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.

中文翻译：

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DRAM，用于蒸馏微生物代谢以自动化微生物组功能的管理。

微生物和病毒群落改变了地球生态系统的化学，但是由于缺乏可扩展的，代谢分解的注释软件，这些生物引擎催化的特定反应难以解读。在这里，我们本DRAM（d istilled和- [R efined甲的nnotation中号etabolism），一个框架，基于微生物基因组信息雨淋转换成目录微生物性状。为了证明DRAM在代谢多样的基因组中的适用性，我们在定义的计算机上评估了DRAM的性能。土壤群落和先前发表的人类肠道元基因组。我们表明，DRAM准确地将微生物贡献分配给了地球化学循环，并在底物水平上自动化了肠道微生物碳水化合物代谢的分配。DRAM-v是DRAM的病毒模式，它建立了识别病毒编码的辅助代谢基因（AMG）的规则，从而导致了成千上万种来自土壤和肠道的推定AMG的代谢分类。DRAM和DRAM-v共同提供了关键的代谢谱分析功能，这些功能可破解支持微生物组功能的机制。