Integration of omics: more than the sum of its parts.

Joerg Martin Buescher,Edward M Driggers

doi:10.1186/s40170-016-0143-y

Joerg Martin Buescher, Edward M Driggers

Open Access

https://doi.org/10.1186/s40170-016-0143-y

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Journal: Cancer & metabolism	Publication Date: Feb 19, 2016
Citations: 93	License type: cc-by

Affiliation: KU Leuven, Winchester Medical Center

Abstract

Genome scale data on biological systems has increasingly become available by sequencing of DNA and RNA, and by mass spectrometric quantification of proteins and metabolites. The cellular components from which these -omics regimes are derived act as one integrated system in vivo; thus, there is a natural instinct to integrate -omics data types. Statistical analyses, the use of previous knowledge in the form of networks, and the use of time-resolved measurements are three key design elements for life scientists to consider in planning integrated -omics studies. These design elements are reviewed in the context of multiple recent systems biology studies that leverage data from different types of -omics analyses. While most of these studies rely on well-established model organisms, the concepts for integrating -omics data that were developed in these studies can help to enable systems research in the field of cancer biology.

Highlights

Our ability to acquire highly multivariate datasets (“omics” datasets) on biological systems has increased tremendously in recent years
Multi-omics studies in cancer Multiple cancer-related studies have been published in recent years that use more than one type of -omics data set, including metabolomics data
One popular approach to co-analyzing -omics datasets is enrichment analysis [2,3,4,5,6]. This technique evaluates the statistical overrepresentation of a priori-assigned gene ontology (GO) terms among a group of metabolites, genes, or enzymes that are found to be different between conditions or between cancer tissue and neighboring healthy tissue

Summary

Introduction

Our ability to acquire highly multivariate datasets (“omics” datasets) on biological systems has increased tremendously in recent years. A second frequently used approach to co-analyzing multi-omics data has been to plot the independently generated results onto a known (metabolic) network [3,4,5, 7, 8], which is a network function study approach.

Results

Conclusion