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Applying Advanced In Vitro Culturing Technology to Study the Human Gut Microbiota
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Database limitations for studying the human gut microbiome.

Camila K Dias1, Robert Starke2, Victor S Pylro3

  • 1Departament of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary
This summary is machine-generated.

Evaluating human gut microbiome data is challenging. The EggNOG database offers better cross-referencing with UniProt for functional annotation than KEGG, though improvements are needed.

Keywords:
DatabaseEggNOGFunctional diversityGut microbiomeHuman MicrobiomeKEGGUniprot

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Advancements in sequencing technologies have generated vast amounts of human gut microbiome data.
  • Processing and annotating this genetic and metabolic information presents significant challenges.
  • This study assesses the utility of major databases for annotating a mock human gut microbiome.

Purpose of the Study:

  • To evaluate the effectiveness of UniProt KB cross-referenced with KEGG Orthologs and EggNOG in describing a mock human gut microbiome.
  • To identify the extent of species and genera coverage within these databases for gut microbiome composition.
  • To analyze the distribution and core functions within the human gut microbiome based on database annotations.

Main Methods:

  • Cross-referencing a list of known human gut microbiome species against UniProt KB, KEGG Orthologs, and EggNOG.
  • Analyzing the number of corresponding entries for species and genera in KEGG and EggNOG.
  • Assessing the distribution of KEGG Orthologs (KOs) and EggNOG Orthologs (NOGs) across different taxonomic levels and identifying core functions.

Main Results:

  • EggNOG provided entries for 82 species and 47 genera, while KEGG provided entries for 53 species and 40 genera.
  • Identified core functions present across multiple species and genera within the human gut microbiome.
  • Highlighted discrepancies in database entries and a general lack of annotated species in UniProt, impacting functional inference.

Conclusions:

  • Cross-referencing UniProt with KEGG or EggNOG for gut microbiome functional inference is limited by low annotation rates.
  • EggNOG demonstrated superior performance compared to KEGG for cross-searching with UniProt in this context.
  • Further research involving cultivation, advanced sequencing, and genome reconstruction is crucial for accurate functional annotation in microbiome studies.