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The Association of Biomolecular Resource Facilities Proteome Informatics Research Group Study on Metaproteomics (iPRG-2020)
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2023 (English)In: Journal of biomolecular techniques : JBT, E-ISSN 1943-4731, Vol. 34, no 3Article in journal (Refereed) Published
Abstract [en]

Metaproteomics research using mass spectrometry data has emerged as a powerful strategy to understand the mechanisms underlying microbiome dynamics and the interaction of microbiomes with their immediate environment. Recent advances in sample preparation, data acquisition, and bioinformatics workflows have greatly contributed to progress in this field. In 2020, the Association of Biomolecular Research Facilities Proteome Informatics Research Group launched a collaborative study to assess the bioinformatics options available for metaproteomics research. The study was conducted in 2 phases. In the first phase, participants were provided with mass spectrometry data files and were asked to identify the taxonomic composition and relative taxa abundances in the samples without supplying any protein sequence databases. The most challenging question asked of the participants was to postulate the nature of any biological phenomena that may have taken place in the samples, such as interactions among taxonomic species. In the second phase, participants were provided a protein sequence database composed of the species present in the sample and were asked to answer the same set of questions as for phase 1. In this report, we summarize the data processing methods and tools used by participants, including database searching and software tools used for taxonomic and functional analysis. This study provides insights into the status of metaproteomics bioinformatics in participating laboratories and core facilities.

Place, publisher, year, edition, pages
Association of Biomolecular Resource Facilities , 2023. Vol. 34, no 3
Keywords [en]
bioinformatics, mass spectrometry, metaproteomics, microbiome, taxonomy
National Category
Bioinformatics and Computational Biology Bioinformatics (Computational Biology) Microbiology
Identifiers
URN: urn:nbn:se:kth:diva-340290DOI: 10.7171/3fc1f5fe.a058bad4PubMedID: 37969874Scopus ID: 2-s2.0-85177054415OAI: oai:DiVA.org:kth-340290DiVA, id: diva2:1816136
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QC 20231201

Available from: 2023-12-01 Created: 2023-12-01 Last updated: 2025-02-05Bibliographically approved

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Käll, Lukas

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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