Network- and Enrichment-based Inference of Phenotypes and Targets from large-scale Disease Maps
Disease maps have emerged as computational knowledge bases for exploring and modeling diseasespecific molecular processes. By capturing molecular interactions, disease-associated processes, and phenotypes in standardized representations, disease maps provide a platform for applying bioinformatics and systems biology approaches. Applications range from simple map exploration to algorithm-driven target discovery and network perturbation. The web-based MINERVA environment for disease maps provides a platform to develop tools not only for mapping experimental data but also to identify, analyze and simulate disease-specific regulatory networks. We have developed a MINERVA plugin suite based on network topology and enrichment analyses that facilitate multi-omics data integration and enable in silico perturbation experiments on disease maps. We demonstrate workflows by analyzing two RNA-seq datasets on the Atlas of Inflammation Resolution (AIR). Our approach improves usability and increases the functionality of disease maps by providing easy access to available data and integration of selfgenerated data. It supports efficient and intuitive analysis of omics data, with a focus on disease maps.