members

Natalie Winans
Institution
Pacific Northwest National Laboratory

Anastasiya Prymolenna
Institution
Pacific Northwest National Laboratory

William Kew
Institution
Environmental Molecular Sciences Laboratory

Robert Young
Institution
Environmental Molecular Sciences Laboratory