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Related Concept Videos

Overview of Metabolism01:40

Overview of Metabolism

Living cells constantly carry out various chemical reactions which are necessary for their proper functioning. These reactions are interlinked to one another via multiple pathways. The collection of these chemical reactions is known as metabolism.
Plant Metabolism
Sunlight, the primary source of energy in plants, is first absorbed by the chlorophyll pigments present in their leaves. Plants then use this energy to carry out photosynthesis, where water is oxidized into oxygen and carbon dioxide...
Metabolic Rate01:25

Metabolic Rate

The human body is a powerhouse of energy, with every cell performing numerous functions that require energy. This energy production and consumption is measured by the metabolic rate, which quantifies the total heat generated by all the body's chemical reactions and mechanical work. This measurement helps to determine the rate of kilocalorie (kcal) consumption needed to fuel all ongoing activities.
The Basal Metabolic Rate (BMR) measures the energy expended at rest.
Several factors influence the...
Analysis of Population Pharmacokinetic Data01:12

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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Noncompartmental Analysis: Miscellaneous Pharmacokinetic Parameters00:54

Noncompartmental Analysis: Miscellaneous Pharmacokinetic Parameters

The noncompartmental approach is a widely used method in pharmacokinetics to assess drugs' behaviors in the body. It considers several factors, including clearance, bioavailability, and total volume of distribution.
One key aspect of the noncompartmental approach is determining a drug's total clearance. This can be done by dividing the drug dose by the area under the concentration-time curve from zero to infinity. The area under the concentration-time curve represents the drug's overall...

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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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An Open-Source Platform for Reference Data-Driven Analysis of Untargeted Metabolomics.

Alejandro Mendoza Cantu1, Julia M Gauglitz1, Wout Bittremieux1

  • 1Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium.

Journal of the American Society for Mass Spectrometry
|February 17, 2026
PubMed
Summary
This summary is machine-generated.

Reference data-driven (RDD) metabolomics identifies dietary patterns from unannotated spectra. This new platform makes RDD analysis accessible, enabling deeper biological insights from complex metabolomics data.

Keywords:
dietary read-outreference data-driven analysissoftware packageuntargeted metabolomicsweb platform

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Computational Biology

Background:

  • Untargeted tandem mass spectrometry (MS/MS) metabolomics offers broad small molecule characterization but often results in unannotated spectra, hindering biological interpretation.
  • Reference data-driven (RDD) metabolomics provides a method to contextualize spectra by comparing them to curated reference datasets, enabling inference of spectral origins without requiring exact structural identification.

Purpose of the Study:

  • To present an open-source RDD metabolomics platform, including a web application and Python package, for analyzing metabolomics data.
  • To facilitate the adoption of RDD analysis within the metabolomics community by removing technical barriers.

Main Methods:

  • Developed an open-source RDD metabolomics platform integrating a web application and a Python package.
  • The platform performs RDD analyses directly from molecular networking outputs generated by the Global Natural Products Social Molecular Networking (GNPS) platform.
  • Integrated tools for visualization and statistical analysis of RDD results, including interactive plots, heat maps, principal component analysis, and Sankey diagrams.

Main Results:

  • Demonstrated the platform's utility by analyzing stool metabolomics data from omnivore and vegan participants using a hierarchical reference dataset of 3500 food items.
  • The RDD analysis successfully revealed clear separation between dietary groups, highlighting the ability to extract biologically meaningful patterns from unannotated spectra.
  • The platform effectively derives dietary patterns from metabolomics data.

Conclusions:

  • The presented RDD metabolomics platform significantly lowers the technical barriers for researchers to implement RDD analysis.
  • This approach enables the extraction of biologically meaningful patterns from complex, otherwise unannotated metabolomics data.
  • The freely available tools empower the metabolomics community to gain deeper biological insights from their experiments.