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

TLDR

Microbial and viral communities reshape Earth’s chemistry, but decoding their specific reactions is hindered by a lack of scalable, metabolically resolved annotation tools. This study introduces DRAM, a framework that converts microbiome genomic data into a catalog of microbial traits. We evaluated DRAM on a defined in silico soil community and published human gut metagenomes to demonstrate its applicability across diverse genomes. DRAM accurately assigned microbial roles in geochemical cycles, automated gut carbohydrate metabolism partitioning, and, via its viral mode DRAM‑v, identified thousands of auxiliary metabolic genes, collectively enabling detailed metabolic profiling of microbiomes.

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.