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Mass Spectrometry: Overview01:19

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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 electron 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 behind a...
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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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Most elements exist in nature as a mixture of isotopes. The isotopes differ in weight due to their respective number of neutrons. The molecular weight of a molecule is different depending on the specific isotope of its elements involved. As a result, the mass spectrum of the molecule exhibits peaks from the same fragment at multiple positions. The positions of these mass signals depend on the mass differences between isotopes. Furthermore, the intensity of these signals is dependent on the...
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The molecular ion peak of a molecule in the mass spectrum provides vital information for molecular identification. However, conventional electron impact ionization can lead to the rapid dissociation of some molecular ions before they reach the detector. A milder ionization method is required to increase the lifetime of such ionized analyte molecules. Chemical ionization (CI) is a gas-phase protonation reaction useful for mass-analyzing analyte molecules that are easily protonated to yield the...
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Automated workflow composition in mass spectrometry-based proteomics.

Magnus Palmblad1, Anna-Lena Lamprecht2, Jon Ison3

  • 1Center for Proteomics and Metabolomics, Leiden University Medical Center, RC Leiden, The Netherlands.

Bioinformatics (Oxford, England)
|July 31, 2018
PubMed
Summary
This summary is machine-generated.

Selecting effective mass spectrometry (MS) data analysis pipelines is challenging. This toolkit aids researchers in comparing bioinformatics tools and workflows for MS proteomics, revealing significant result variations.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Mass Spectrometry Data Analysis

Background:

  • Numerous software utilities exist for mass spectrometry (MS) data analysis, offering building blocks for custom workflows.
  • Researchers often struggle to identify optimal tool combinations and effective data analysis pipelines for specific experimental designs.
  • This difficulty hinders the selection of practical and efficient bioinformatics workflows for MS-based proteomics.

Purpose of the Study:

  • To provide a toolkit that supports researchers in identifying, comparing, and benchmarking multiple workflows composed of individual bioinformatics tools.
  • To enable automated workflow composition through semantic annotation of tools using the EDAM ontology.
  • To demonstrate the practical utility of the framework by evaluating equivalent workflows for common MS-based proteomics tasks.

Main Methods:

  • Developed a toolkit for automated workflow composition using semantically annotated bioinformatics tools (EDAM ontology).
  • Created and evaluated multiple logically and semantically equivalent workflows for four representative MS-based proteomics use cases.
  • Benchmarked the performance and output of these diverse workflows.

Main Results:

  • The evaluation demonstrated considerable variations in results computed by different, yet semantically equivalent, workflows.
  • This highlights the importance of systematic exploration and comparison of potential data analysis pipelines.
  • The toolkit facilitates the identification of optimal workflows for specific mass spectrometry data analysis needs.

Conclusions:

  • The developed framework effectively supports the systematic exploration and benchmarking of bioinformatics workflows for mass spectrometry data.
  • Automated workflow composition using semantic annotations (EDAM) simplifies the process of creating and comparing analysis pipelines.
  • Researchers can leverage this toolkit to improve the selection of practical and effective data analysis strategies in MS-based proteomics.