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...

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Published in:	Nucleic acids research Vol. 48; no. 16; pp. 8883 - 8900
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 18.09.2020
Subjects:	Bacteria - classification BASIC BIOLOGICAL SCIENCES Computational Biology Computational Methods Gastrointestinal Microbiome Genomics - methods Humans Metabolomics - methods Metagenome Molecular Sequence Annotation - methods Software Soil Microbiology Viruses - classification
ISSN:	0305-1048, 1362-4962, 1362-4962
Online Access:	Get full text
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Abstract	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.
AbstractList	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. 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.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.
Author	La Rosa, Sabina Leanti Daly, Rebecca A Rodríguez-Ramos, Josué Gazitúa, M Consuelo Vik, Dean R Zayed, Ahmed A Shaffer, Michael Smith, Garrett J Borton, Mikayla A McGivern, Bridget B Solden, Lindsey M Sullivan, Matthew B Narrowe, Adrienne B Wrighton, Kelly C Pope, Phil B Liu, Pengfei Bolduc, Benjamin Roux, Simon
Author_xml	– sequence: 1 givenname: Michael surname: Shaffer fullname: Shaffer, Michael organization: Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA – sequence: 2 givenname: Mikayla A surname: Borton fullname: Borton, Mikayla A organization: Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA – sequence: 3 givenname: Bridget B orcidid: 0000-0001-9023-0018 surname: McGivern fullname: McGivern, Bridget B organization: Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA – sequence: 4 givenname: Ahmed A surname: Zayed fullname: Zayed, Ahmed A organization: Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA – sequence: 5 givenname: Sabina Leanti orcidid: 0000-0003-3527-8101 surname: La Rosa fullname: La Rosa, Sabina Leanti organization: Faculty of Biosciences, Norwegian University of Life Sciences, Aas 1432, Norway – sequence: 6 givenname: Lindsey M surname: Solden fullname: Solden, Lindsey M organization: Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA – sequence: 7 givenname: Pengfei surname: Liu fullname: Liu, Pengfei organization: Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA – sequence: 8 givenname: Adrienne B surname: Narrowe fullname: Narrowe, Adrienne B organization: Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA – sequence: 9 givenname: Josué surname: Rodríguez-Ramos fullname: Rodríguez-Ramos, Josué organization: Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA – sequence: 10 givenname: Benjamin surname: Bolduc fullname: Bolduc, Benjamin organization: Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA – sequence: 11 givenname: M Consuelo surname: Gazitúa fullname: Gazitúa, M Consuelo organization: Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA – sequence: 12 givenname: Rebecca A surname: Daly fullname: Daly, Rebecca A organization: Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA – sequence: 13 givenname: Garrett J surname: Smith fullname: Smith, Garrett J organization: Department of Microbiology, Radboud University, Nijmegen 6525, Netherlands – sequence: 14 givenname: Dean R surname: Vik fullname: Vik, Dean R organization: Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA – sequence: 15 givenname: Phil B surname: Pope fullname: Pope, Phil B organization: Faculty of Biosciences, Norwegian University of Life Sciences, Aas 1432, Norway – sequence: 16 givenname: Matthew B surname: Sullivan fullname: Sullivan, Matthew B organization: Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA – sequence: 17 givenname: Simon surname: Roux fullname: Roux, Simon organization: Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA – sequence: 18 givenname: Kelly C orcidid: 0000-0003-0434-4217 surname: Wrighton fullname: Wrighton, Kelly C organization: Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32766782$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/1646715$$D View this record in Osti.gov
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Copyright	The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. 2020
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Snippet	Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to...
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SubjectTerms	Bacteria - classification BASIC BIOLOGICAL SCIENCES Computational Biology Computational Methods Gastrointestinal Microbiome Genomics - methods Humans Metabolomics - methods Metagenome Molecular Sequence Annotation - methods Software Soil Microbiology Viruses - classification
Title	DRAM for distilling microbial metabolism to automate the curation of microbiome function
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