Footprint-based functional analysis of multiomic data. Issue 15 (June 2019)

• Record Type: Journal Article
• Authors: Dugourd, Aurelien; Saez-Rodriguez, Julio
• Is Part Of: Current opinion in systems biology. Issue 15(2019)
• Page Start: 82
• Page End: 90
• Publication Date: 2019-06
• Subjects: Transcriptomics -- Phosphoproteomics -- Proteomics -- Metabolomics -- Multi-omics -- Trans-omics -- Footprint -- Mechanistic -- Data analysis -- Functional -- Integration; Systems biology -- Periodicals; 570
• DOI: 10.1016/j.coisb.2019.04.002
• Languages: English
• ISSNs: 2452-3100

Abstract

Abstract: Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms. Graphical abstract: Image 1 Highlights: Functional information on signalling pathways, metabolism and gene regulation can be found across multiple types of omic data. One way to extract such information is to consider these data as the footprint of the activity of enzymes and pathways. Information on enzyme/pathway activities and omic data can be integrated together to contextualise multi-scale networks. Such an approach can lead to the discovery of regulatory events spanning across multiple biological processes.