Multi-omics data integration considerations and study design for biological systems and disease

With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.

Automated summary

This article discusses the increasing demand for merging biological features to study a system as a whole, due to advancements in next-generation sequencing and mass spectrometry. Technological limitations such as sample preparation steps, material amount required for sequencing, and sequencing depth requirements need to be addressed. The development of data integration methods, considerations for each data feature, limitations in gene and protein abundance, and the influence of the microbiome on gene and protein expression are important aspects in studying multi-omics data integration.