RT Journal Article
SR Electronic
T1 Gazing into the Metaboverse: Automated exploration and contextualization of metabolic data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.25.171850
DO 10.1101/2020.06.25.171850
A1 Jordan A. Berg
A1 Youjia Zhou
A1 T. Cameron Waller
A1 Yeyun Ouyang
A1 Sara M. Nowinski
A1 Tyler Van Ry
A1 Ian George
A1 James E. Cox
A1 Bei Wang
A1 Jared Rutter
YR 2020
UL http://biorxiv.org/content/early/2020/06/28/2020.06.25.171850.abstract
AB 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.