Transcription factors (TFs) bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 TFs in 458 ChIP-Seq experiments. We found the combinatorial, co-association of TFs to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the TF binding into a hierarchy and integrated it with other genomic information (e.g. miRNA regulation), forming a dense meta-network. Factors at different levels have different properties: for instance, top-level TFs more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs -- e.g. noise-buffering feed-forward loops. Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (i.e., differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

To develop a catalog of regulatory sites in two major model organisms, and, the modERN (model organism Encyclopedia of Regulatory Networks) consortium has systematically assayed the binding sites of transcription factors (TFs). Combined with data produced by our predecessor, modENCODE (Model Organism ENCyclopedia Of DNA Elements), we now have data for 262 TFs identifying 1.23 M sites in the fly genome and 217 TFs identifying 0.67 M sites in the worm genome. Because sites from different TFs are often overlapping and tightly clustered, they fall into 91,011 and 59,150 regions in the fly and worm, respectively, and these binding sites span as little as 8.7 and 5.8 Mb in the two organisms. Clusters with large numbers of sites (so-called high occupancy target, or HOT regions) predominantly associate with broadly expressed genes, whereas clusters containing sites from just a few factors are associated with genes expressed in tissue-specific patterns. All of the strains expressing GFP-tagged TFs are available at the stock centers, and the chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center and also through a simple interface (http://epic.gs.washington.edu/modERN/) that facilitates rapid accessibility of processed data sets. These data will facilitate a vast number of scientific inquiries into the function of individual TFs in key developmental, metabolic, and defense and homeostatic regulatory pathways, as well as provide a broader perspective on how individual TFs work together in local networks and globally across the life spans of these two key model organisms.

Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TFbinding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner.[Supplemental material is available for this article.]Transcription factors (TFs) are critical for the transcriptional regulation of gene expression (Takahashi and Yamanaka 2006;Vaquerizas et al. 2009). In humans, they represent the largest family of proteins, accounting for around 10% of genes (Babu et al. 2004). There are two types of TFs: general and sequence-specific. The former TFs act cooperatively with RNA polymerase II and are ubiquitously involved in the transcription of a large fraction of genes (Lee and Young 2000). The latter TFs bind specific subsets of target genes, leading to distinct spatiotemporal patterns of gene expression (Kadonaga 2004). Although systematic gene expression quantification has been available for a decade from microarray experiments (Schena et al. 1995), only recently has the genome-wide identification of TF-binding sites become possible, owing to the development of chromatin immunoprecipitation followed by microarray (ChIP-chip) and sequencing (ChIP-seq) technologies (Ren et al. 2000;Johnson et al. 2007).In several previous studies, statistical models were constructed to study the regulatory functions of TF on gene expression based on the gene expression and TF-binding data (Ouyang et al. 2009;Cheng and Gerstein 2011). These studies showed that TFbinding signals around the transcription start sites (TSSs) of genes are predictive of gene expression levels with fairly high accuracy. But these studies have the following limitations: First, estimates of gene expression have relied on probes (microarray) or sequence reads (RNA-seq) spread across a gene, possibly across multiple unknown isoforms of that gene. It is often difficult to accurately determine the expression level of each transcript based on such data, which...

We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs. We demonstrate the prediction in a variety of contexts, focusing particularly on the modENCODE worm datasets. Moreover, our framework reveals the positional contribution around genes (upstream or downstream) of distinct chromatin features to the overall prediction of expression levels.