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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.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Related Experiment Video

Updated: Jun 29, 2025

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry
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Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

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Data-Independent Acquisition Peptidomics.

Leon Bichmann1, Shubham Gupta2, Hannes Röst2

  • 1Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany.

Methods in Molecular Biology (Clifton, N.J.)
|March 29, 2024
PubMed
Summary
This summary is machine-generated.

Data-independent acquisition (DIA) in mass spectrometry offers improved reproducibility and sensitivity. The DIAproteomics pipeline automates high-throughput analysis of proteomics and peptidomics data, making DIA accessible to more researchers.

Keywords:
Automated data analysisBiological mass spectrometryDIANextflowPeptidomicsProteomicsSWATH

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Area of Science:

  • Biological Mass Spectrometry
  • Proteomics
  • Peptidomics

Background:

  • Data-independent acquisition (DIA) is a powerful mass spectrometry technique offering enhanced reproducibility, sensitivity, and dynamic range over data-dependent acquisition (DDA).
  • Implementing DIA for high-throughput biological analysis requires robust computational pipelines to manage complex datasets.

Purpose of the Study:

  • To present DIAproteomics, an automated, high-throughput computational pipeline for processing proteomics and peptidomics DIA datasets.
  • To enhance accessibility of DIA mass spectrometry analysis for non-expert users through a user-friendly workflow.

Main Methods:

  • Developed DIAproteomics as a Nextflow-based computational workflow.
  • Integrated OpenSwathWorkflow for chromatogram extraction, peak-picking, scoring, alignment, and statistical evaluation.
  • Utilized spectral libraries (existing or DDA-generated) for DIA data analysis.

Main Results:

  • DIAproteomics enables high-throughput processing of DIA datasets across diverse computing infrastructures.
  • The pipeline facilitates qualitative and quantitative analysis of peptidomics data via command-line usage.
  • The workflow is open-source and available under a permissive license for community use and modification.

Conclusions:

  • DIAproteomics significantly lowers the barrier to entry for utilizing DIA mass spectrometry in biological research.
  • The pipeline supports reproducible and sensitive analysis of large-scale proteomics and peptidomics studies.
  • Encourages community adoption and adaptation of DIAproteomics for specific research needs.