Footprint-based functional analysis of multiomic data

Aurelien Dugourd; Julio Saez-Rodriguez

doi:10.1016/j.coisb.2019.04.002

Footprint-based functional analysis of multiomic data

Curr Opin Syst Biol. 2019 Jun:15:82-90. doi: 10.1016/j.coisb.2019.04.002.

Authors

Aurelien Dugourd^{1

2}, Julio Saez-Rodriguez^{1

2}

Affiliations

¹ Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute of Computational Biomedicine, Bioquant, 69120 Heidelberg, Germany.
² RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), 52074, Aachen, Germany.

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.

Keywords: Data analysis; Footprint; Functional; Integration; Mechanistic; Metabolomics; Multi-omics; Phosphoproteomics; Proteomics; Trans-omics; Transcriptomics.

Publication types

Review