BICORN: An R package for integrative inference of de novo cis-regulatory modules

Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.

[1]  Roded Sharan,et al.  CREME: Cis-Regulatory Module Explorer for the human genome , 2004, Nucleic Acids Res..

[2]  Ernest Fraenkel,et al.  ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data , 2011, Nucleic Acids Res..

[3]  Christian J Stoeckert,et al.  Clustering of genes into regulons using integrated modeling-COGRIM , 2007, Genome Biology.

[4]  ENCODEConsortium,et al.  An Integrated Encyclopedia of DNA Elements in the Human Genome , 2012, Nature.

[5]  Susanne Bornelöv,et al.  The Nucleosome Remodeling and Deacetylation Complex Modulates Chromatin Structure at Sites of Active Transcription to Fine-Tune Gene Expression , 2018, Molecular cell.

[6]  M. Scharfe,et al.  Differential roles for MBD2 and MBD3 at methylated CpG islands, active promoters and binding to exon sequences , 2013, Nucleic acids research.

[7]  Daniel S. Day,et al.  YY1 Is a Structural Regulator of Enhancer-Promoter Loops , 2017, Cell.

[8]  H. Aburatani,et al.  Cohesin mediates transcriptional insulation by CCCTC-binding factor , 2008, Nature.

[9]  Mikhail G Dozmorov,et al.  Polycomb repressive complex 2 epigenomic signature defines age-associated hypermethylation and gene expression changes , 2015, Epigenetics.

[10]  Dario Floreano,et al.  Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods , 2009, J. Comput. Biol..

[11]  Howard Y. Chang,et al.  ATAC‐seq: A Method for Assaying Chromatin Accessibility Genome‐Wide , 2015, Current protocols in molecular biology.

[12]  Xiao Wang,et al.  CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large‐scale ChIP‐seq and time‐course RNA‐seq data , 2018, Bioinform..

[13]  Fang-Xiang Wu,et al.  Properties of sparse penalties on inferring gene regulatory networks from time-course gene expression data. , 2015, IET systems biology.

[14]  M. Karin,et al.  AP-1 as a regulator of cell life and death , 2002, Nature Cell Biology.

[15]  Manolis Kellis,et al.  Chromatin-state discovery and genome annotation with ChromHMM , 2017, Nature Protocols.

[16]  U. Alon,et al.  Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[17]  C. Glass,et al.  The selection and function of cell type-specific enhancers , 2015, Nature Reviews Molecular Cell Biology.

[18]  Derek W Wright,et al.  Gateways to the FANTOM5 promoter level mammalian expression atlas , 2015, Genome Biology.

[19]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[20]  Xing-Ming Zhao,et al.  NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference , 2013, Bioinform..

[21]  Michael Wegner,et al.  Sox13 functionally complements the related Sox5 and Sox6 as important developmental modulators in mouse spinal cord oligodendrocytes , 2016, Journal of neurochemistry.

[22]  Neil D. Lawrence,et al.  Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays , 2015, Proceedings of the National Academy of Sciences.

[23]  P. Farnham,et al.  The identification of E2F1-specific target genes , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Chiara Sabatti,et al.  Bayesian sparse hidden components analysis for transcription regulation networks , 2005, Bioinform..

[25]  Ryan Dale,et al.  Cell type specificity of chromatin organization mediated by CTCF and cohesin , 2010, Proceedings of the National Academy of Sciences.

[26]  Peter J Park,et al.  A dynamic H3K27ac signature identifies VEGFA-stimulated endothelial enhancers and requires EP300 activity , 2013, Genome research.

[27]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[28]  Feng Zhang,et al.  Enhancer Activation Requires trans-Recruitment of a Mega Transcription Factor Complex , 2014, Cell.

[29]  Jie Zhou,et al.  RNA-seq differential expression studies: more sequence or more replication? , 2014, Bioinform..

[30]  Cory Y. McLean,et al.  GREAT improves functional interpretation of cis-regulatory regions , 2010, Nature Biotechnology.

[31]  S. Aerts,et al.  Discovery of Transcription Factors and Regulatory Regions Driving In Vivo Tumor Development by ATAC-seq and FAIRE-seq Open Chromatin Profiling , 2015, PLoS genetics.

[32]  Chiara Sabatti,et al.  Network component analysis: Reconstruction of regulatory signals in biological systems , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Junwen Wang,et al.  Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods. , 2014, Methods.

[34]  Xi Chen,et al.  mAPC-GibbsOS: an integrated approach for robust identification of gene regulatory networks , 2013, BMC Systems Biology.

[35]  P. Park.  ChIP–seq: advantages and challenges of a maturing technology , 2009, Nature Reviews Genetics.

[36]  V. Hakim,et al.  Genome-wide identification of cis-regulatory motifs and modules underlying gene coregulation using statistics and phylogeny , 2010, Proceedings of the National Academy of Sciences.

[37]  David M. Livingston,et al.  A Complex with Chromatin Modifiers That Occupies E2F- and Myc-Responsive Genes in G0 Cells , 2002, Science.

[38]  Ying Wang,et al.  ChIPModule: Systematic Discovery of Transcription Factors and Their Cofactors from ChIP-seq Data , 2012, Pacific Symposium on Biocomputing.

[39]  Xi Chen,et al.  Reconstruction of Transcriptional Regulatory Networks by Stability-Based Network Component Analysis , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[40]  Bin Yan,et al.  CMGRN: a web server for constructing multilevel gene regulatory networks using ChIP-seq and gene expression data , 2014, Bioinform..

[41]  P. Geurts,et al.  Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.

[42]  Sarah A. Teichmann,et al.  Assessing Computational Methods of Cis-Regulatory Module Prediction , 2010, PLoS Comput. Biol..

[43]  W. Wong,et al.  CisModule: de novo discovery of cis-regulatory modules by hierarchical mixture modeling. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Andreas R. Pfenning,et al.  High-throughput functional comparison of promoter and enhancer activities , 2016, Genome research.