Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments

A knowledge-based grouping of genes into pathways or functional units is essential for describing and understanding cellular complexity. However, it is not always clear a priori how and at what level of specificity functionally interconnected genes should be partitioned into pathways, for a given application. Here, we assess and compare nine existing and two conceptually novel functional classification systems, with respect to their discovery power and generality in gene set enrichment testing. We base our assessment on a collection of nearly 2000 functional genomics datasets provided by users of the STRING database. With these real-life and diverse queries, we assess which systems typically provide the most specific and complete enrichment results. We find many structural and performance differences between classification systems. Overall, the well-established, hierarchically organized pathway annotation systems yield the best enrichment performance, despite covering substantial parts of the human genome in general terms only. On the other hand, the more recent unsupervised annotation systems perform strongest in understudied areas and organisms, and in detecting more specific pathways, albeit with less informative labels.

Automated summary

A knowledge-based grouping of genes into pathways or functional units is crucial for understanding cellular complexity. This study evaluates and compares existing and novel functional classification systems in terms of their discovery power and generality in gene set enrichment testing. The results show structural and performance differences between classification systems, with well-established, hierarchically organized pathway annotation systems performing best overall. On the other hand, recent unsupervised annotation systems excel in understudied areas and organisms, detecting more specific pathways.