DRAM for distilling microbial metabolism to automate the curation of microbiome function

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ID: 302499
2020
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Abstract
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.
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Authors Michael Shaffer, Mikayla Borton, Bridget B. McGivern, Ahmed A. Zayed, Sabina Leanti La Rosa, Lindsey Solden, Pengfei Liu, Adrienne B. Narrowe, Josué Rodríguez-Ramos, Benjamin Bolduc, María Consuelo Gazitúa, Rebecca A. Daly, Garrett J. Smith, Dean Vik, Phillip B. Pope, Matthew B. Sullivan, Simon Roux, Kelly Wrighton
Journal Nucleic Acids Research
Year 2020
DOI
10.1093/nar/gkaa621
URL
https://doi.org/10.1093/nar/gkaa621
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