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

3Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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.

Cite

CITATION STYLE

APA

Gable, A. L., Szklarczyk, D., Lyon, D., Matias Rodrigues, J. F., & Von Mering, C. (2022). Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments. Briefings in Bioinformatics, 23(5). https://doi.org/10.1093/bib/bbac355

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free