A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping

doi:10.1016/J.CELL.2014.11.021

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A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping

Journal Article•DOI•

A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping

Suhas S.P. Rao¹, Miriam H. Huntley¹, Neva C. Durand, Elena K. Stamenova, Ivan D. Bochkov¹, James T. Robinson², James T. Robinson¹, Adrian L. Sanborn¹, Ido Machol³, Ido Machol¹, Arina D. Omer³, Arina D. Omer¹, Eric S. Lander⁴, Eric S. Lander², Eric S. Lander⁵, Erez Lieberman Aiden - Show less +12 more•Institutions (5)

Baylor College of Medicine¹, Broad Institute², Rice University³, Harvard University⁴, Massachusetts Institute of Technology⁵

18 Dec 2014-Cell (Cell Press)-Vol. 159, Iss: 7, pp 1665-1680

TL;DR: In situ Hi-C is used to probe the 3D architecture of genomes, constructing haploid and diploid maps of nine cell types, identifying ∼10,000 loops that frequently link promoters and enhancers, correlate with gene activation, and show conservation across cell types and species.

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About: This article is published in Cell.The article was published on 2014-12-18 and is currently open access. It has received 5945 citations till now. The article focuses on the topics: CTCF & Chromatin Loop.

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Journal Article•DOI•

Disruptions of Topological Chromatin Domains Cause Pathogenic Rewiring of Gene-Enhancer Interactions

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Darío G. Lupiáñez¹, Darío G. Lupiáñez², Katerina Kraft¹, Katerina Kraft², Verena Heinrich¹, Peter Krawitz¹, Peter Krawitz², Francesco Brancati³, Eva Klopocki⁴, Denise Horn¹, Hülya Kayserili⁵, John M. Opitz⁶, Renata Laxova⁶, Fernando Santos-Simarro⁷, Fernando Santos-Simarro⁸, Brigitte Gilbert-Dussardier, Lars Wittler², Marina Borschiwer², Stefan A. Haas², Marco Osterwalder⁹, Martin Franke², Martin Franke¹, Bernd Timmermann², Jochen Hecht¹, Jochen Hecht², Malte Spielmann¹, Malte Spielmann², Axel Visel¹⁰, Axel Visel¹¹, Axel Visel⁹, Stefan Mundlos¹, Stefan Mundlos² - Show less +28 more•Institutions (11)

Charité¹, Max Planck Society², University of Rome Tor Vergata³, University of Würzburg⁴, Istanbul University⁵, University of Utah⁶, Carlos III Health Institute⁷, Hospital Universitario La Paz⁸, Lawrence Berkeley National Laboratory⁹, United States Department of Energy¹⁰, University of California, Merced¹¹

21 May 2015-Cell

TL;DR: The results demonstrate the functional importance of TADs for orchestrating gene expression via genome architecture and indicate criteria for predicting the pathogenicity of human structural variants, particularly in non-coding regions of the human genome.

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1,677 citations

Cites background from "A 3D Map of the Human Genome at Kil..."

...While the present study focused on one locus and one set of related morphological phenotypes, TAD data for the entire human and mouse genome are becoming available at increasing resolution (Jin et al., 2013; Rao et al., 2014)....
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Journal Article•DOI•

Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments

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Neva C. Durand, Muhammad S. Shamim¹, Muhammad S. Shamim², Ido Machol¹, Ido Machol², Suhas S.P. Rao, Miriam H. Huntley, Eric S. Lander³, Eric S. Lander⁴, Erez Lieberman Aiden - Show less +6 more•Institutions (4)

Baylor College of Medicine¹, Rice University², Harvard University³, Massachusetts Institute of Technology⁴

27 Jul 2016-Cell systems

TL;DR: Juicer as mentioned in this paper is an open-source tool for analyzing terabase-scale Hi-C datasets, which allows users without a computational background to transform raw sequence data into normalized contact maps with one click.

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Abstract: Hi-C experiments explore the 3D structure of the genome, generating terabases of data to create high-resolution contact maps. Here, we introduce Juicer, an open-source tool for analyzing terabase-scale Hi-C datasets. Juicer allows users without a computational background to transform raw sequence data into normalized contact maps with one click. Juicer produces a hic file containing compressed contact matrices at many resolutions, facilitating visualization and analysis at multiple scales. Structural features, such as loops and domains, are automatically annotated. Juicer is available as open source software at http://aidenlab.org/juicer/.

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1,649 citations

Journal Article•DOI•

Formation of Chromosomal Domains by Loop Extrusion

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Geoffrey Fudenberg¹, Maxim Imakaev¹, Carolyn Lu¹, Anton Goloborodko¹, Nezar Abdennur¹, Leonid A. Mirny¹, Leonid A. Mirny² - Show less +3 more•Institutions (2)

Massachusetts Institute of Technology¹, Harvard University²

31 May 2016-Cell Reports

TL;DR: This model produces TADs and finer-scale features of Hi-C data because each TAD emerges from multiple loops dynamically formed through extrusion, contrary to typical illustrations of single static loops.

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1,479 citations

Cites background or methods or result from "A 3D Map of the Human Genome at Kil..."

...(A) Hi-C contact maps at 5-kb resolution for six chromosomal regions (GM12878 in situ MboI) (Rao et al., 2014), highlighting TADs (purple lines) and peak loci (blue circles)....
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...First, only 50% of TADs have cornerpeaks (Rao et al., 2014)....
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...Third, inward-oriented CTCF sites are enriched at TAD boundaries (Vietri Rudan et al., 2015) and TAD corner-peaks (Rao et al., 2014)....
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..., 2012) and corner peaks (Rao et al., 2014), and its depletion makes TADs less prominent (Sofueva et al....
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...Finally, cohesin is enriched at interphase TAD boundaries (Dixon et al., 2012) and corner peaks (Rao et al., 2014), and its depletion makes TADs less prominent (Sofueva et al., 2013; Zuin et al., 2014)....
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Journal Article•DOI•

HiC-Pro: an optimized and flexible pipeline for Hi-C data processing

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Nicolas Servant¹, Nelle Varoquaux², Nelle Varoquaux³, Nelle Varoquaux¹, Bryan R. Lajoie⁴, Eric Viara, Chong-Jian Chen, Jean-Philippe Vert¹, Jean-Philippe Vert³, Jean-Philippe Vert², Edith Heard⁵, Edith Heard³, Edith Heard¹, Job Dekker⁴, Emmanuel Barillot¹, Emmanuel Barillot³, Emmanuel Barillot² - Show less +13 more•Institutions (5)

Curie Institute¹, PSL Research University², French Institute of Health and Medical Research³, University of Massachusetts Medical School⁴, Centre national de la recherche scientifique⁵

01 Dec 2015-Genome Biology

TL;DR: This work applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time and its fast implementation of the iterative correction method.

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Abstract: 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 .

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1,444 citations

Cites background or methods from "A 3D Map of the Human Genome at Kil..."

...Generating genome-wide contact maps at 40 to 1 kb resolution requires a sequencing depth of hundreds of millions to multi-billions of paired-end reads depending on the organism [7, 8]....
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...More recently, very large data sets with deeper sequencing have been used to increase the Hi-C resolution in order to detect loops across the entire genome [7, 8]....
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...Differences in paternal and maternal X chromosome organization were recently described, with the presence of mega-domains on the inactive X chromosome, which are not seen in the active X chromosome [7, 21, 22]....
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...In the same way, a high quality experiment is usually characterized by a significant fraction (>40 %) of long-range intrachromosomal valid pairs [7]....
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Journal Article•DOI•

Chromatin architecture reorganization during stem cell differentiation

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Jesse R. Dixon¹, Inkyung Jung¹, Siddarth Selvaraj¹, Yin Shen¹, Jessica Antosiewicz-Bourget², Ah Young Lee¹, Zhen Ye¹, Audrey Kim¹, Nisha Rajagopal¹, Wei Xie³, Yarui Diao¹, Jing Liang⁴, Huimin Zhao⁴, Victor V. Lobanenkov⁵, Joseph R. Ecker⁶, James A. Thomson⁷, Bing Ren⁸ - Show less +13 more•Institutions (8)

Ludwig Institute for Cancer Research¹, Morgridge Institute for Research², Tsinghua University³, University of Illinois at Urbana–Champaign⁴, National Institutes of Health⁵, Salk Institute for Biological Studies⁶, University of Wisconsin-Madison⁷, University of California, San Diego⁸

19 Feb 2015-Nature

TL;DR: Mapping genome-wide chromatin interactions in human embryonic stem cells and four human ES-cell-derived lineages reveals extensive chromatin reorganization during lineage specification, providing a global view of chromatin dynamics and a resource for studying long-range control of gene expression in distinct human cell lineages.

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Abstract: Higher-order chromatin structure is emerging as an important regulator of gene expression. Although dynamic chromatin structures have been identified in the genome, the full scope of chromatin dynamics during mammalian development and lineage specification remains to be determined. By mapping genome-wide chromatin interactions in human embryonic stem (ES) cells and four human ES-cell-derived lineages, we uncover extensive chromatin reorganization during lineage specification. We observe that although self-associating chromatin domains are stable during differentiation, chromatin interactions both within and between domains change in a striking manner, altering 36% of active and inactive chromosomal compartments throughout the genome. By integrating chromatin interaction maps with haplotype-resolved epigenome and transcriptome data sets, we find widespread allelic bias in gene expression correlated with allele-biased chromatin states of linked promoters and distal enhancers. Our results therefore provide a global view of chromatin dynamics and a resource for studying long-range control of gene expression in distinct human cell lineages.

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1,393 citations

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References

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Journal Article•DOI•

Fast and accurate short read alignment with Burrows–Wheeler transform

[...]

Heng Li¹, Richard Durbin¹•Institutions (1)

Wellcome Trust Sanger Institute¹

01 Jul 2009-Bioinformatics

TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.

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Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: [email protected]

...read moreread less

43,862 citations

Journal Article•DOI•

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

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Aaron McKenna¹, Matthew Hanna, Eric Banks, Andrey Sivachenko, Kristian Cibulskis, Andrew Kernytsky, Kiran V. Garimella, David Altshuler, Stacey Gabriel, Mark J. Daly, Mark A. DePristo - Show less +7 more•Institutions (1)

Broad Institute¹

01 Sep 2010-Genome Research

TL;DR: The GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

...read moreread less

Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

...read moreread less

20,557 citations

"A 3D Map of the Human Genome at Kil..." refers background in this paper

...…Loops and Massive Domains and Loops on the Inactive X Chromosome Because many of our reads overlap SNPs, it is possible to use GM12878 phasing data (McKenna et al., 2010; 1000 Genomes Project Consortium et al., 2012) to assign contacts to specific chromosomal homologs (Figure 7A; Table S8)....
[...]

Journal Article•DOI•

An integrated encyclopedia of DNA elements in the human genome

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Principal investigators¹, Nhgri groups², Data production leads³, Lead analysts³•Institutions (3)

Wellcome Trust¹, University of Washington², Pennsylvania State University³

06 Sep 2012-Nature

TL;DR: The Encyclopedia of DNA Elements project provides new insights into the organization and regulation of the authors' genes and genome, and is an expansive resource of functional annotations for biomedical research.

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Abstract: The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

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13,548 citations

Journal Article•

An integrated encyclopedia of DNA elements in the human genome.

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ENCODEConsortium

01 Jan 2012-Nature

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8,106 citations

"A 3D Map of the Human Genome at Kil..." refers background in this paper

...Including Imprinting-Specific Loops and Massive Domains and Loops on the Inactive X Chromosome Because many of our reads overlap SNPs, it is possible to use GM12878 phasing data (McKenna et al., 2010; 1000 Genomes Project Consortium et al., 2012) to assign contacts to specific chromosomal homologs (Figure 7A; Table S8)....
[...]
...Loci within a contact domain show correlated histone modifications for eight different factors (H3K36me3, H3K27me3, H3K4me1, H3K4me2, H3K4me3, H3K9me3, H3K79me2, and H4K20me1) based on data from the ENCODE project in GM12878 cells (ENCODE Project Consortium, 2012)....
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Journal Article•DOI•

An integrated map of genetic variation from 1,092 human genomes

[...]

Gonçalo R. Abecasis¹, Adam Auton², Lisa D. Brooks³, Mark A. DePristo⁴, Richard Durbin⁵, Robert E. Handsaker⁴, Robert E. Handsaker⁶, Hyun Min Kang¹, Gabor T. Marth⁷, Gil McVean⁸ - Show less +6 more•Institutions (8)

University of Michigan¹, Yeshiva University², National Institutes of Health³, Broad Institute⁴, Wellcome Trust Sanger Institute⁵, Harvard University⁶, Boston College⁷, University of Oxford⁸

01 Nov 2012-Nature

TL;DR: It is shown that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites.

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Abstract: By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.

...read moreread less

7,710 citations

"A 3D Map of the Human Genome at Kil..." refers background in this paper

...Including Imprinting-Specific Loops and Massive Domains and Loops on the Inactive X Chromosome Because many of our reads overlap SNPs, it is possible to use GM12878 phasing data (McKenna et al., 2010; 1000 Genomes Project Consortium et al., 2012) to assign contacts to specific chromosomal homologs (Figure 7A; Table S8)....
[...]
...…Loops and Massive Domains and Loops on the Inactive X Chromosome Because many of our reads overlap SNPs, it is possible to use GM12878 phasing data (McKenna et al., 2010; 1000 Genomes Project Consortium et al., 2012) to assign contacts to specific chromosomal homologs (Figure 7A; Table S8)....
[...]