Abstract
MOTIVATION: Protein-protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein-protein interaction data analysis. RESULTS: We performed topological analysis of three protein-protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson's disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein-protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein-protein interactions and their multi-omics consequences. AVAILABILITY AND IMPLEMENTATION: A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb. CONTACT: alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.