Chromosomal Mapping: The conformation of the genome in the nucleus and contacts between both proximal and distal loci influence gene expression. In order to map genomic contacts, Lieberman-Aiden et al. (p. 289 , see the cover) developed a technique to allow the detection of all interactions between genomic loci in the eukaryotic nucleus followed by deep sequencing. This technology was used to map the organization of the human genome and to examine the spatial proximity of chromosomal loci at one megabase resolution. The map suggests that the genome is partitioned into two spatial compartments that are related to local chromatin state and whose remodeling correlates with changes in the chromatin state.

The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE¹ and Roadmap Epigenomics² data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, performs quality controls and generates intra- and inter-chromosomal contact maps. It includes a fast implementation of the iterative correction method and is based on a memory-efficient data format for Hi-C contact maps. In addition, HiC-Pro can use phased genotype data to build allele-specific contact maps. We applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time. Source code and documentation are available at http://github.com/nservant/HiC-Pro .

Transcription factors control cell-specific gene expression programs through interactions with diverse coactivators and the transcription apparatus. Gene activation may involve DNA loop formation between enhancer-bound transcription factors and the transcription apparatus at the core promoter, but this process is not well understood. Here we report that mediator and cohesin physically and functionally connect the enhancers and core promoters of active genes in murine embryonic stem cells. Mediator, a transcriptional coactivator, forms a complex with cohesin, which can form rings that connect two DNA segments. The cohesin-loading factor Nipbl is associated with mediator–cohesin complexes, providing a means to load cohesin at promoters. DNA looping is observed between the enhancers and promoters occupied by mediator and cohesin. Mediator and cohesin co-occupy different promoters in different cells, thus generating cell-type-specific DNA loops linked to the gene expression program of each cell.

ICE (iterative correction and eigenvector decomposition) provides insight into inter- and intrachromosome interaction patterns. Extracting biologically meaningful information from chromosomal interactions obtained with genome-wide chromosome conformation capture (3C) analyses requires the elimination of systematic biases. We present a computational pipeline that integrates a strategy to map sequencing reads with a data-driven method for iterative correction of biases, yielding genome-wide maps of relative contact probabilities. We validate this ICE (iterative correction and eigenvector decomposition) technique on published data obtained by the high-throughput 3C method Hi-C, and we demonstrate that eigenvector decomposition of the obtained maps provides insights into local chromatin states, global patterns of chromosomal interactions, and the conserved organization of human and mouse chromosomes.