Unifying the known and unknown microbial coding sequence space
- Max Planck Institute for Marine Microbiology, Germany
- University of Chicago, United States
- Institut de Ciències del Mar-CMIMA (CSIC), Spain
- University of Arizona, United States
- Alfred Wegener Institute, Germany
- Spanish Council for Research, Spain
- Genoscope, Institut François Jacob, CEA, CNRS, France
- King Abdullah University of Science and Technology, Saudi Arabia
- European Molecular Biology Laboratory, United Kingdom
- University of Copenhagen, Denmark
- Seoul National University, Republic of Korea
- University of Bremen, Germany
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 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 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.
Data availability
We used public data as described in the Methods section and Appendix 1-table 5.The code used for the analyses in the manuscript is available at https://github.com/functional-dark-side/functional-dark-side.github.io/tree/master/scripts. A list with the program versions can be found in https://github.com/functional-dark-side/functional-dark-side.github.io/blob/master/programs_and_versions.txt.The code to create the figures is available at https://github.com/functional-dark-side/vanni_et_al-figures, and the data for the figure can be downloaded from https://doi.org/10.6084/m9.figshare.12738476.v2. A reproducible version of the workflow is available at https://github.com/functional-dark-side/agnostos-wf.The data is publicly available at https://doi.org/10.6084/m9.figshare.12459056.
Article and author information
Author details
Matthew S Schechter
Emilio O Casamayor
Antonio Fernàndez-Guerra
Funding
Max Planck Society
- Chiara Vanni
European Union's Horizon 2020 (INMARE)
- Antonio Fernàndez-Guerra
Biotechnology and Biological Sciences Research Council
- Alex Mitchell
European Molecular Biology Laboratory
- Robert D Finn
Spanish Agency of Science MICIU/AEI (INTERACTOMA RTI2018-101205-B-I00)
- Emilio O Casamayor
Spanish Ministry of Economy and Competitiveness (MAGGY (CTM2017-87736-R))
- Silvia G Acinas
- Pablo Sánchez
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- C Titus Brown, University of California, Davis, United States
Publication history
- Preprint posted: July 1, 2020 (view preprint)
- Received: February 18, 2021
- Accepted: March 30, 2022
- Accepted Manuscript published: March 31, 2022 (version 1)
Copyright
© 2022, Vanni et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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