TY - JOUR T1 - Gazing into the Metaboverse: Automated exploration and contextualization of metabolic data JF - bioRxiv DO - 10.1101/2020.06.25.171850 SP - 2020.06.25.171850 AU - Jordan A. Berg AU - Youjia Zhou AU - T. Cameron Waller AU - Yeyun Ouyang AU - Sara M. Nowinski AU - Tyler Van Ry AU - Ian George AU - James E. Cox AU - Bei Wang AU - Jared Rutter Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/06/28/2020.06.25.171850.abstract N2 - Metabolism and its component reactions are complex, each with variable inputs, outputs, and modifiers. The harmony between these factors consequently determines the health and stability of a cell or an organism. Perturbations to any reaction component can have rippling downstream effects, which can be challenging to trace across the global reaction network, particularly when the effects occur between canonical representations of pathways. Researchers have primarily utilized reductionist approaches to understand metabolic reaction systems; however, customary methods often limit the analysis scope. Even the power of systems-centric omics approaches can be limited when only a handful of high magnitude signals in the data are prioritized. To address these challenges, we developed Metaboverse, an interactive tool for the exploration and automated extraction of potential regulatory events, patterns, and trends from multi-omic data within the context of the metabolic network and other global reaction networks. This framework will be foundational in increasing our ability to holistically understand static and temporal metabolic events and perturbations as well as gene-metabolite intra-cooperativity. Metaboverse is freely available under a GPL-3.0 license at https://github.com/Metaboverse/.Graphical Abstract (displayed as Figure 1)Competing Interest StatementThe authors have declared no competing interest. ER -