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DRAM for distilling microbial metabolism to automate the curation of microbiome function
Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotati...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498326/ https://www.ncbi.nlm.nih.gov/pubmed/32766782 http://dx.doi.org/10.1093/nar/gkaa621 |
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author | Shaffer, Michael Borton, Mikayla A McGivern, Bridget B Zayed, Ahmed A La Rosa, Sabina Leanti Solden, Lindsey M Liu, Pengfei Narrowe, Adrienne B Rodríguez-Ramos, Josué Bolduc, Benjamin Gazitúa, M Consuelo Daly, Rebecca A Smith, Garrett J Vik, Dean R Pope, Phil B Sullivan, Matthew B Roux, Simon Wrighton, Kelly C |
author_facet | Shaffer, Michael Borton, Mikayla A McGivern, Bridget B Zayed, Ahmed A La Rosa, Sabina Leanti Solden, Lindsey M Liu, Pengfei Narrowe, Adrienne B Rodríguez-Ramos, Josué Bolduc, Benjamin Gazitúa, M Consuelo Daly, Rebecca A Smith, Garrett J Vik, Dean R Pope, Phil B Sullivan, Matthew B Roux, Simon Wrighton, Kelly C |
author_sort | Shaffer, Michael |
collection | PubMed |
description | Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function. |
format | Online Article Text |
id | pubmed-7498326 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-74983262020-09-23 DRAM for distilling microbial metabolism to automate the curation of microbiome function Shaffer, Michael Borton, Mikayla A McGivern, Bridget B Zayed, Ahmed A La Rosa, Sabina Leanti Solden, Lindsey M Liu, Pengfei Narrowe, Adrienne B Rodríguez-Ramos, Josué Bolduc, Benjamin Gazitúa, M Consuelo Daly, Rebecca A Smith, Garrett J Vik, Dean R Pope, Phil B Sullivan, Matthew B Roux, Simon Wrighton, Kelly C Nucleic Acids Res Computational Biology Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function. Oxford University Press 2020-08-07 /pmc/articles/PMC7498326/ /pubmed/32766782 http://dx.doi.org/10.1093/nar/gkaa621 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Shaffer, Michael Borton, Mikayla A McGivern, Bridget B Zayed, Ahmed A La Rosa, Sabina Leanti Solden, Lindsey M Liu, Pengfei Narrowe, Adrienne B Rodríguez-Ramos, Josué Bolduc, Benjamin Gazitúa, M Consuelo Daly, Rebecca A Smith, Garrett J Vik, Dean R Pope, Phil B Sullivan, Matthew B Roux, Simon Wrighton, Kelly C DRAM for distilling microbial metabolism to automate the curation of microbiome function |
title | DRAM for distilling microbial metabolism to automate the curation of microbiome function |
title_full | DRAM for distilling microbial metabolism to automate the curation of microbiome function |
title_fullStr | DRAM for distilling microbial metabolism to automate the curation of microbiome function |
title_full_unstemmed | DRAM for distilling microbial metabolism to automate the curation of microbiome function |
title_short | DRAM for distilling microbial metabolism to automate the curation of microbiome function |
title_sort | dram for distilling microbial metabolism to automate the curation of microbiome function |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498326/ https://www.ncbi.nlm.nih.gov/pubmed/32766782 http://dx.doi.org/10.1093/nar/gkaa621 |
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