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Related Concept Videos

Proteomics01:33

Proteomics

7.2K
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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Updated: Jun 13, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
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MultiStageSearch: An Iterative Workflow for Unbiased Taxonomic Analysis of Pathogens Using Proteogenomics.

Julian Pipart1, Tanja Holstein1,2,3,4,5, Lennart Martens2,3,4,5

  • 1Data Competence Center MF 2, Robert Koch Institute, Berlin 13353, Germany.

Journal of Proteome Research
|May 19, 2025
PubMed
Summary
This summary is machine-generated.

MultiStageSearch enhances pathogen diagnostics by combining genomic and proteomic data. This novel method overcomes database biases for more accurate strain identification in research and public health.

Keywords:
Reverse Transcription-Polymerase Chain Reaction (RT-PCR)SARS-CoV-2open reading frame (ORF)peptide-spectrum matches (PSMs)“Norovirus GII”

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics and Proteomics

Background:

  • The SARS-CoV-2 pandemic highlighted critical needs in accurate pathogen diagnostics.
  • Genomics is standard, but mass spectrometry-based proteomics offers complementary data.
  • Existing genomic and proteomic databases suffer from taxonomic bias and incompleteness, hindering accurate identification.

Purpose of the Study:

  • To develop a robust computational method for accurate taxonomic analysis of pathogens.
  • To address limitations of current reference databases in pathogen identification.
  • To improve strain-level identification capabilities for infectious disease research and diagnostics.

Main Methods:

  • Introduced MultiStageSearch, a multistep database search strategy.
  • Combined generalist proteome databases for initial species inference.
  • Generated specialized, preprocessed proteogenomic databases for precise identification, reducing redundancy and bias.
  • Workflow operates independently of existing strain-level taxonomies.

Main Results:

  • MultiStageSearch demonstrated superior performance in strain-level taxonomic inference on viral and bacterial samples.
  • The method effectively overcomes incomplete search spaces and biases in reference databases.
  • Successfully identified strains not represented in current taxonomies.

Conclusions:

  • MultiStageSearch provides a flexible and accurate approach for pathogen research and diagnostics.
  • This method enhances the reliability of identifying and characterizing microbial strains.
  • Addresses key challenges in infectious disease surveillance and outbreak response.