Cell lines were not tested for mycoplasma contamination. Commonly misidentified lines (See ICLAC register) No commonly misidentified cell lines were used.

4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or “bait”) that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

ENCODE 3 (2012-2017) expanded production and added new types of assays 8 (Fig. 1, Extended Data Fig. 1), which revealed landscapes of RNA binding and the 3D organization of chromatin via methods such as chromatin interaction analysis by paired-end tagging (ChIA-PET) and Hi-C chromosome conformation capture. Phases 2 and 3 delivered 9,239 experiments (7,495 in human and 1,744 in mouse) in more than 500 cell types and tissues, including mapping of transcribed regions and transcript isoforms, regions of transcripts recognized by RNA-binding proteins, transcription factor binding regions, and regions that harbour specific histone modifications, open chromatin, and 3D chromatin interactions. The results of all of these experiments are available at the ENCODE portal (http://www.encodeproject.org). These efforts, combined with those of related projects and many other laboratories, have produced a greatly enhanced view of the human genome (Fig. 2), identifying 20,225 protein-coding and 37,595 noncoding genes

SUMMARY B cell development is a tightly regulated process dependent on sequential rearrangements of immunoglobulin loci that encode the antigen receptor. To elucidate the role of microRNAs (miRNAs) in the orchestration of B cell development, we ablated all miRNAs at the earliest stage of B cell development by conditionally targeting the enzymes critical for RNAi in early B cell precursors. Absence of any one of these enzymes led to a block at the pro- to pre-B cell transition due to increased apoptosis and a failure of pre-B cells to proliferate. Expression of a Bcl2 transgene allowed for partial rescue of B cell development, however, the majority of the rescued B cells had low surface immunoglobulin expression with evidence of ongoing light chain editing. Our analysis revealed that miRNAs are critical for the regulation of the PTEN-AKT-FOXO1 pathway that in turn controls Rag expression during B cell development.

in ultraconserved enhancers result in very subtle but still selectively disadvantageous effects on gene expression, or additional, yet-to-be-identified, regulatory or nonregulatory functions of these sequences contribute to their extreme conservation in evolution.