Summary of project PR001685
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001685. The data can be accessed directly via it's Project DOI: 10.21228/M8WT6H This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Project ID: | PR001685 |
| Project DOI: | doi: 10.21228/M8WT6H |
| Project Title: | SAND: automated time-domain modeling of NMR spectra applied to metabolic quantification |
| Project Type: | NMR quantification of spike-in samples |
| Project Summary: | New developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of hundreds to thousands of biological samples in biomedical studies, with great potential in drug discovery and diagnostics. The exploitation of this rich information requires detailed quantification of spectral features. However, the development of a consistent and automatic workflow for NMR feature quantification has been a long-standing challenge because of the difficulties of extensive spectral overlap. To address this challenge, we introduce the software SAND (Spectral Automated NMR Deconvolution), for automated feature quantification in the time domain. SAND follows upon the previous success of time-domain modeling and provides automated quantification of entire spectra without the need for manual interaction. SAND employs subsampling, global optimization, and statistic model selection, which are readily expandable to higher dimensional NMR and non-uniform sampling applications. Here, we demonstrate the accuracy of the SAND approach (a correlation around 0.9) using highly overlapped simulated datasets, a two-compound mixture, and a urine spectral series spiked with differing amounts of a four-compound mixture. We further demonstrate automated annotation using correlation networks derived from SAND deconvoluted peaks, and on average 74% of peaks for each compound can be recovered in a single correlation network cluster. SAND is currently integrated with NMRbox and the Network for Advanced NMR (NAN). |
| Institute: | University of Georgia |
| Department: | Genetics; Biochemistry and Molecular Biology; Institute of Bioinformatics; College of Engineering; Complex Carbohydrate Research Center |
| Laboratory: | Arthur S. Edison and Frank Delaglio |
| Last Name: | Wu |
| First Name: | Yue |
| Address: | 3165 Porter Drive, Palo Alto, CA, 94304 |
| Email: | yuewu.mike@gmail.com |
| Phone: | 7062546619 |
| Funding Source: | NSF 1946970, NIH P41GM111135 (NIGMS) |
| Publications: | to be submitted soon |
| Contributors: | Yue Wu, Omid Sanati, Mario Uchimiya, Krish Krishnamurthy, Arthur S. Edison, Frank Delaglio |
Summary of all studies in project PR001685
| Study ID | Study Title | Species | Institute | Analysis(* : Contains Untargted data) | Release Date | Version | Samples | Download(* : Contains raw data) |
|---|---|---|---|---|---|---|---|---|
| ST002718 | SAND: automated time-domain modeling of NMR spectra applied to metabolic quantification | Homo sapiens | University of Georgia | NMR* | 2023-06-21 | 1 | 7 | Uploaded data (2.6M)* |
