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Immunity. 2015 Sep 15;43(3):605-14. doi: 10.1016/j.immuni.2015.08.014. Epub 2015 Sep 8.

Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases.

Author information

1
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
2
Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
3
New York Genome Center, 101 Avenue of the Americas, New York, NY 10013, USA.
4
Simons Center for Data Analysis, Simons Foundation, New York, NY 10010, USA.
5
Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
6
Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
7
Departments of Pathology and Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA; Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.
8
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Simons Center for Data Analysis, Simons Foundation, New York, NY 10010, USA; Department of Computer Science, Princeton University, Princeton, NJ 08540, USA. Electronic address: ogt@cs.princeton.edu.
9
Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. Electronic address: stuart.sealfon@mssm.edu.

Abstract

Many functionally important interactions between genes and proteins involved in immunological diseases and processes are unknown. The exponential growth in public high-throughput data offers an opportunity to expand this knowledge. To unlock human-immunology-relevant insight contained in the global biomedical research effort, including all public high-throughput datasets, we performed immunological-pathway-focused Bayesian integration of a comprehensive, heterogeneous compendium comprising 38,088 genome-scale experiments. The distillation of this knowledge into immunological networks of functional relationships between molecular entities (ImmuNet), and tools to mine this resource, are accessible to the public at http://immunet.princeton.edu. The predictive capacity of ImmuNet, established by rigorous statistical validation, is easily accessed by experimentalists to generate data-driven hypotheses. We demonstrate the power of this approach through the identification of unique host-virus interaction responses, and we show how ImmuNet complements genetic studies by predicting disease-associated genes. ImmuNet should be widely beneficial for investigating the mechanisms of the human immune system and immunological diseases.

PMID:
26362267
PMCID:
PMC4753773
DOI:
10.1016/j.immuni.2015.08.014
[Indexed for MEDLINE]
Free PMC Article
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