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An Integrated Approach for Microprotein Identification and Sequence Analysis
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Unifying the known and unknown microbial coding sequence space.

Chiara Vanni1,2, Matthew S Schechter1,3, Silvia G Acinas4

  • 1Microbial Genomics and Bioinformatics Research G, Max Planck Institute for Marine Microbiology, Bremen, Germany.

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|March 31, 2022
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Summary
This summary is machine-generated.

Scientists developed AGNOSTOS, a computational workflow to analyze microbial genes of unknown function. This approach bridges the known-unknown gap, revealing the diversity and conservation of these genes and aiding in understanding microbial biology and discovering new functions like antibiotic resistance.

Keywords:
bioinformaticscomputational biologyfunctional metageomicsgene clustersinfectious diseasemicrobial genomicsmicrobiologyphylogenomicssystems biologyunknown function

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Area of Science:

  • Genomics
  • Metagenomics
  • Molecular Biology

Background:

  • A significant portion (40-60%) of predicted genes in microbial systems remain of unknown function, posing a major challenge in molecular biology.
  • Existing analytical workflows lack systematic approaches to incorporate and analyze this 'unknown fraction' of genes.

Purpose of the Study:

  • To present a conceptual framework and computational workflow (AGNOSTOS) for analyzing genes of unknown function in genomes and metagenomes.
  • To quantify the extent, diversity, and relevance of unknown genes across various organisms and environments.
  • To demonstrate the utility of analyzing unknown genes for expanding biological understanding and hypothesis generation.

Main Methods:

  • Analysis of over 415 million genes from 1749 metagenomes and 28,941 bacterial and archaeal genomes.
  • Development and application of the AGNOSTOS computational workflow.
  • Compilation of lineage-specific unknown genes from Candidate Phyla Radiation (CPR) bacteria.

Main Results:

  • The unknown sequence space is highly diverse, phylogenetically conserved, and taxonomically restricted at the species level.
  • A collection of 283,874 lineage-specific unknown genes was identified for CPR bacteria.
  • A target gene of unknown function linked to antibiotic resistance was identified, enabling hypothesis generation.

Conclusions:

  • The AGNOSTOS workflow effectively bridges the known-unknown gap in genomic and metagenomic data.
  • Analysis of unknown genes provides valuable insights into microbial biology, particularly for understudied groups like CPR bacteria.
  • This approach can accelerate the discovery of novel functions, including those relevant to practical applications like antibiotic resistance.