dc.contributor.author | Hamzeiy, Hamid | |
dc.contributor.author | Ferretti, Daniela | |
dc.contributor.author | Robles, Maria S. | |
dc.contributor.author | Cox, Juergen | |
dc.date.accessioned | 2023-04-14T11:56:39Z | |
dc.date.available | 2023-04-14T11:56:39Z | |
dc.date.created | 2022-10-11T08:46:47Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 2667-2375 | |
dc.identifier.uri | https://hdl.handle.net/11250/3063116 | |
dc.description.abstract | We introduce Metis, a new plugin for the Perseus software aimed at analyzing quantitative multi-omics data based on metabolic pathways. Data from different omics types are connected through reactions of a genome-scale metabolic-pathway reconstruction. Metabolite concentrations connect through the reactants, while transcript, protein, and protein post-translational modification (PTM) data are associated through the enzymes catalyzing the reactions. Supported experimental designs include static comparative studies and time-series data. As an example for the latter, we combine circadian mouse liver multi-omics data and study the contribution of cycles of phosphoproteome and metabolome to enzyme activity regulation. Our analysis resulted in 52 pairs of cycling phosphosites and metabolites connected through a reaction. The time lags between phosphorylation and metabolite peak show non-uniform behavior, indicating a major contribution of phosphorylation in the modulation of enzymatic activity. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Cell Press | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.title | Perseus plugin “Metis” for metabolic-pathway-centered quantitative multi-omics data analysis for static and time-series experimental designs | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright 2022 The Author(s) | en_US |
dc.source.articlenumber | 100198 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | 10.1016/j.crmeth.2022.100198 | |
dc.identifier.cristin | 2060301 | |
dc.source.journal | Cell Reports Methods | en_US |
dc.identifier.citation | Cell Reports Methods. 2022, 2 (4), 100198. | en_US |
dc.source.volume | 2 | en_US |
dc.source.issue | 4 | en_US |