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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass.  One common type of ionization, known as electrospray ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave...
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Related Experiment Video

Updated: Aug 31, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Addressing big data challenges in mass spectrometry-based metabolomics.

Jian Guo1, Huaxu Yu1, Shipei Xing1

  • 1Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada. thuan@chem.ubc.ca.

Chemical Communications (Cambridge, England)
|August 23, 2022
PubMed
Summary

Mass spectrometry-based metabolomics generates big data, requiring advanced bioinformatics tools for accurate analysis. This study presents novel computational solutions to address these challenges, making tools freely available.

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Area of Science:

  • Analytical Chemistry
  • Computer Science
  • Bioinformatics

Background:

  • Mass spectrometry (MS)-based untargeted metabolomics generates vast datasets, posing significant big data challenges.
  • Manual processing of gigabytes of metabolomics data is infeasible, necessitating automated solutions.
  • Interdisciplinary knowledge is crucial for developing effective bioinformatics tools for metabolomics.

Purpose of the Study:

  • To elaborate on the major big data challenges encountered in metabolomics.
  • To introduce novel bioinformatics solutions developed to address these challenges.
  • To provide freely accessible computational tools and source codes for the metabolomics community.

Main Methods:

  • Development of bioinformatics tools integrating analytical chemistry, computer science, and statistics.
  • Focus on addressing challenges in data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation.
  • Utilizing GitHub as a platform for sharing open-source tools and code.

Main Results:

  • Identification of key big data challenges in MS-based metabolomics.
  • Development and presentation of specific bioinformatics solutions for each challenge.
  • Successful creation of freely available software and source code.

Conclusions:

  • Effective management of big data in metabolomics requires integrated bioinformatics approaches.
  • The developed tools offer practical solutions for accurate and efficient processing of metabolomics data.
  • Open-source accessibility of tools and code promotes further research and application in the field.