Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes

摘要: Motivation Differential network analysis, designed to highlight changes between conditions, is an important paradigm in biology. However, differential analysis methods have been typically compare two conditions and were rarely applied multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation lacking. Results Here, we present a framework assessing the ability of human tissue interactomes tissue-selective processes disorders. For this, created benchmark 6499 curated tissue-specific Gene Ontology biological processes. We five methods, including four construct weighted 34 tissues. Rigorous assessment this revealed that perform well revealing (AUCs 0.82-0.9). Next, illuminate genes underlying hereditary dataset 1305 disorders manifesting Focusing on subnetworks containing top 1% interactions disease-relevant significant enrichment disorder-causing 18.6% cases, with significantly high success rate blood, nerve, muscle heart diseases. Summary Altogether, offer includes expansive manually datasets be used as or genes. Our results demonstrate powerful tool highlighting functionality clinical impact. Availability implementation Datasets are available part Supplementary data. information data at Bioinformatics online.