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Related Concept Videos

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. 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...
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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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Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
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Algorithms for database-dependent search of MS/MS data.

Rune Matthiesen1

  • 1Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal.

Methods in Molecular Biology (Clifton, N.J.)
|May 14, 2013
PubMed
Summary
This summary is machine-generated.

This study clarifies the algorithms behind database-dependent search engines for protein identification using MS/MS spectra. It details search strategy, peptide/protein scoring, and protein inference, highlighting areas for improvement.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Mass Spectrometry

Background:

  • Bottom-up proteomics generates thousands of MS/MS spectra for protein identification.
  • Database-dependent search engines match spectra against protein sequence databases.

Purpose of the Study:

  • To outline the algorithmic strategies of different database-dependent search engines.
  • To make algorithmic ideas more transparent and comparable by dissecting the search process.

Main Methods:

  • The study divides the database-dependent search process into four steps: search strategy, peptide scoring, protein scoring, and protein inference.
  • It analyzes the algorithms underlying each step, rather than focusing on software manuals.

Main Results:

  • Most search engines offer robust implementations for the first three steps (search strategy, peptide/protein scoring).
  • Protein inference is often less developed and frequently handled by external software.

Conclusions:

  • Algorithmic ideas remain valuable even with suboptimal software implementations.
  • The VEMS search engine integrates protein inference, and SIR is a standalone tool for this purpose.