Towards a reproducible interactome: semantic-based detection of redundancies to unify protein–protein interaction databases

Abstract

Motivation Information on protein-protein interactions is collected in numerous primary databases with their own curation process. Several meta-databases aggregate primary databases to provide more exhaustive datasets. In addition to exhaustivity, aggregation contributes to reliability by providing an overview of the various studies and detection methods supporting an interaction. However, interactions listed in different primary databases are partly redundant because some publications reporting protein-protein interactions have been curated by multiple primary databases. Mere aggregation can thus introduce a bias if these redundancies are not identified and eliminated. To overcome this bias, meta-databases rely on the Molecular Interaction ontology that describes interaction detection methods, but they do not fully take advantage of the ontology’s rich semantics, which leads to systematically overestimating interaction reproducibility. Results We propose a precise definition of explicit and implicit redundancy, and show that both can be easily detected using Semantic Web technologies. We apply this process to a dataset from the APID meta-database and show that while explicit redundancies were detected by the APID aggregation process, about 15% of APID entries are implicitly redundant and should not be taken into account when presenting confidence-related metrics. More than 90% of implicit redundancies result from the aggregation of distinct primary databases, while the remaining occurs between entries of a single database. Finally, we build a” reproducible interactome” with interactions that have been reproduced by multiple methods or publications. The size of the reproducible interactome is drastically impacted by removing redundancies for both yeast (-59%) and human (-56%), and we show that this is largely due to implicit redundancies. Availability Software, data and results are available at https://gitlab.com/nnet56/reproducible-interactome, https://reproducible-interactome.genouest.org/, Zenodo (doi : 10.5281/zenodo.5595037) and NDEx (doi : 10.18119/N94302, doi : 10.18119/N97S4D Supplementary information Supplementary data are available at Bioinformatics online.

PhyloString: A web server designed to identify, visualize, and evaluate functional relationships between orthologous protein groups across different phylogenetic lineages

“…Modules (1,1), (1,2), (1,3), and (1,5) in the heatmap shown in Fig 4 illustrate the presence of essential proteins involved in the RNA polymerase II transcription initiation. In contrast, modules (2,1), (2,2), and (2,3) cluster proteins with less significant relationships.…”

“…Modules (1,1), (1,2), (1,3), and (1,5) in the heatmap shown in Fig 4 illustrate the presence of essential proteins involved in the RNA polymerase II transcription initiation. In contrast, modules (2,1), (2,2), and (2,3) cluster proteins with less significant relationships. These proteins are linked to various functions, including interactions with vitamin receptors, histone acetyltransferases, and proteins with annotations as "uncharacterized conserved protein".…”

“…The creation and maintenance of these databases involve methodological and conceptual challenges such as utilizing standardized vocabularies and formats for protein and interaction descriptions, meticulous curation of information from scientific publications, and establishing a consistent approach for identifying the same protein across different databases. These challenges and a review of the most significant protein-protein interaction databases have been extensively discussed in [1][2][3].…”