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Related Experiment Video

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A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
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ProteoStorm: An Ultrafast Metaproteomics Database Search Framework.

Doruk Beyter1, Miin S Lin2, Yanbao Yu3

  • 1Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA.

Cell Systems
|October 1, 2018
PubMed
Summary
This summary is machine-generated.

ProteoStorm enhances shotgun metaproteomics by enabling rapid analysis of complex microbial communities. This new framework significantly speeds up the identification of microbial proteins, even in previously uncharacterized samples.

Keywords:
LC-MS/MSmetaproteomicsmicrobial communitiesmicrobiologyproteome informaticsproteomics

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Area of Science:

  • Microbiology
  • Proteomics
  • Bioinformatics

Background:

  • Shotgun metaproteomics offers insights into microbial community function but faces challenges with complex, unknown samples.
  • Current methods struggle with large pan-microbial databases, leading to computational burdens and reduced sensitivity.
  • Lack of prior taxonomic information hinders comprehensive analysis of microbial communities.

Purpose of the Study:

  • To introduce ProteoStorm, an efficient database search framework for large-scale metaproteomics.
  • To address computational limitations and improve sensitivity in analyzing complex microbial samples.
  • To enable high-confidence peptide-spectrum match (PSM) identification in metaproteomic studies.

Main Methods:

  • Development of ProteoStorm, a novel database search framework for metaproteomics.
  • Implementation of algorithms for efficient searching against large pan-microbial databases.
  • Reanalysis of a urinary tract infection (UTI) dataset comprising 110 individuals.

Main Results:

  • ProteoStorm achieved a two-to-three orders-of-magnitude speedup compared to existing tools.
  • Identified complex polymicrobial expression patterns in UTI samples, including bacterial vaginosis and healthy cases.
  • Discovered previously unreported microbial genera, including the rare pathogen Propionimicrobium, expanding beyond a limited initial database.

Conclusions:

  • ProteoStorm significantly enhances the efficiency and scope of shotgun metaproteomics.
  • The framework enables deeper understanding of microbial community composition and function in complex diseases like UTIs.
  • ProteoStorm facilitates the identification of novel or rare microbial players in human-associated microbiomes.