Multi 'omic data integration: A review of concepts, considerations, and approaches

The application of 'omic techniques including, but not limited to genomics/metagenomics, transcriptomics/meta-transcriptomics, proteomics/meta-proteomics, and metabolomics to generate multiple datasets from a single sample have facilitated hypothesis generation leading to the identification of biological, molecular and ecological functions and mechanisms, as well as associations and correlations. Despite their power and promise, a variety of challenges must be considered in the successful design and execution of a multi-omics study. In this review, various 'omic technologies applicable to single-and meta-organisms (i.e., host + microbiome) are described, and considerations for sample collection, storage and processing prior to data generation and analysis, as well as approaches to data storage, dissemination and analysis are discussed. Finally, case studies are included as examples of multi-omic applications providing novel insights and a more holistic understanding of biological processes. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

The application of various 'omic techniques on a single sample can generate multiple datasets to identify biological, molecular, and ecological functions, as well as associations. However, numerous challenges must be addressed in the successful design and execution of a multi-omics study, including considerations for sample collection, processing, data storage, and analysis. Case studies provide examples of how multi-omic approaches can offer novel insights and a more holistic understanding of biological processes.