# Multifaceted human gut microbiome data associated with health and nutrition

## TL;DR

This review discusses how gut microbiome data related to health and nutrition are scattered and need better standardization for improved research and therapies.

## Contribution

The paper proposes using FAIR data principles and semantic technologies to enhance microbiome data integration and reuse.

## Key findings

- Microbiome data are scattered across databases with varying curation and standardization.
- FAIR principles can improve data discovery and support new scientific hypotheses.
- Semantic classification and ontologies can enhance metadata enrichment and alignment.

## Abstract

The microbiome, also considered the hidden organ, is a fundamental ecosystem directly associated with the disease and health status of the human body. With the availability of high-throughput DNA sequencing technologies, a growing number of studies from clinical and experimental (observation and intervention) samples are constantly revealing new findings on the relationship between human organs and their microbiomes. In such a context, diet and nutrition are among the key factors influencing microbiome composition, richness, and functional behavior. In this review, we illustrate how microbiome-related data and associated metadata are in recent times scattered across primary and specialized databases with different levels of curation, annotation, and standardization, limiting, to some extent, the possibility of deep data discovery, reuse, alignment, and harmonization. Therefore, we describe the way Findable, Accessible, Interoperable, and Reusable (FAIR) data principles would enhance the onset of novel scientific hypotheses and potential microbiome-targeted therapies by improving the standardization policies in data sources. Accordingly, using advanced semantic classification and data mining technologies based on suitable and comprehensive ontologies, annotations of studies present in source databases or in scientific literature would further improve the data and metadata enrichment, integration and alignment relevant to microbiome data associated with health, disease and nutrition.