Thomas Head
Research Subjects
Thomas Head has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
- Arthur S. Edison
Complex Carbohydrate Research Center, University of Georgia
1 shared publication - Candace C. Fleischer
Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta Georgia USA
1 shared publication Chaevien S. Clendinen
Metabolomics Association of North America (MANA), NMR Special Interest Group, Edmonton, Canada
1 shared publication- Daniel Raftery
University of Washington
1 shared publication - Goncalo Jorge Gouveia
Boyce Thompson Institute, Cornell University, Ithaca, NY 14853, USA
1 shared publication - Hamid R. Eghbalnia
Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA
1 shared publication
Latest Publications
- 13 March 2024
- Published by Springer Nature in Metabolomics
- Vol. 20 (2) , 1-11
Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging
Background The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. Aim of review The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. Key scientific concepts of review We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.
- 13 March 2024
- Published by Springer Nature in Metabolomics
- Vol. 20 (2) , 1-11
Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging
Background The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. Aim of review The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. Key scientific concepts of review We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.