ariadne: enriching your 'omics analyses with prior knowledge from public databases

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. Leveraging this knowledge in the analysis can improve robustness and interpretation of results. 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. In this poster tutorial, we will discuss strategies to incorporate information from large databases such as KEGG, UniProt and ENZYME (ec). Using an example gut microbiome study with both metagenomic and metabolomic measurements, we demonstrate how ariadne, an R package we developed, can be used to conveniently retrieve, wrangle and integrate prior knowledge with your datasets. We further demonstrate how this strategy can be used to leverage domain expertise, in this case microbial ecology, to ask more precise questions of your data. The aim of this poster tutorial is to provide a simple and way to integrate biological knowledge into new and existing analytical routines.