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Related Concept Videos

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called collision-induced...
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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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The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
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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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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 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.
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Updated: Sep 3, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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TidyMass an object-oriented reproducible analysis framework for LC-MS data.

Xiaotao Shen1, Hong Yan2, Chuchu Wang3

  • 1Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.

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|July 28, 2022
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Summary
This summary is machine-generated.

The tidyMass R package offers a reproducible framework for metabolomics data analysis, addressing long-standing issues in data processing and workflow traceability for LC-MS untargeted metabolomics.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Metabolomics

Background:

  • Reproducibility, traceability, and transparency remain significant challenges in metabolomics data analysis.
  • Existing computational tools often have limitations in addressing these issues comprehensively.

Purpose of the Study:

  • To introduce tidyMass, an R-based computational framework designed to meet the needs for traceable, shareable, and reproducible workflows in LC-MS-based untargeted metabolomics.
  • To provide a flexible and extensible ecosystem of R packages for robust metabolomics data processing and analysis.

Main Methods:

  • Development of tidyMass as an ecosystem of R packages.
  • Implementation of a shared design philosophy, grammar, and data structure across packages.
  • Creation of a modular architecture for enhanced flexibility and extensibility.

Main Results:

  • tidyMass provides a comprehensive, reproducible, and object-oriented computational framework.
  • The modular design allows for customization and integration with other tools.
  • The framework facilitates traceable and shareable metabolomics data analysis workflows.

Conclusions:

  • tidyMass offers a robust solution to enhance reproducibility and transparency in metabolomics data analysis.
  • The R-based framework empowers researchers to build customized and reliable data processing pipelines.
  • tidyMass contributes to advancing the field of untargeted metabolomics through improved data analysis practices.