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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Related Experiment Video

Updated: Oct 12, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Personal Metabolomics: A Global Challenge.

Petr G Lokhov1, Oxana P Trifonova1, Dmitry L Maslov1

  • 1Analytical Branch, Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia.

Metabolites
|November 25, 2021
PubMed
Summary

Personalized metabolomics offers accurate disease signatures but faces clinical adoption hurdles. Data analysis complexity and incompatibility hinder its translation into practical clinical tests.

Keywords:
challengesdisease signaturelaboratory-developed testmass spectrometrymetabolomicsp-valuepersonal analysis

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Area of Science:

  • Clinical metabolomics
  • Personalized omics testing
  • Biomarker discovery

Background:

  • Metabolomics provides highly informative signatures for numerous diseases.
  • Despite analytical potential, personalized metabolomics is not yet integrated into clinical practice.
  • Challenges exist in translating broad metabolomic data into individual diagnostic tools.

Purpose of the Study:

  • To analyze the reasons behind the lack of personalized metabolomics in clinical settings.
  • To identify the complexities hindering the clinical application of metabolomic data.
  • To explore strategies for overcoming current limitations.

Main Methods:

  • Analysis of data complexity in personal metabolic profiling.
  • Assessment of compatibility issues with standard metabolomic data processing.
  • Review of existing metabolomic databases and their translation into clinical tests.

Main Results:

  • The complexity of analyzing personal metabolic data is a significant barrier.
  • Current data treatment methods in metabolomics are incompatible with personalized testing.
  • Metabolic signatures in databases cannot be directly translated into reliable personal tests.

Conclusions:

  • The translation of metabolomic signatures into clinical practice requires novel data analysis approaches.
  • Addressing data complexity and compatibility is crucial for realizing personalized metabolomics.
  • New strategies are needed to integrate metabolomics into medical laboratory practice for personalized diagnostics.