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Learning from the unknown: exploring the range of bacterial functionality

Determining the repertoire of a microbe's molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA....

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Autores principales: Mahlich, Yannick, Zhu, Chengsheng, Chung, Henri, Velaga, Pavan K, De Paolis Kaluza, M Clara, Radivojac, Predrag, Friedberg, Iddo, Bromberg, Yana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602916/
https://www.ncbi.nlm.nih.gov/pubmed/37739408
http://dx.doi.org/10.1093/nar/gkad757
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author Mahlich, Yannick
Zhu, Chengsheng
Chung, Henri
Velaga, Pavan K
De Paolis Kaluza, M Clara
Radivojac, Predrag
Friedberg, Iddo
Bromberg, Yana
author_facet Mahlich, Yannick
Zhu, Chengsheng
Chung, Henri
Velaga, Pavan K
De Paolis Kaluza, M Clara
Radivojac, Predrag
Friedberg, Iddo
Bromberg, Yana
author_sort Mahlich, Yannick
collection PubMed
description Determining the repertoire of a microbe's molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here, we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and assigns organisms to Fusion-taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, bacterial functional comparisons outperform marker genes in assigning taxonomic clades. Fusion profiles are also better for this task than other functional annotation schemes. Second, Fusion-taxa are robust to addition of novel organisms and are, arguably, able to capture the environment-driven bacterial diversity. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, created using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities.
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spelling pubmed-106029162023-10-28 Learning from the unknown: exploring the range of bacterial functionality Mahlich, Yannick Zhu, Chengsheng Chung, Henri Velaga, Pavan K De Paolis Kaluza, M Clara Radivojac, Predrag Friedberg, Iddo Bromberg, Yana Nucleic Acids Res Computational Biology Determining the repertoire of a microbe's molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here, we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and assigns organisms to Fusion-taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, bacterial functional comparisons outperform marker genes in assigning taxonomic clades. Fusion profiles are also better for this task than other functional annotation schemes. Second, Fusion-taxa are robust to addition of novel organisms and are, arguably, able to capture the environment-driven bacterial diversity. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, created using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities. Oxford University Press 2023-09-22 /pmc/articles/PMC10602916/ /pubmed/37739408 http://dx.doi.org/10.1093/nar/gkad757 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Computational Biology
Mahlich, Yannick
Zhu, Chengsheng
Chung, Henri
Velaga, Pavan K
De Paolis Kaluza, M Clara
Radivojac, Predrag
Friedberg, Iddo
Bromberg, Yana
Learning from the unknown: exploring the range of bacterial functionality
title Learning from the unknown: exploring the range of bacterial functionality
title_full Learning from the unknown: exploring the range of bacterial functionality
title_fullStr Learning from the unknown: exploring the range of bacterial functionality
title_full_unstemmed Learning from the unknown: exploring the range of bacterial functionality
title_short Learning from the unknown: exploring the range of bacterial functionality
title_sort learning from the unknown: exploring the range of bacterial functionality
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602916/
https://www.ncbi.nlm.nih.gov/pubmed/37739408
http://dx.doi.org/10.1093/nar/gkad757
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