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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics.

Chih-Chiang Tsou1, Dmitry Avtonomov2, Brett Larsen3

  • 11] Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA. [2] Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.

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|January 20, 2015
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Summary
This summary is machine-generated.

DIA-Umpire software enables sensitive, untargeted analysis of mass spectrometry data-independent acquisition (DIA) using multiplex fragmentation. It allows for accurate protein quantification without spectral libraries, improving consistency across samples.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Recent advancements in mass spectrometry (MS) have spurred interest in data-independent acquisition (DIA) strategies.
  • DIA involves systematic fragmentation of all peptides using wide mass-isolation windows, termed multiplex fragmentation.
  • Analyzing DIA data requires specialized computational tools for feature detection and spectral assembly.

Purpose of the Study:

  • To introduce DIA-Umpire, a comprehensive computational workflow and open-source software for DIA data analysis.
  • To enable sensitive, untargeted analysis of DIA data without reliance on spectral libraries.
  • To provide robust and consistent quantification of proteins across multiple samples.

Main Methods:

  • DIA-Umpire detects precursor and fragment chromatographic features from DIA data.
  • It assembles these features into pseudo-tandem MS spectra for identification.
  • Identification is performed using conventional database searching and protein inference tools.
  • Quantification utilizes both precursor- and fragment-ion intensities.
  • Targeted extraction of quantitative information is enabled based on initially identified peptides.

Main Results:

  • DIA-Umpire successfully identifies precursor and fragment features, generating pseudo-tandem MS spectra.
  • The software allows for sensitive, untargeted protein identification and quantification from DIA data.
  • Demonstrated performance with complex control samples, glycoproteomics, and affinity purification-MS data.
  • Achieved consistent quantification across multiple samples through targeted information extraction.

Conclusions:

  • DIA-Umpire provides a robust computational solution for analyzing mass spectrometry data-independent acquisition data.
  • The software facilitates sensitive, untargeted proteomics and accurate quantification without spectral libraries.
  • DIA-Umpire enhances the consistency and reliability of quantitative proteomics workflows.