OCEAN-C: mapping hubs of open chromatin interactions across the genome reveals gene regulatory networks
© The Author(s). 2018
Received: 3 February 2018
Accepted: 30 March 2018
Published: 24 April 2018
We develop a method called open chromatin enrichment and network Hi-C (OCEAN-C) for antibody-independent mapping of global open chromatin interactions. By integrating FAIRE-seq and Hi-C, OCEAN-C detects open chromatin interactions enriched by active cis-regulatory elements. We identify more than 10,000 hubs of open chromatin interactions (HOCIs) in human cells, which are mainly active promoters and enhancers bound by many DNA-binding proteins and form interaction networks crucial for gene transcription. In addition to identifying large-scale topological structures, including topologically associated domains and A/B compartments, OCEAN-C can detect HOCI-mediated chromatin interactions that are strongly associated with gene expression, super-enhancers, and broad H3K4me3 domains.
The local chromatin conformation regulates gene transcriptional activity through facilitating interactions between promoters and distant active regulatory elements such as enhancers, repressors, and silencers [1, 2]. These cis-regulatory elements are loosely packed and relatively free of nucleosomes, which are necessary for transcription factors and other regulatory proteins to gain access to DNA [3–5]. Traditionally, active regulatory elements (open chromatin) can be assayed genome-wide by DNase-hypersensitive sites identified by sequencing (DNase-seq) or formaldehyde-assisted isolation of regulatory elements by sequencing (FAIRE-seq) [6, 7].
Recently, several elaborate methods to identify chromatin interaction maps have been developed, including chromatin interaction analysis by paired-end tag sequencing (ChIA-PET)  and chromosome conformation capture (3C)-based methods , such as 4C [10, 11], 5C , Hi-C , in situ Hi-C , Capture-C , DNase-C , Micro-C , single-cell Hi-C [18, 19], HiChIP , and PLAC-seq . In particular, DNase-C identifies high-confidence DNA contacts at kilobase resolution by using DNase I to digest the genome DNA instead of restriction enzymes [16, 22]. These techniques have greatly advanced our understanding of detailed features of the genome 3D structure and regulation of the genome [6, 23–26]. However, ChIA-PET, HiChIP, and PLAC-seq only determine the subset of interactions mediated by specific DNA-binding proteins, whereas Hi-C captures all genomic interactions indiscriminately, which may flood important contacts between open chromatin and distal regulatory elements.
In order to overcome these limitations, we integrated the FAIRE-seq and in situ Hi-C assays and developed the open chromatin enrichment and network Hi-C (OCEAN-C) method for mapping global open chromatin interactions. By aggregating open chromatin associated with interacting partners through direct phenol-chloroform extraction, OCEAN-C enriched interactions among active cis-regulatory elements, which mainly occurred among promoters and enhancers and thus regulated gene transcription. OCEAN-C is a novel tool for studying open chromatin interactions and their relationship with gene regulation.
Genome-wide open chromatin interaction assay using OCEAN-C
We identified 12,003 OCEAN-C peaks (median of broad size was 1.4 kb and of narrow size 232 bp) with 43.4 million valid read pairs standing for intra-chromsomal interactions in the U266 cell line. Of these, 74.3% overlapped with FAIRE-seq peaks; in contrast, only 850 peaks were determined from the same number of Hi-C reads, which barely had any intersection with OCEAN-C or FAIRE-seq peaks (Fig. 1b). The high ratio of overlap with FAIRE-seq peaks confirmed that the peak regions determined by OCEAN-C are open chromatin regions. Moreover, the OCEAN-C peaks only comprise a small portion (approximately 13%) of the total number of open chromatin regions identified by FAIRE-seq, indicating that most open chromatin regions do not show a significantly higher interaction frequency than other regions. We observed 174 interactions per OCEAN-C peak on average (Fig. 1c), which is significantly higher than the number for Hi-C data (p value < 2.2e-16). Therefore, OCEAN-C peaks represent chromatin interaction hubs that form multiple interactions with a set of loci along the chromosome (Fig. 1d and Additional file 1: Figure S2A), and we name these regions hubs of open chromatin interactions (HOCIs). Correlation analysis using epigenetic markers revealed that HOCIs are mainly occupied by active histone modifications (H3K4me3, approximately 70%; H3K4me1, approximately 50%; and H3K27ac, approximately 50%) at percentages that remarkably exceed those of open chromatin identified by FAIRE-seq and Hi-C peaks (Fig. 1e), demonstrating that HOCIs are mainly active cis-acting elements, especially promoters (H3K4me3) and enhancers (H3K4me1 and H3K27ac).
To further test the reproducibility and feasibility of OCEAN-C, we examined the method in RPMI-8226 multiple myeloma cells and GM12878 lymphoblastoid cells. The three cell lines exhibited similar numbers of HOCIs and similar histone modification properties, demonstrating that HOCIs represent a common phenomenon in different cell lines (Fig. 1f and Additional file 1: Figure S2B). The large difference in the locations of HOCIs between different cell lines is suggestive of specific open chromatin interactions that are associated with gene regulation. Next, we compared the results of OCEAN-C and in situ Hi-C in identifying large-scale chromatin architectures such as topologically associated domains (TADs) and compartments and found that interaction heat maps, TADs, and A/B compartments exhibited high concordance between OCEAN-C and Hi-C (Additional file 1: Figure S2C–F), demonstrating the ability of OCEAN-C to identify the same TADs and A/B compartments as in situ Hi-C. Furthermore, we evaluated the effect of sequencing depth and software packages used on peak calling. The number of HOCIs identified was affected by low sequencing depth and gradually became saturated with increasing read number (Additional file 1: Figure S3A). By using the MACS2 software to call peaks from the OCEAN-C data of U266 cells, we obtained 9926 peaks, 4718 of which overlapped ZINBA-identified peaks, suggesting that the peak signals of open chromatin in OCEAN-C data can be detected by different algorithms and combining different peak-calling methods may be helpful to identify reliable HOCIs (Additional file 1: Figure S3B–E).
We also compared OCEAN-C with the DNase-C technique in identifying open chromatin interactions (Additional file 1: Figure S4). The results showed that while DNase-C method captures open-chromatin interactions at fine-scale, OCEAN-C performs better than DNase-C in peak calling and identifying accurate open chromatin interaction peaks.
HOCIs are bound by a cluster of DNA-binding proteins
As OCEAN-C is designed to capture interactions between open chromatin regions without relying on specific antibodies, we speculated that HOCIs are chromatin regions bound by multiple DNA-binding proteins. To confirm this hypothesis, we integrated ChIP-seq data from ENCODE, ChIA-PET, and OCEAN-C data of GM12878 cells. As expected, chromatin anchors identified by CTCF ChIA-PET displayed much stronger CTCF ChIP-seq signals than any other DNA-binding proteins, and Pol II also exhibited the strongest binding signal at anchors of Pol II ChIA-PET (Fig. 2b), demonstrating the enrichment of specific protein-binding regions in ChIA-PET experiments. In contrast, HOCIs displayed enriched binding signals for a larger set of DNA-binding proteins, including active transcription factors (PKNOX1, Pol II), transcription repressors (BHLHE40, SP1, YY1), transcription regulators (ZNF143, CREB1, GABPA), and CTCF (Fig. 2b). Moreover, several lymphoid cell-specific transcription factors showed strong binding signals, including E74-like factor 1 (ELF1) and Early B-cell factor 1 (EBF1), demonstrating the ability of OCEAN-C to identify key lineage-specific DNA-binding proteins (Fig. 2b). Specifically, the B-cell-specific transcription factor ELF1 showed higher binding signal at HOCIs than other factors except Pol II-related proteins (POL2A, PKNOX1, BHLHE40, ZNF143, and CREB1; Fig. 2c).
On average, a HOCI is occupied by 9.1 different DNA-binding proteins, compared to an average of 6.7, 5.3, and 6.5 different DNA-binding proteins occupying a Pol II ChIA-PET anchor, CTCF ChIA-PET anchor, and Hi-C loop anchor, respectively (Additional file 1: Figure S6). Moreover, the ChIA-PET and Hi-C loop anchors overlapping HOCIs were bound by significantly more DNA-binding proteins than the other anchors (t-test, p value < 2.2e-16; Additional file 1: Figure S6B), demonstrating that ChIA-PET can only capture a portion of HOCIs, which were DNA loop anchors occupied by both ChIA-PET anchor proteins and other DNA-binding proteins. In addition, contour plots showed that HOCIs had shorter width and more binding proteins overall, while most POL2/CTCF ChIA-PET anchors were longer and occupied by less than five different DNA-binding proteins (Additional file 1: Figure S6C). We also analyzed the DNA sequence motifs of HOCIs and ChIA-PET anchors. CTCF ChIA-PET anchors showed extremely enriched CTCF/CTCFL DNA binding motifs, while HOCIs showed less difference in the significance level of the top five enriched motifs, including CTCF/CTCFL (Additional file 1: Figure S6D). Specifically, at the locus of the gene WBP1L, two regions were identified as open chromatin regions by FAIRE-seq, one near the promoter and the other in close proximity to the promoter within the gene body (Fig. 2d). The promoter of WBP1L was identified as a HOCI by OCEAN-C and confirmed by strong binding signals for many DNA-binding proteins, including Pol II but not CTCF, while the second open chromatin region was not identified as a HOCI due to the binding signals of mainly CTCF and Pol II but not other proteins (Fig. 2d). Therefore, the occupancy of multiple proteins and frequent interactions with other chromatin regions distinguishes HOCIs from other open chromatin regions.
To further explore the genomic properties of HOCIs, we analyzed the chromatin states of HOCIs as well as anchors of CTCF or Pol II ChIA-PET in GM12878 cells (Additional file 1: Figure S7A). CTCF anchors were mainly marked as insulators, and Pol II anchors were mainly marked as promoters and enhancers, consistent with the biological function of these two proteins. HOCIs were most commonly identified as promoters (approximately 50%), followed by enhancers (approximately 15%), and insulators (approximately 15%). We clustered HOCIs according to their binding signals of multiple DNA-binding proteins. The results showed that promoter and enhancer HOCIs are occupied by many proteins, whereas insulator HOCIs are occupied by a few proteins, including CTCF, ZNF143, EBF1, and BHLHE40 (Additional file 1: Figure S7B). Meanwhile, HOCIs located within inactive chromatin regions had few interactions with DNA-binding proteins (Additional file 1: Figure S7B). Taken together, these results indicate that HOCIs identified by OCEAN-C are mainly functional cis-regulatory elements that are bound by a cluster of regulatory proteins.
HOCIs form promoter- and enhancer-based topological architectures that associate with gene expression
We next explored the location of HOCIs relative to the hierarchical spatial structures of the genome, including topological associated domains (TADs) and A/B compartments. HOCIs preferentially occurred at TAD boundaries (Fig. 3e, Additional file 3: Table S2B), and HOCI-mediated interactions were mainly within active A compartments (Fig. 3f, h); in contrast, Hi-C interactions occurred abundantly within both A and B compartments (Fig. 3g). These results suggest that HOCI-mediated interactions preferentially involve active chromatin regions, especially TAD boundaries.
HOCI-mediated interactions explain differential gene expression
To specifically illustrate the relationship between open chromatin interactions and gene expression, we selected one differentially expressed gene, Class II major histocompatibility complex transactivator (CIITA), an important gene that participates in B-cell differentiation, and examined the nearby open chromatin interactions, Hi-C heat maps, and RNA-expression levels (Fig. 5c). In U266 cells, the promoter of CIITA was identified as a HOCI that forms multiple interactions with nearby genes, associating with high expression of the gene, whereas such HOCIs and interactions were not detected in RPMI8226 cells, associating with a weak transcription signal of the gene. In contrast, Hi-C heat maps cannot detect such differences at 40-kb resolution. Taken together, we demonstrated that OCEAN-C identified HOCI-mediated open chromatin interactions that are crucial for gene transcription and changes.
Most super-enhancers and many broad H3K4me3 domains overlap with HOCIs
Hi-C-based methods and ChIA-PET have greatly advanced our understanding of the 3D architecture of the nucleus by uncovering TADs, compartments, and chromatin loops. Previous ChIA-PET studies illustrate that promoter–promoter interactions provide a topological basis for transcriptional regulation, and CTCF and cohesin mediate the formation of 3D genome architectures [26, 33]. Several algorithms have been developed to discover chromatin interaction structures such as chromatin interaction hubs , long-range interaction networks , inter-chromosomal chromatin clusters , and active promoter–enhancer associations  by integrating Hi-C data with epigenome and transcriptome data. However, Hi-C requires billions of reads to detect loops, while ChIA-PET and HiChIP are antibody-dependent and thus only capture DNA interactions mediated by specific proteins. Capture-C mainly captures interactions directly involving promoters. To overcome these limitations, we developed the OCEAN-C method, which enriches open chromatin interactions through phenol-chloroform extraction without using antibodies. OCEAN-C can identify sharp open chromatin regions interacting with many other chromatin regions, which we define as HOCIs, and facilitates the study of open chromatin interactions. We show that OCEAN-C is reproducible, time saving (~ 3 days), and has low sequencing costs (~ 100 million read pairs are sufficient to identify 10,000 HOCIs along with TADs and compartments).
The conformation of cis-regulatory elements is as important as their primary sequences with regard to gene regulation. It is important to explore the interactions among cis-regulatory elements such as enhancers, promoters, and insulators to understand how they regulate gene expression. Based on OCEAN-C data, we identify HOCIs as open chromatin interaction hubs with potential regulatory functions. We demonstrate that HOCIs preferentially form open chromatin interactions, including promoter–enhancer, promoter–promoter, and enhancer–enhancer interactions, which distinguish HOCIs from other open chromatin regions. A HOCI often mediates clustered chromatin interactions and can be important for coordinated transcription of multiple genes that are nearby and faraway. In addition, OCEAN-C is feasible for investigating changes in open chromatin conformations, such as promoter–enhancer interactions, that result in differential gene expression. We demonstrate that hub genes whose promoters are HOCIs display the highest transcription activity, and changes in HOCIs between different cell lines are associated with marked changes of transcription. These findings suggest that OCEAN-C is a suitable tool for studying the activation or inactivation of developmental genes or cancer genes due to the changes in chromatin conformation.
Despite these advantages, the current version of OCEAN-C has several areas that could be improved. First, OCEAN-C is based on the Hi-C method, which only captures chromatin interactions near recognition sites of the specific restriction enzyme used. Although the four-base restriction enzymes we used have abundant cutting sites along the genome, they may miss capturing certain chromatin regions. Second, because 1–3% of the total DNA was extracted as chromatin interactions related to open chromatin, OCEAN-C needs ~ 1 million cells in order to obtain sufficient DNA for library construction, which restricts its application for clinical samples. We will continue to develop and improve OCEAN-C to overcome these limitations.
We demonstrate that OCEAN-C is a powerful method for investigating open chromatin interactions and the dynamic of HOCIs in regulating gene transcription.
Cell culture and collection
U266 cells (ATCC TIB-196), RPMI8226 cells (ATCC CCL-155), and GM12878 cells were grown in RPMI-1640 medium containing 10% fetal bovine serum at 37°C and 5% CO2. The cells were cultured to 80–90% confluence and then collected and washed once with PBS. For crosslinking cells, formaldehyde was added at a final concentration of 1% at room temperature (RT) for 10 min, and then quenched with glycine (0.2 M) for 5 min. The crosslinked cells were washed once with PBS, flash-frozen by liquid nitrogen, and stored at − 80 °C for further usage.
FAIRE-seq and in situ Hi-C experiments
These two experiments were performed strictly in line with previously reported protocols [14, 27]. For FAIRE-seq data, reads were mapped to the hg19 assembly by bwa-mem, filtering was performed by removing unmapped and duplicated reads, and open chromatin peaks were determined by ZINBA  using the following parameters: input = none, offset = 50, method = “mixture,” peak confidence = 0.95, numProc = 4, buildwin = 1, refinepeaks = 1, selected model = T, tol = 1 × 10 − 5, and others as default. The “--broad” parameter was set to TRUE when calling broad peaks by the “callpeak” function of ZINBA. For Hi-C data, reads were trimmed to 36 bp and aligned to the hg19 assembly by bowtie2. Only uniquely mapped read pairs (MAPQ > 1) were kept, filtering was performed following previous protocols, the interaction matrix was normalized by the ICE method, and TADs and A/B compartments were identified using the HiTC package.
U266 and RPMI8226 cells were cultured to 80–90% confluence and harvested. RNA purification and library construction were performed by Novogene (Beijing, China) with three independent replicates for each cell line, and the differential expression analysis was performed using the TopHat-cufflinks software with the recommended parameters.
Cell fixation, digestion, and re-ligation
Digestion with the MboI enzyme, filling-in with biotin-labeled dATP, and re-ligation by the T4 ligase were performed using fixed cells (2–5 × 106 cells) following the instructions of the in situ Hi-C method.
Cells were resuspended in 2 ml lysis buffer (10 mM Tris-HCl [pH 8.0], 2% Triton X-100, 1% SDS, 100 mM NaCl, and 1 mM EDTA) and sonicated to an average DNA fragment size of 300–400 bp (Branson Sonifier 450D). The results for each 30 s of sonication were checked under a microscope until no intact cells were observed. The cells were kept on ice and foaming was avoided. The efficiency of sonication was further confirmed by agarose gels of purified DNA from a portion (5%, 100 μl) of the cell lysate.
Open chromatin purification
The supernatants were transfered to new 1.5 ml tubes after centrifugation (15,000-20,000×g for 5 min at 4°C). To purify the open chromatin, 1 volume phenol-chloroform-isoamyl alcohol was added to each aliquot of cell lysate. After vortexing for 10 s, each aliquot was centrifuged at 13,000×g for 5 min, and the top layer was transferred to a fresh 1.5 ml tube. The phenol–chloroform–isoamyl alcohol extraction step was repeated once, after which 200 μl of chloroform–isoamyl alcohol were added to each tube to remove traces of phenol, and the aqueous layer was transferred to a new 1.5-ml tube. Next, a 1/10 volume of 3 M sodium acetate (pH 5.2), 2 volumes of 95% ethanol, and 1 μl of 20 mg/ml glycogen were added to each tube and incubated at − 80 °C for 30 min (or longer) after fully mixing the sample. Each pellet was centrifuged at 13,000×g for 15 min at 4 °C, and the DNA pellet was washed twice with 500 μl of ice-cold 70% ethanol. The DNA was dried by leaving tubes open for 5 min and re-suspended in 200 μl of 10 mM Tris-HCl (pH 7.4).
Reverse cross-linking and DNA quantification
DNase-free RNase A (1 μl) was added following 30 min of incubation at 37 °C, and 1 μl of proteinase K was added and incubated at 55 °C for 1 h and then at 65 °C overnight to reverse cross-linking. The DNA was collected by adding 0.9 volume of AMPure XP beads (Beckman Coulter, A63881) and washed with 300 μl 10 mM Tris-HCl (pH 7.4). The concentration of DNA was measured by Qubit. The amount of purified DNA should not exceed 5% of total genomic DNA (1–3%). An optimized step can be performed to boost the yield before the biotin pull-down operation by sonicating the purified DNA with Covaris to a median fragment size of 300–500 bp.
Myone Streptavidin T1 beads (150 μl; Life technologies) were washed once with 400 μl 1 × TWB (5 mM Tris-HCl (pH 7.5), 0.05 mM EDTA, 1 M NaCl, 0.05% Tween 20), separated on a magnet, and resuspended with 300 μl 2× binding buffer (10 mM Tris-HCl (pH 7.5), 1 mM EDTA, 2 M NaCl). Then DNA dissolved in 300 μl 10 mM Tris-HCl (pH 7.4) was added into the bead solution and incubated at RT for 15 min with rotation. The beads were then separated on a magnet and biotinylated DNA was bound to the streptavidin beads.
Sequencing library construction
The library preparation processes were performed with streptavidin beads as described for the in situ Hi-C protocol. Briefly, the ends of sheared DNA were repaired and the biotin from un-ligated ends was removed, adapters were added to the A-tailed DNA fragments, and PCR was performed with eight to ten cycles using Illumina primers. Finally, DNA size selection was performed with 0.65–0.8× volume of AMPure XP beads to make sure the DNA length distributes between 300 and 500 bp. The library was quantified with Qubit and sequenced using an Illumina sequencing platform.
OCEAN-C data processing
OCEAN-C reads were mapped and filtered similarly to the situ Hi-C data. Briefly, clean reads were first trimmed to 36 bp and then mapped to genome hg19 with bowtie2, and reads with MAPQ less than 1 were discarded. If a read pair locates in the same restriction fragment (MboI), it was classified as dangling ends (inward), self-cycled (outward), or dumped pairs (same strand) and discarded. For the remaining read pairs that mapped to two different restriction fragments, if the distance between these two fragments was less than 1 kb, the read pairs were discarded due to the two ends’ close distance in sequence. The remaining read pairs were considered valid and used to call peaks and generate interaction heat maps. In the U266 cell line, the OCEAN-C peaks, which were defined as HOCIs, were determined by the ZINBA algorithm from the filtered data with the same parameters used for FAIRE-seq. In RPMI-8226 and GM12878 cell lines, HOCIs were called by ZINBA with the “pscl” method from the filtered data since the signals were too weak to be selected using the “mixture” method. HOCIs overlapping both H3K4me3 ChIP-seq peaks and gene promoters (2 kb up to genes’ transcription start sites) were defined as promoter HOCIs, and the rest which overlapping both H3k4me1 and H3K27Ac ChIP-seq peaks were defined as enhancer HOCIs. HOCI interactions, ChIA–PET interactions, gene densities, and gene transcription were examined using the WashU Epigenome Browser, and the network of HOCIs was constructed using the ggnet R package.
Identification of super-enhancers
We used the rose software to identify enhancers . First, enhancers were defined by H3K27ac ChIP-seq enriched regions. Second, the total background-subtracted ChIP-seq binding signals of DP1 or E2F1 were used to rank all enhancers and plotted (in units of rpm/bp). Finally, the x-axis points were identified where a line with slope of 1 was tangent to the curve, and the enhancers to the right of this point were defined as super-enhancers. Enhancers within 12.5 kb were stitched together, and regions within 2 kb of transcription start sites were considered as promoters rather than enhancers. The ChIP-seq data (H3K27ac, DP1 and E2F1) of U266 were downloaded from BipProject of NCBI (PRJNA319620).
Identification of broad H3K4me3 peaks
Broad H3K4me3 peaks were called from ChIP-seq data downloaded from ENCODE using MACS2 . The “gapped peaks” were ranked by their width. The top 5% of peaks in width were defined as broad H3K4me3 peaks.
We use Homer  to find enrichment of sequence motifs. HOCIs and ChIA-PET anchors in bed format were used as input. The “Known Results” were used as final results.
KEGG pathway enrichment analysis
We use DAVID  to find KEGG pathway enrichment. Genes whose promoters overlap with HOCIs were used as background, while genes whose promoters overlap with both HOCIs and broad H3K4me3 peaks were used as the input gene list.
We thank Zhihua Zhang for providing the GM12878 cell line, and Yujie Sun, Xiong Ji, Yong Zhang, Zhihua Zhang, Yixin Yao, and reviewers for critical comments on this study.
This work was supported by funding from Peking-Tsinghua Center for Life Sciences, School of Life Sciences and Center for Statistical Science of Peking University, National Natural Science Foundation China Key Research Grant 71532001, and Chinese National Key Projects of Research and Development (2016YFA0100103).
Availability of data and materials
All related sequencing data have been uploaded to the Genome Sequence Archive (GSE100832), and all related analysis scripts are stored at GitHub (https://github.com/ChengLiLab/OCEAN-C/)  and zenodo (DOI: https://doi.org/10.5281/zenodo.1210107) . The public datasets used in this paper were downloaded from GEO and ENCODE databases (Additional file 4: Table S3). The ChIP-seq datasets of the GM12878 cell line were downloaded from ENCODE . The ChIA-PET and Hi-C data of GM12878 are publicly available: Tang et al. (GSE72816) , Rao et al. (GSE63525) . The ChIP-seq datasets of the U266 cell line were downloaded from BioProject of NCBI (PRJNA319620 and PRJEB1912). The ChiA-PET, FAIRE-seq, DNase-C and DNase-seq datasets of the K562 cell line are publicly available: Li et al. (GSE39495) , Furey et al. (GSE35239) , Ma et al. (GSE56869) , and Encode Project Consortium (GSE90438) .
LTT designed this project and supervised the study with LC. LTT, JLM, and CY performed the experiments, JLM performed data analysis, and CQ participated in the project. LTT, JLM, and LC wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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- Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B, et al. The accessible chromatin landscape of the human genome. Nature. 2012;489:75–82.View ArticlePubMedPubMed CentralGoogle Scholar
- Rivera CM, Ren B. Mapping human epigenomes. Cell. 2013;155:39–55.View ArticlePubMedGoogle Scholar
- Gaffney DJ, McVicker G, Pai AA, Fondufe-Mittendorf YN, Lewellen N, Michelini K, Widom J, Gilad Y, Pritchard JK. Controls of nucleosome positioning in the human genome. PLoS Genet. 2012;8:e1003036.View ArticlePubMedPubMed CentralGoogle Scholar
- Schones DE, Cui K, Cuddapah S, Roh TY, Barski A, Wang Z, Wei G, Zhao K. Dynamic regulation of nucleosome positioning in the human genome. Cell. 2008;132:887–98.View ArticlePubMedGoogle Scholar
- Albert I, Mavrich TN, Tomsho LP, Qi J, Zanton SJ, Schuster SC, Pugh BF. Translational and rotational settings of H2A.Z nucleosomes across the Saccharomyces cerevisiae genome. Nature. 2007;446:572–6.View ArticlePubMedGoogle Scholar
- Boyle AP, Davis S, Shulha HP, Meltzer P, Margulies EH, Weng Z, Furey TS, Crawford GE. High-resolution mapping and characterization of open chromatin across the genome. Cell. 2008;132:311–22.View ArticlePubMedPubMed CentralGoogle Scholar
- Giresi PG, Kim J, McDaniell RM, Iyer VR, Lieb JD. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 2007;17:877–85.View ArticlePubMedPubMed CentralGoogle Scholar
- Fullwood MJ, Wei CL, Liu ET, Ruan Y. Next-generation DNA sequencing of paired-end tags (PET) for transcriptome and genome analyses. Genome Res. 2009;19:521–32.View ArticlePubMedPubMed CentralGoogle Scholar
- Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science. 2002;295:1306–11.View ArticlePubMedGoogle Scholar
- Simonis M, Klous P, Splinter E, Moshkin Y, Willemsen R, de Wit E, van Steensel B, de Laat W. Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat Genet. 2006;38:1348–54.View ArticlePubMedGoogle Scholar
- Zhao Z, Tavoosidana G, Sjolinder M, Gondor A, Mariano P, Wang S, Kanduri C, Lezcano M, Sandhu KS, Singh U, et al. Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions. Nat Genet. 2006;38:1341–7.View ArticlePubMedGoogle Scholar
- Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL, Honan TA, Rubio ED, Krumm A, Lamb J, Nusbaum C, et al. Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 2006;16:1299–309.View ArticlePubMedPubMed CentralGoogle Scholar
- Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009;326:289–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 2014;159:1665–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Mifsud B, Tavares-Cadete F, Young AN, Sugar R, Schoenfelder S, Ferreira L, Wingett SW, Andrews S, Grey W, Ewels PA, et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat Genet. 2015;47:598–606.View ArticlePubMedGoogle Scholar
- Ma W, Ay F, Lee C, Gulsoy G, Deng X, Cook S, Hesson J, Cavanaugh C, Ware CB, Krumm A, et al. Fine-scale chromatin interaction maps reveal the cis-regulatory landscape of human lincRNA genes. Nat Methods. 2015;12:71–8.View ArticlePubMedGoogle Scholar
- Hsieh TH, Weiner A, Lajoie B, Dekker J, Friedman N, Rando OJ. Mapping Nucleosome Resolution Chromosome Folding in Yeast by Micro-C. Cell. 2015;162:108–19.View ArticlePubMedPubMed CentralGoogle Scholar
- Nagano T, Lubling Y, Yaffe E, Wingett SW, Dean W, Tanay A, Fraser P. Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat Protoc. 2015;10:1986–2003.View ArticlePubMedGoogle Scholar
- Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, Laue ED, Tanay A, Fraser P. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502:59–64.View ArticlePubMedGoogle Scholar
- Mumbach MR, Rubin AJ, Flynn RA, Dai C, Khavari PA, Greenleaf WJ, Chang HY. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919-922Google Scholar
- Fang R, Yu M, Li G, Chee S, Liu T, Schmitt AD, Ren B. Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Res. 2016;26:1345–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Ma W, Ay F, Lee C, Gulsoy G, Deng X, Cook S, Hesson J, Cavanaugh C, Ware CB, Krumm A, et al. Using DNase Hi-C techniques to map global and local three-dimensional genome architecture at high resolution. Methods. 2018;S1046-2023(17)30240-2Google Scholar
- Song L, Zhang Z, Grasfeder LL, Boyle AP, Giresi PG, Lee BK, Sheffield NC, Graf S, Huss M, Keefe D, et al. Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res. 2011;21:1757–67.View ArticlePubMedPubMed CentralGoogle Scholar
- Murtha M, Strino F, Tokcaer-Keskin Z, Sumru Bayin N, Shalabi D, Xi X, Kluger Y, Dailey L. Comparative FAIRE-seq analysis reveals distinguishing features of the chromatin structure of ground state- and primed-pluripotent cells. Stem Cells. 2015;33:378–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Dekker J, Mirny L. The 3D genome as moderator of chromosomal communication. Cell. 2016;164:1110–21.View ArticlePubMedPubMed CentralGoogle Scholar
- Tang Z, Luo OJ, Li X, Zheng M, Zhu JJ, Szalaj P, Trzaskoma P, Magalska A, Wlodarczyk J, Ruszczycki B, et al. CTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription. Cell. 2015;163:1611–27.View ArticlePubMedPubMed CentralGoogle Scholar
- Simon JM, Giresi PG, Davis IJ, Lieb JD. Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active regulatory DNA. Nat Protoc. 2012;7:256–67.View ArticlePubMedPubMed CentralGoogle Scholar
- Rashid NU, Giresi PG, Ibrahim JG, Sun W, Lieb JD. ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions. Genome Biol. 2011;12:R67.View ArticlePubMedPubMed CentralGoogle Scholar
- Hnisz D, Abraham BJ, Lee TI, Lau A, Saint-Andre V, Sigova AA, Hoke HA, Young RA. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934–47.View ArticlePubMedGoogle Scholar
- Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, Rahl PB, Lee TI, Young RA. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell. 2013;153:307–19.View ArticlePubMedPubMed CentralGoogle Scholar
- Chen K, Chen Z, Wu D, Zhang L, Lin X, Su J, Rodriguez B, Xi Y, Xia Z, Chen X, et al. Broad H3K4me3 is associated with increased transcription elongation and enhancer activity at tumor-suppressor genes. Nat Genet. 2015;47:1149–57.View ArticlePubMedPubMed CentralGoogle Scholar
- Cao F, Fang Y, Tan HK, Goh Y, Choy JYH, Koh BTH, Hao Tan J, Bertin N, Ramadass A, Hunter E, et al. Super-enhancers and broad H3K4me3 domains form complex gene regulatory circuits involving chromatin interactions. Sci Rep. 2017;7:2186.View ArticlePubMedPubMed CentralGoogle Scholar
- Li G, Ruan X, Auerbach RK, Sandhu KS, Zheng M, Wang P, Poh HM, Goh Y, Lim J, Zhang J, et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell. 2012;148:84–98.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang JL, Marco E, Pinello L, Yuan GC. Predicting chromatin organization using histone marks. Genome Biol. 2015;16:162.Google Scholar
- Sandhu KS, Li G, Poh HM, Quek YL, Sia YY, Peh SQ, Mulawadi FH, Lim J, Sikic M, Menghi F, et al. Large-scale functional organization of long-range chromatin interaction networks. Cell Rep. 2012;2:1207–19.View ArticlePubMedPubMed CentralGoogle Scholar
- Dai C, Li W, Tjong H, Hao S, Zhou Y, Li Q, Chen L, Zhu B, Alber F, Jasmine Zhou X. Mining 3D genome structure populations identifies major factors governing the stability of regulatory communities. Nat Commun. 2016;7:11549.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhu Y, Chen Z, Zhang K, Wang M, Medovoy D, Whitaker JW, Ding B, Li N, Zheng L, Wang W. Constructing 3D interaction maps from 1D epigenomes. Nat Commun. 2016;7:10812.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9:R137.View ArticlePubMedPubMed CentralGoogle Scholar
- Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38:576–89.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57.View ArticlePubMedGoogle Scholar
- Li T, Ji L, Cao Y, Chen Q, Li C. OCEAN-C: mapping hubs of open chromatin interactions across the genome reveals gene regulatory networks. Github; 2018. https://github.com/ChengLiLab/OCEAN-C/. Accessed 30 Mar 2018.
- Li T., Ji L., Cao Y., Chen Q., and Li C., OCEAN-C: mapping hubs of open chromatin interactions across the genome reveals gene regulatory networks. Zenodo; 2018. https://doi.org/10.5281/zenodo.1210107.
- Consortium, E.P.,(Encode Project Consortium). An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.View ArticleGoogle Scholar