Unifying the known and unknown microbial coding sequence space
Open accessPublished version of a preprint
Abstract
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 1749 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 71 M genes identified to be of unknown function, we compiled a collection of 283,874 lineage-specific genes of unknown function for 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.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Self-citationFast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2022) · doi:10.1101/2022.02.07.479398 · Cited by 2,598 · see citing works
Self-citationFast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01773-0 · Cited by 2,596 · see citing works
Self-citationShotgun metagenomics, from sampling to analysis
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017) · Nature Biotechnology · doi:10.1038/nbt.3935 · Cited by 2,202 · see citing works
Complex archaea that bridge the gap between prokaryotes and eukaryotes
Spang, A., Saw, J. H., Jørgensen, S. L., Zaremba-Niedzwiedzka, K., Martijn, J., Lind, A. E., van Eijk, R., Schleper, C., Guy, L., & Ettema, T. J. G. (2015) · Nature · doi:10.1038/nature14447 · Cited by 1,324 · see citing works
Strains, functions and dynamics in the expanded Human Microbiome Project
Lloyd-Price, J., Mahurkar, A., Rahnavard, G., Crabtree, J., Orvis, J., Hall, A. B., Brady, A., Creasy, H. H., McCracken, C., Giglio, M. G., et al. (2017) · Nature · doi:10.1038/nature23889 · Cited by 1,267 · see citing works
Unusual biology across a group comprising more than 15% of domain Bacteria
Brown, C. T., Hug, L. A., Thomas, B. C., Sharon, I., Castelle, C. J., Singh, A., Wilkins, M. J., Wrighton, K. C., Williams, K. H., & Banfield, J. F. (2015) · Nature · doi:10.1038/nature14486 · Cited by 1,216 · cited 3× · see citing works
“Patescibacteria, more commonly known as Candidate Phyla Radiation (CPR), a phylum that has raised considerable interest due to its unusual biology (Brown et al., 2015) and for which we provide an extensive catalog of 283,874 linegage- specific genes of unknown function at different taxonomic level resolutions, which w…”
BackgroundModeling the language of life – Deep Learning Protein Sequences
Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606 · see citing works
Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
Steinegger, M., Mirdita, M., & Söding, J. (2019) · Nature Methods · doi:10.1038/s41592-019-0437-4 · Cited by 445 · cited 1× · see citing works
“Alternatives like proteinlevel assembly (Steinegger et al., 2019b) combined with exploring the assembly graphs’ neighborhoods (Brown et al.”
BackgroundSelf-citationUsing deep learning to annotate the protein universe
Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., Bateman, A., DePristo, M. A., & Colwell, L. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01179-w · Cited by 332 · see citing works
Steinegger, M., & Salzberg, S. L. (2020) · Genome Biology · doi:10.1186/s13059-020-02023-1 · Cited by 246 · see citing works
Self-citationA Review of bioinformatics tools for bio-prospecting from metagenomic sequence data
Roumpeka, D. D., Wallace, R. J., Escalettes, F., Fotheringham, I., & Watson, M. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00023 · Cited by 199 · see citing works
Delmont, T. O., Gaia, M., Hinsinger, D. D., Frémont, P., Vanni, C., Fernandez-Guerra, A., Eren, A. M., Kourlaiev, A., d'Agata, L., Clayssen, Q., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100123 · Cited by 173 · see citing works
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Delmont, T. O., Gaia, M., Hinsinger, D. D., Frémont, P., Vanni, C., Fernandez-Guerra, A., Eren, A. M., Kourlaiev, A., d'Agata, L., Clayssen, Q., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100123 · Cited by 173 · see citing works
Self-citationGenomic language model predicts protein co-regulation and function
Hwang, Y., Cornman, A. L., Kellogg, E. H., Ovchinnikov, S., & Girguis, P. R. (2024) · Nature Communications · doi:10.1038/s41467-024-46947-9 · Cited by 99 · see citing works
Ghaly, T. M., Rajabal, V., Russell, D., Colombi, E., & Tetu, S. G. (2025) · doi:10.1101/2025.04.02.646905 · Cited by 5 · see citing works
- Authors
- Vanni, C., Schechter, M. S., Acinas, S. G., Barberán, A., Buttigieg, P. L., Casamayor, E. O., Delmont, T. O., Duarte, C. M., Eren, A. M., Finn, R. D., Kottmann, R., Mitchell, A., Sánchez, P., Siren, K., Steinegger, M., Gloeckner, F. O., & Fernàndez-Guerra, A.
- Affiliations
- Max Planck Society (DE)
- Consejo Superior de Investigaciones Científicas (ES)
- University of Arizona (US)
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (DE)
- Centre National de la Recherche Scientifique (FR)
- King Abdullah University of Science and Technology (SA)
- Marine Biological Laboratory (US)
- European Bioinformatics Institute (GB)
- University of Copenhagen (DK)
- Seoul National University (KR)
- Corresponding author
- Fernàndez-Guerra, Antonio
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2022-03-31
- Volume
- 11
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC9132574
- OpenAlex
- W4225434086
- Cited by
- 134
- References
- 13
- Citations by year
- 2026: 32 · 2025: 33 · 2024: 33 · 2023: 24 · 2022: 11 · 2021: 1
- FWCI
- 8.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Helmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) (acceptedVersion)
- UA Campus Repository (The University of Arizona) (acceptedVersion, cc-by)
- UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by)
- Knowledge@UChicago (University of Chicago) (submittedVersion, cc-by-nc-nd)
- HAL (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, cc-by)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Max Planck Society
- Horizon 2020 (INMARE)
- Biotechnology and Biological Sciences Research Council
- European Molecular Biology Laboratory
- Spanish Agency of Science MICIU/AEI/FEDER (INTERACTOMA RTI2018-101205-B-I00)
- Spanish Ministry of Economy and Competitiveness (MAGGY (CTM2017-87736-R))
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- bacterial genomes, archaeal genomes, lineage-specific genes, Candidate Phyla Radiation, Patescibacteria, antibiotic resistance genes, hypothesis generation
- MeSH terms
- Bacteria, Open Reading Frames, Genome, Archaeal, Metagenome
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