Investigating immunological pathways and diseases with a comprehensive compendium of human data

The exponential growth of high throughput immunological data motivates the need to leverage this global effort to guide new investigation and to contextualize domain-specific results. Improved specificity and accuracy over currently utilized approaches such as ontological analysis would be advantageous. To develop such a resource (ImmuNet), we used immunological pathway-guided Bayesian integration of a comprehensive, heterogeneous compendium of human data to assemble functional relationship networks. The utility of ImmuNet was evaluated in various applications. We studied the cellular gene response signature to virus infection, using ImmuNet to guide confirmatory experiments into functionally relevant responses to different viruses. We also demonstrated that ImmuNet can accurately predict disease associated genes in the vicinity of GWAS loci and to explore processes and pathways underlying immune-mediated diseases. By unlocking immunological information captured within the global biomedical research effort, ImmuNet should be widely beneficial to future studies investigating the mechanisms of the human immune system and immunological disease. An interactive user-friendly web interface for investigators is publicly available at immunet.princeton.edu.