{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Shaffer M"],"funding":["U.S. Department of Energy","NIAID NIH HHS","National Institutes of Health","National Science Foundation","Wrighton Laboratory"],"pagination":["8883-8900"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7498326"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["48(16)"],"pubmed_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. D"],"journal":["Nucleic acids research"],"pubmed_title":["DRAM for distilling microbial metabolism to automate the curation of microbiome function."],"pmcid":["PMC7498326"],"funding_grant_id":["DE-SC0018022","007447-00002","DE-AC02-05CH11231","1759874","1750189","R01 AI143288","1450032"],"pubmed_authors":["Rodriguez-Ramos J","McGivern BB","Gazitua MC","Shaffer M","Borton MA","La Rosa SL","Liu P","Smith GJ","Pope PB","Vik DR","Bolduc B","Zayed AA","Daly RA","Roux S","Wrighton KC","Narrowe AB","Solden LM","Sullivan MB"],"additional_accession":[]},"is_claimable":false,"name":"DRAM for distilling microbial metabolism to automate the curation of microbiome function.","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. D","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Sep","modification":"2025-04-21T15:37:09.868Z","creation":"2020-09-30T07:04:22Z"},"accession":"S-EPMC7498326","cross_references":{"pubmed":["32766782"],"doi":["10.1093/nar/gkaa621"]}}