Cloud‐based archived metabolomics data: A resource for in‐source fragmentation/annotation, meta‐analysis and systems biology. Issue 1 (13th June 2020)

• Record Type: Journal Article
• Authors: Palermo, Amelia; Huan, Tao; Rinehart, Duane; Rinschen, Markus M.; Li, Shuzhao; O'Donnell, Valerie B.; Fahy, Eoin; Xue, Jingchuan; Subramaniam, Shankar; Benton, H. Paul; Siuzdak, Gary
• Is Part Of: Analytical Science Advances. Volume 1:Issue 1(2020)
• Page Start: 70
• Page End: 80
• Publication Date: 2020-06-13
• Subjects: archived data -- meta‐analysis -- metabolic pathways -- metabolomics -- proteomics -- systems biology -- transcriptomics; Chemistry, Analytic -- Periodicals; Science -- Periodicals; 543
• DOI: 10.1002/ansa.202000042
• Languages: English
• ISSNs: 2628-5452

Abstract

Abstract: Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta‐analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here, we present a framework for the meta‐analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (eg, XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta‐analysis of (a) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, (b) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high‐throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta‐analysis tool combined with our in‐source fragmentation/annotation (ISA) technology will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.