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Related Concept Videos

Mass Analyzers: Overview01:13

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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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Enabling Next-Generation Mass Spectrometry-Based Proteomics: Standards, Proteoform Resolution, and FAIR,

Rui Vitorino1,2

  • 1iBiMED, Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal.

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Summary

This study introduces an integrated framework for next-generation proteomics, emphasizing FAIR data principles and proteoform-centric approaches. It aims to enhance data sharing, interoperability, and computational analysis for reproducible and scalable proteomic research.

Keywords:
AI/MLFAIR infrastructuresproteoform-centric biology

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Area of Science:

  • Proteomics
  • Computational Biology
  • Bioinformatics

Background:

  • Advances in mass spectrometry and multi-omics have expanded proteomics, but data reuse is hindered by inconsistent standards and poor interoperability.
  • Proteoform-centric approaches offer higher resolution for protein variants and post-translational modifications.
  • Current AI and machine learning applications in proteomics often operate independently of FAIR data practices.

Purpose of the Study:

  • To present an integrated framework for next-generation proteomics.
  • To establish machine-actionable foundations for proteoform-resolved analysis and computational inference using standardization and FAIR principles.
  • To provide practical recommendations for AI-ready proteomics, including a community checklist for reproducibility and scalability.

Main Methods:

  • Examining community efforts for data sharing and interoperability.
  • Characterizing proteoforms using bottom-up, middle-down, and top-down mass spectrometry approaches.
  • Highlighting emerging AI and ML applications within the proteomics workflow.

Main Results:

  • An integrated framework is proposed that links FAIR principles with proteoform resolution.
  • An architectural model is introduced to merge FAIR infrastructures and proteoform analysis.
  • Recommendations and a checklist are provided to promote AI-ready proteomics, reproducibility, and translational scalability.

Conclusions:

  • Treating proteoforms as primary computational entities and adopting FAIR practices are crucial for reproducible and interpretable proteomic modeling.
  • Integrating FAIR infrastructures with proteoform resolution is key for next-generation proteomics.
  • Standardization and AI analytics, when combined with proteoform-centric approaches, will drive the future of proteomics research and application.