iRegNet3D: three-dimensional integrated regulatory network for the genomic analysis of coding and non-coding disease
Siqi Liang1,2, Nathaniel D Tippens1,2, Yaoda Zhou1,2
1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA.
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The mechanistic details of most disease-causing mutations remain poorly explored within the context of regulatory networks. We present a high-resolution three-dimensional integrated regulatory network (iRegNet3D) in the form of a web tool, where we resolve the interfaces of all known transcription factor (TF)-TF, TF-DNA and chromatin-chromatin interactions for the analysis of both coding and non-coding disease-associated mutations to obtain mechanistic insights into their functional impact. Using iRegNet3D, we find that disease-associated mutations may perturb the regulatory network through diverse mechanisms including chromatin looping. iRegNet3D promises to be an indispensable tool in large-scale sequencing and disease association studies.
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