Unifying the known and unknown microbial coding sequence space

Research output: Contribution to journal › Journal article › Research › peer-review

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Unifying the known and unknown microbial coding sequence space. / Vanni, Chiara; Schechter, Matthew S.; Acinas, Silvia G.; Barberán, Albert; Buttigieg, Pier Luigi; Casamayor, Emilio O.; Delmont, Tom O.; Duarte, Carlos M.; Eren, A. Murat; Finn, Robert D.; Kottmann, Renzo; Mitchell, Alex; Sánchez, Pablo; Siren, Kimmo; Steinegger, Martin; Glöckner, Frank Oliver; Fernandez-Guerra, Antonio.

In: eLife, Vol. 11, e67667, 2022.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Vanni, C, Schechter, MS, Acinas, SG, Barberán, A, Buttigieg, PL, Casamayor, EO, Delmont, TO, Duarte, CM, Eren, AM, Finn, RD, Kottmann, R, Mitchell, A, Sánchez, P, Siren, K, Steinegger, M, Glöckner, FO & Fernandez-Guerra, A 2022, 'Unifying the known and unknown microbial coding sequence space', eLife, vol. 11, e67667. https://doi.org/10.7554/eLife.67667

APA

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., Glöckner, F. O., & Fernandez-Guerra, A. (2022). Unifying the known and unknown microbial coding sequence space. eLife, 11, [e67667]. https://doi.org/10.7554/eLife.67667

Vancouver

Vanni C, Schechter MS, Acinas SG, Barberán A, Buttigieg PL, Casamayor EO et al. Unifying the known and unknown microbial coding sequence space. eLife. 2022;11. e67667. https://doi.org/10.7554/eLife.67667

Author

Vanni, Chiara ; Schechter, Matthew S. ; Acinas, Silvia G. ; Barberán, Albert ; Buttigieg, Pier Luigi ; Casamayor, Emilio O. ; Delmont, Tom O. ; Duarte, Carlos M. ; Eren, A. Murat ; Finn, Robert D. ; Kottmann, Renzo ; Mitchell, Alex ; Sánchez, Pablo ; Siren, Kimmo ; Steinegger, Martin ; Glöckner, Frank Oliver ; Fernandez-Guerra, Antonio. / Unifying the known and unknown microbial coding sequence space. In: eLife. 2022 ; Vol. 11.

Bibtex

@article{4849a15eb28543558b3b171cf4d9a13c,
title = "Unifying the known and unknown microbial coding sequence space",
abstract = "Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 4060 systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we propose a conceptual framework and a computational workflow that bridge the known-unknown gap in genomes and metagenomes. We showcase our approach by exploring 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 biomes. Furthermore, we provide a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria, being 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.Competing Interest StatementThe authors have declared no competing interest.",
author = "Chiara Vanni and Schechter, {Matthew S.} and Acinas, {Silvia G.} and Albert Barber{\'a}n and Buttigieg, {Pier Luigi} and Casamayor, {Emilio O.} and Delmont, {Tom O.} and Duarte, {Carlos M.} and Eren, {A. Murat} and Finn, {Robert D.} and Renzo Kottmann and Alex Mitchell and Pablo S{\'a}nchez and Kimmo Siren and Martin Steinegger and Gl{\"o}ckner, {Frank Oliver} and Antonio Fernandez-Guerra",
year = "2022",
doi = "10.7554/eLife.67667",
language = "English",
volume = "11",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications Ltd.",

}

RIS

TY - JOUR

T1 - Unifying the known and unknown microbial coding sequence space

AU - Vanni, Chiara

AU - Schechter, Matthew S.

AU - Acinas, Silvia G.

AU - Barberán, Albert

AU - Buttigieg, Pier Luigi

AU - Casamayor, Emilio O.

AU - Delmont, Tom O.

AU - Duarte, Carlos M.

AU - Eren, A. Murat

AU - Finn, Robert D.

AU - Kottmann, Renzo

AU - Mitchell, Alex

AU - Sánchez, Pablo

AU - Siren, Kimmo

AU - Steinegger, Martin

AU - Glöckner, Frank Oliver

AU - Fernandez-Guerra, Antonio

PY - 2022

Y1 - 2022

N2 - Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 4060 systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we propose a conceptual framework and a computational workflow that bridge the known-unknown gap in genomes and metagenomes. We showcase our approach by exploring 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 biomes. Furthermore, we provide a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria, being 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.Competing Interest StatementThe authors have declared no competing interest.

AB - Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 4060 systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we propose a conceptual framework and a computational workflow that bridge the known-unknown gap in genomes and metagenomes. We showcase our approach by exploring 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 biomes. Furthermore, we provide a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria, being 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.Competing Interest StatementThe authors have declared no competing interest.

U2 - 10.7554/eLife.67667

DO - 10.7554/eLife.67667

M3 - Journal article

C2 - 35356891

VL - 11

JO - eLife

JF - eLife

SN - 2050-084X

M1 - e67667

ER -

ID: 259629765