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SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification.
Wu, Yue; Sanati, Omid; Uchimiya, Mario; Krishnamurthy, Krish; Wedell, Jonathan; Hoch, Jeffrey C; Edison, Arthur S; Delaglio, Frank.
Afiliação
  • Wu Y; Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States.
  • Sanati O; Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, United States.
  • Uchimiya M; School of Electrical and Computer Engineering, University of Georgia, Athens, Georgia 30602, United States.
  • Krishnamurthy K; Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, United States.
  • Wedell J; Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, United States.
  • Hoch JC; Chempacker LLC, San Jose, California 95135, United States.
  • Edison AS; National Magnetic Resonance Facility, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
  • Delaglio F; Department of Molecular Biology and Biophysics, University of Connecticut, Farmington, Connecticut 06030-3305, United States.
Anal Chem ; 96(5): 1843-1851, 2024 02 06.
Article em En | MEDLINE | ID: mdl-38273718
ABSTRACT
Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Imageamento por Ressonância Magnética Idioma: En Revista: Anal Chem Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Imageamento por Ressonância Magnética Idioma: En Revista: Anal Chem Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos