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

Autor: Gable AL; Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland., Szklarczyk D; Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland., Lyon D; Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland., Matias Rodrigues JF; Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland., von Mering C; Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland.; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
Jazyk: angličtina
Zdroj: Briefings in bioinformatics [Brief Bioinform] 2022 Sep 20; Vol. 23 (5).
DOI: 10.1093/bib/bbac355
Abstrakt: 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.
(© The Author(s) 2022. Published by Oxford University Press.)
Databáze: MEDLINE
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