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Related Concept Videos

Sample Preparation for Analysis: Advanced Techniques01:08

Sample Preparation for Analysis: Advanced Techniques

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Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
Acid digestion with strong acids is commonly used to dissolve inorganic materials that are insoluble (do not dissolve) in water. This method can be useful for...
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Optimizing Chromatographic Separations01:15

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Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Optimization of metabolomic data processing using NOREVA.

Jianbo Fu1, Ying Zhang1, Yunxia Wang1

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.

Nature Protocols
|December 25, 2021
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Summary
This summary is machine-generated.

NOREVA is a novel protocol for metabolomic peak table processing. It optimizes data analysis by evaluating thousands of workflows against objective criteria for improved performance in complex studies.

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Data Science

Background:

  • Metabolomic experiments generate peak tables requiring essential processing.
  • Existing data processing methods lack standardization and are study-dependent.
  • Selecting optimal processing workflows for specific metabolomic studies is challenging.

Purpose of the Study:

  • To develop a robust protocol for optimizing metabolomic peak table processing.
  • To address the challenge of selecting appropriate processing workflows for diverse metabolomic studies.
  • To provide a flexible and accessible tool for enhancing metabolomic data analysis.

Main Methods:

  • Developed NOREVA, an out-of-the-box protocol for peak table processing.
  • Subjected peak tables to numerous processing workflows with combinatorially determined sequences.
  • Evaluated workflow performance using objective criteria and analyzed benchmarks.

Main Results:

  • NOREVA uniquely evaluates processing performance across multiple criteria.
  • The protocol optimizes data processing by scanning thousands of potential workflows.
  • NOREVA supports data processing for time-course and multiclass metabolomics.

Conclusions:

  • NOREVA offers a systematic approach to optimize metabolomic data processing.
  • The R package implementation ensures accessibility and data privacy.
  • This protocol enhances the reliability and efficiency of metabolomic data analysis.