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Related Concept Videos

Spin Saturation Transfer Difference NMR (SSTD NMR): A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes11:44

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Related Experiment Video

Updated: Jan 20, 2026

Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes
11:44

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Processing and Analysis of Untargeted Multicohort NMR Data.

Timothy M D Ebbels1, Ibrahim Karaman2,3, Gonçalo Graça4

  • 1Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College, London, UK. t.ebbels@imperial.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)
|August 30, 2019
PubMed
Summary

Large-scale metabolomics using Nuclear Magnetic Resonance (NMR) data presents processing challenges. Advanced algorithms can overcome these issues, improving data quality for further analysis.

Keywords:
Data analysisData processingMetabolome-wide significance level (MWSL)MulticohortNMRSubset optimization by reference matching (STORM)

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Related Experiment Videos

Last Updated: Jan 20, 2026

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Bioinformatics

Background:

  • Large-scale metabolomics studies increasingly utilize Nuclear Magnetic Resonance (NMR) data.
  • Combining multiple cohorts introduces significant data processing and analysis challenges due to diverse sample properties and large data volumes.

Purpose of the Study:

  • To address the specific challenges encountered in processing and analyzing large-scale, multi-cohort NMR metabolomics data.
  • To highlight the necessity of advanced algorithms for effective data handling in complex metabolomics studies.

Main Methods:

  • Development and application of specialized algorithms for data alignment and normalization.
  • Implementation of robust techniques for outlier detection and removal in high-dimensional NMR datasets.
  • Strategies for managing strong correlations and identifying unknown compounds within large metabolomics datasets.

Main Results:

  • Successfully addressed key data processing challenges including alignment, normalization, and outlier management.
  • Enabled the identification and handling of strong correlations and unknown metabolites.
  • Produced enhanced metabolomics datasets suitable for advanced data mining and interpretation.

Conclusions:

  • Suitable algorithms and techniques are crucial for overcoming the complexities of large-scale, multi-cohort NMR metabolomics data.
  • Effective data processing leads to higher quality datasets, facilitating deeper insights and further data mining.
  • The presented approaches enhance the utility of NMR metabolomics for biological and biomedical research.