Unifying the known and unknown microbial coding sequence space.

Chiara Vanni, Matthew S. Schechter, Silvia G Acinas, Albert Barberán, Pier Luigi Buttigieg, Emilio O Casamayor, Tom O Delmont, Carlos M. Duarte, A. Murat Eren, Robert D Finn, Renzo Kottmann, Alex Mitchell, Pablo Sanchez, Kimmo Siren, Martin Steinegger, Frank Oliver Gloeckner, Antonio Fernandez-Guerra

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40%-60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we present a conceptual framework, its translation into the computational workflow AGNOSTOS and a demonstration on how we can bridge the known-unknown gap in genomes and metagenomes. By analyzing 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes, we quantify the extent of the unknown fraction, its diversity, and its relevance across multiple organisms and environments. The unknown sequence space is exceptionally diverse, phylogenetically more conserved than the known fraction and predominantly taxonomically restricted at the species level. From the 71M genes identified to be of unknown function, we compiled a collection of 283,874 lineage-specific genes of unknown function for $\textit{Cand}$. Patescibacteria (also known as Candidate Phyla Radiation, CPR), which provides a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data.
Original languageEnglish (US)
StatePublished - Mar 31 2022

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • General Medicine
  • General Immunology and Microbiology
  • General Neuroscience


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