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Related Experiment Video

Updated: Mar 2, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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NOREVA: normalization and evaluation of MS-based metabolomics data.

Bo Li1, Jing Tang1, Qingxia Yang1,2

  • 1Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 401331, China.

Nucleic Acids Research
|May 20, 2017
PubMed
Summary
This summary is machine-generated.

Unwanted variations in mass spectrometry metabolomics data hinder accurate metabolic profiling. NOREVA is a new tool that evaluates multiple normalization methods using several criteria for better data accuracy.

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Analytical Chemistry

Background:

  • Mass spectrometry-based metabolomics is crucial for biological research.
  • Unwanted signal variations in metabolomics data can compromise accurate metabolic profiling.
  • Existing normalization methods have variable performance dependent on data characteristics.

Purpose of the Study:

  • To develop a comprehensive tool, NOREVA, for evaluating the performance of various data normalization methods in metabolomics.
  • To address the limitations of single-criterion assessments by integrating multiple evaluation perspectives.

Main Methods:

  • Developed NOREVA, a novel computational tool for assessing normalization method performance.
  • Integrated five distinct, well-established criteria for multi-perspective evaluation.
  • Incorporated unique features for removing unwanted variations using quality control (QC) metabolites and sequential QC sample-based correction followed by data normalization.

Main Results:

  • NOREVA provides a comprehensive evaluation of normalization methods from multiple viewpoints.
  • The tool demonstrated superior performance in identifying effective normalization strategies across five benchmark datasets.
  • Validated the reliability and originality of NOREVA's algorithms through extensive case studies.

Conclusions:

  • NOREVA enables robust identification of optimal normalization methods for mass spectrometry-based metabolomics data.
  • The tool offers an indispensable complement to existing data analysis workflows.
  • NOREVA is freely accessible, promoting wider adoption and improved metabolomics data quality.