PT - JOURNAL ARTICLE AU - Jing Zhang AU - Jason Liu AU - Donghoon Lee AU - Shaoke Lou AU - Zhanlin Chen AU - Gamze Gürsoy AU - Mark Gerstein TI - DiNeR: a <em>Di</em>fferential Graphical Model for analysis of co-regulation <em>Ne</em>twork <em>R</em>ewiring AID - 10.1101/2020.05.29.124164 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.05.29.124164 4099 - http://biorxiv.org/content/early/2020/05/30/2020.05.29.124164.short 4100 - http://biorxiv.org/content/early/2020/05/30/2020.05.29.124164.full AB - Background During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. “co-binding changes”) can affect the co-regulating associations between TFs (i.e. “rewiring the co-regulator network”). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied.Methods To address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease.Results We assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes.Conclusions Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.Competing Interest StatementThe authors have declared no competing interest.Abbreviations:DiNeRDifferential Graphical Model of co-regulation Network Rewiring to Infer Transcription Factor Co-binding AlterationsTFTranscription factorCMLchronic myeloid leukemiaGGMGaussian graphical modelChIP-seqChromatin immunoprecipitation followed by sequencingStARSStability Approach to Regularization SelectionTCGAThe Cancer Genome Atlas