Ariadne's thread: Navigating the maze of Biology (without getting lost)

With the continuous advancement of high-throughput techniques in the biological sciences, large multi-modal datasets are becoming increasingly prevalent. In tandem, the research community has developed extensive and interconnected knowledge databases capturing known relations within and across data modalities.

However, such relational information between data modalities remains unused unless it is organised into a searchable format and made publicly accessible through online databases, thereby transforming untidy data into new usable knowledge. Although this knowledge is widely available across numerous public databases, it is often difficult to access and even more challenging to integrate into a typical ‘omic data analysis workflow. 

To this end, we developed ariadne, an R package providing tools to retrieve, wrangle and integrate knowledge from both public (e.g., ChocoPhlAn, Web of Life and UniProt) and user-defined databases, linking microbial features not only to their genetic makeup but also to the metabolites they produce, their biological functions, and ultimately their impact on host’s health and the environment. Our package relies on graph theory and the novel MultiFactor class, designed for optimised storage and efficient translation of relational information. When possible, the SPARQL semantic query language is used to retrieve knowledge by flexible and rapid queries to online databases. 

This framework interoperates with and enhances the SummarizedExperiment-based ecosystem by integrating knowledge into analytical routines such as module-wise and microbe set enrichment analyses — an area that has, until now, lacked easy access to such valuable resources.