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Related Concept Videos

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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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.
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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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Related Experiment Video

Updated: Nov 2, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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MS2Compound: A User-Friendly Compound Identification Tool for LC-MS/MS-Based Metabolomics Data.

Santosh Kumar Behera1, Sandeep Kasaragod1, Gayathree Karthikkeyan1

  • 1Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India.

Omics : a Journal of Integrative Biology
|June 11, 2021
PubMed
Summary
This summary is machine-generated.

Metabolite identification in mass spectrometry (MS)-based metabolomics is challenging due to limited spectral databases. MS2Compound offers a user-friendly solution for generating custom predicted spectral libraries and identifying metabolites, accelerating research.

Keywords:
MS2Compoundbioinformaticscomputational biologydata analysismetabolite identificationmetabolomicssystems science

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Area of Science:

  • Systems science
  • Biomedical innovation
  • Metabolomics

Background:

  • Metabolite identification in mass spectrometry (MS)-based global metabolomics is a significant challenge.
  • Lack of comprehensive spectral databases hinders accurate compound identification.
  • Current tools lack user-friendly options for creating or selecting context-specific databases.

Purpose of the Study:

  • Introduce MS2Compound, a novel metabolite identification tool.
  • Enable the generation of custom predicted spectra databases using the Competitive Fragmentation Modeling-ID (CFM-ID) algorithm.
  • Provide a user-friendly platform for metabolomic analysis and compound identification.

Main Methods:

  • Utilized the Competitive Fragmentation Modeling-ID (CFM-ID) algorithm to generate predicted spectra databases.
  • Developed MS2Compound, a tool for creating custom databases and identifying metabolites.
  • Incorporated the mS-score, a novel scoring function for matching raw fragment spectra to predicted databases.
  • Validated mS-score against dot product and hypergeometric scores using benchmarking datasets.
  • Re-analyzed a public metabolomic dataset (MassIVE id: MSV000086784) of Triphala.

Main Results:

  • MS2Compound facilitates the creation of context-specific spectral libraries.
  • The mS-score demonstrates robustness comparable to existing scoring functions for metabolite identification.
  • The tool was successfully applied to a complex traditional drug formulation (Triphala).

Conclusions:

  • MS2Compound provides a user-friendly solution for generating predicted spectral libraries and identifying metabolites in metabolomics.
  • The mS-score enhances the accuracy and reliability of metabolite identification.
  • MS2Compound is expected to accelerate research in systems science and biomedical innovation within the metabolomics community.