Big Data in Gastroenterology Research

Abstract

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Keywords: epigenomics; genomics; gut microbiome; medical informatics; metabolomics; proteomics; transcriptomics.

Figure 1

A pipeline for multi-omics data generation and analysis. Luminal GI, hepatobiliary, and pancreatic tissue can be sampled, homogenized, and used to generate multiple types of data from the same sample, such as DNA and RNA sequencing, as well as metabolomic and proteomic mass spectrometry-based and NMR-based data. These data can then be quality checked, cleaned, and processed into final datasets which can then be incorporated into a set of integrative analyses. Created with BioRender.com. novel hypotheses. Created with BioRender.com. D, day; Wk, week.

Figure 2

Example of a research design illustrating the benefits of using longitudinal multi-omics data in the context of evaluating changes in IBD treatment. Using stool and biopsy samples from multiple sites in the colon permits assessment of localized biosignatures that can be correlated for the development of diagnostics and therapeutics. Both pre- and post-treatment analyses facilitate the detection of biosignatures predicting therapeutic response. Using multi-omics data permits the inclusion of changes in the microbiome with genomic and metabolomic data—a holistic approach is also likely to generate this.