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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

Peptide Identification Using Tandem Mass Spectrometry

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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: Overview01:19

Mass Spectrometry: Overview

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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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Mass Spectrometers01:16

Mass Spectrometers

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This lesson details the instrumentation of a mass spectrometer—a physical instrument to perform mass spectrometry on analyte molecules and record the characteristic mass spectra. This is achieved via three chief functions:
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Data Processing and Analysis in Mass Spectrometry-Based Metabolomics.

Ángela Peralbo-Molina1, Pol Solà-Santos2,3,4, Alexandre Perera-Lluna2,3,4

  • 1IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain. angela.peralbo@imibic.org.

Methods in Molecular Biology (Clifton, N.J.)
|September 24, 2022
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Summary
This summary is machine-generated.

Metabolomics, the study of small molecules, faces data processing challenges. This chapter introduces tools enabling researchers to analyze complex metabolomic data reliably and efficiently, even without extensive bioinformatics skills.

Keywords:
Data processingLiquid chromatographyMass spectrometryUntargeted metabolomics

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Area of Science:

  • Metabolomics as a key component of the 'omics' sciences.
  • Focus on the analysis of small chemical compounds (<1500 Da) in biological systems.

Background:

  • Metabolic reactions are closely linked to cellular phenotype, making metabolomics valuable for research areas like personalized medicine.
  • Significant challenges exist in metabolomic data processing due to data complexity and a lack of comprehensive metabolite identification databases.
  • A shortage of bioinformatics expertise among technicians hinders reliable and rapid data analysis.

Purpose of the Study:

  • To present accessible tools for metabolomic data processing.
  • To empower researchers with limited bioinformatics experience to achieve dependable results.
  • To streamline the analysis process, minimizing extensive parameterization.

Main Methods:

  • Review and description of available bioinformatics tools for metabolomic data processing.
  • Focus on user-friendly interfaces and simplified workflows.
  • Emphasis on tools requiring minimal parameterization for ease of use.

Main Results:

  • Identification of specific software and platforms that simplify metabolomic data analysis.
  • Demonstration that inexperienced researchers can obtain reliable results using these tools.
  • Reduction in the time and expertise required for data processing.

Conclusions:

  • Accessible data processing tools are crucial for advancing metabolomics research.
  • Simplifying bioinformatics workflows can democratize the use of metabolomic data.
  • Empowering researchers to analyze their own data enhances the pace of discovery in personalized medicine and other fields.