TY - JOUR
T1 - Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases
AU - Gorenshteyn, Dmitriy
AU - Zaslavsky, Elena
AU - Fribourg, Miguel
AU - Park, Christopher Y.
AU - Wong, Aaron K.
AU - Tadych, Alicja
AU - Hartmann, Boris M.
AU - Albrecht, Randy A.
AU - García-Sastre, Adolfo
AU - Kleinstein, Steven H.
AU - Troyanskaya, Olga G.
AU - Sealfon, Stuart C.
N1 - Funding Information:
We thank Drs. Judy Cho and Gareth John for helpful discussions or manuscript comments, Nada Marjanovic for technical support, and the Icahn School of Medicine qPCR Core Facility. Supported by NIH Contract HHSN272201000054C and Grant 1U19AI117873. O.G.T. is a Senior Fellow of CIFAR. M.F. was supported by T32 MH096678.
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2015/9/15
Y1 - 2015/9/15
N2 - 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. The large amount of publically available high-throughput data contains, in aggregate, a vast amount of immunologically relevant insight. Sealfon and colleagues report ImmuNet, a web-accessible public resource based on 38,088 experiments that allows researchers to predict gene-gene relationships relevant to the human immune system and immunological diseases.
AB - 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. The large amount of publically available high-throughput data contains, in aggregate, a vast amount of immunologically relevant insight. Sealfon and colleagues report ImmuNet, a web-accessible public resource based on 38,088 experiments that allows researchers to predict gene-gene relationships relevant to the human immune system and immunological diseases.
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U2 - 10.1016/j.immuni.2015.08.014
DO - 10.1016/j.immuni.2015.08.014
M3 - Article
C2 - 26362267
AN - SCOPUS:84941733052
SN - 1074-7613
VL - 43
SP - 605
EP - 614
JO - Immunity
JF - Immunity
IS - 3
M1 - 3157
ER -