Supporting data for "Suggesting disease associations for overlooked metabolites using literature from metabolic neighbours"

In human health research, metabolic signatures extracted from metabolomics data are a strong-added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and scientific literature. However, we show that the vast majority of compounds (&gt; 99%) in the PubChem database lack annotated literature. This dearth of available information can have a direct impact on the interpretation of metabolic signatures, which is often restricted to a subset of significant metabolites. To suggest potential pathological phenotypes related to these overlooked metabolites which lack annotated literature, we extend the guilt by association principle to literature information by using a Bayesian framework. The underlying assumption is that the literature associated with the metabolic neighbours of a compound can provide valuable insights, or an <i>a prior</i>, into its biomedical context. The metabolic neighbourhood of a compound can be defined from a metabolic network and correspond to metabolites to which it is connected through biochemical reactions. With the proposed approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM FTP server (See information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation).