Editorial: Multi-omic data integration

Big Data in Gastroenterology Research

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.

DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project

PRIMAGE is a European Commission-financed project dealing with medical imaging and artificial intelligence aiming to create an imaging biobank in oncology. The project includes a task dedicated to the interoperability between imaging and standard biobanks. We aim at linking Digital imaging and Communications in Medicine (DICOM) metadata to the Minimum Information About BIobank data Sharing (MIABIS) standard of biobanking. A very first integration model based on the fusion of the two existing standards, MIABIS and DICOM, has been developed. The fundamental method was that of expanding the MIABIS core to the imaging field, adding DICOM metadata derived from CT scans of 18 paediatric patients with neuroblastoma. The model was developed with the relational database management system Structured Query Language. The integration data model has been built as an Entity Relationship Diagram, commonly used to organise data within databases. Five additional entities have been linked to the “Image Collection” subcategory in order to include the imaging metadata more specific to the particular type of data: Body Part Examined, Modality Information, Dataset Type, Image Analysis, and Registration Parameters. The model is a starting point for the expansion of MIABIS with further DICOM metadata, enabling the inclusion of imaging data in biorepositories.

Review of otitis media microbiome studies: What do they tell us?

OBJECTIVES

To provide a state of the art review on accruing studies focused on defining the middle ear microbiome, highlighting the relationship of the microbiome to disease pathophysiology.

DATA SOURCES

Pubmed indexed peer-reviewed articles and published textbooks.

REVIEW METHODS

Comprehensive review of the literature using the following search terms: "microbiome" "bacterial pathogens" with the term "otitis media," and "middle ear."

RESULTS

A multitude of microbiome studies have been published in the recent past. In general findings from these studies underscore distinct profiles based on disease category. The adenoidal reservoir theory may not explain all etiologies of middle ear effusion production. The host immune system appears to be associated to the bacterial population identified in the middle ear space. Atopic respiratory diseases correlate to the middle ear microbiome. Some novel middle ear bacterial genera may be protective in terms of disease.

CONCLUSION

The understanding of otitis media disease progression pathophysiology is evolving, informed by accruing middle ear microbiomic data. The functional implications of middle ear microbiome findings need to be studied further. This may help counterbalance probiotic vs antibiotic approaches to disease mitigation.

Multi-omics integration-a comparison of unsupervised clustering methodologies

With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.

Dynamical systems approaches to personalized medicine

The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution.

‘Omics’: an emerging field in pain research and management

Precision medicine is an emerging approach for prevention and treatment of diseases considering individuals’ uniqueness. Omics provide one step forward toward advanced precision medicine and include technologies such as genomics, proteomics and metabolomics generating valuable data through characterization of entire biological systems. With the aid of omics, a major shift has been started to occur in understanding of diseases followed by potential fundamental changes in medical care strategies. This short review aims at providing some examples of current omics that are applied in the field of pain in terms of new biomarkers for diagnosis of different pain types, stratification of patients and new therapeutic targets. Implementation of omics would most likely offer breakthrough in the future of pain management.