Accessible Region Conformation Capture (ARC-C) gives high resolution insights into genome architecture and regulation

Nuclear organization and chromatin interactions are important for genome function, yet determining chromatin connections at high-resolution remains a major challenge. To address this, we developed Accessible Region Conformation Capture (ARC-C), which profiles interactions between regulatory elements genome-wide without a capture step. Applied to C. elegans, we identify ~15,000 significant interactions between regulatory elements at 500bp resolution. Of 105 TFs or chromatin regulators tested, we find that the binding sites of 60 are enriched for interacting with each other, making them candidates for mediating interactions. These include cohesin and condensin II. Applying ARC-C to a mutant of transcription factor BLMP-1 detected changes in interactions between its targets. ARC-C simultaneously profiles domain level architecture, and we observe that C. elegans chromatin domains defined by either active or repressive modifications form topologically associating domains (TADs) which interact with A/B (active/inactive) compartment-like structure. Furthermore, we discovered that inactive compartment interactions are dependent on H3K9 methylation. ARC-C is a powerful new tool to interrogate genome architecture and regulatory interactions at high resolution.

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High-resolution mapping of regulatory element interactions and genome architecture using ARC-C

10.1101/467506 ◽

2018 ◽

Author(s):

Ni Huang ◽

Wei Qiang Seow ◽

Julie Ahringer

Keyword(s):

High Resolution ◽

Regulatory Element ◽

Regulatory Elements ◽

Genome Architecture ◽

Chromosome Conformation ◽

Regulatory Interactions ◽

C Elegans ◽

Chromatin Regulators ◽

Accessible Region ◽

Resolution Mapping

AbstractInteractions between cis-regulatory elements such as promoters and enhancers are important for transcription but global identification of these interactions remains a major challenge. Leveraging the chromatin accessiblity of regulatory elements, we developed ARC-C (accessible region chromosome conformation capture), which profiles chromatin regulatory interactions genome-wide at high resolution. Applying ARC-C to C. elegans, we identify ~15,000 significant interactions at 500bp resolution. Regions bound by transcription factors and chromatin regulators such as cohesin and condensin II are enriched for interactions, and we use ARC-C to show that the BLMP-1 transcription factor mediates interactions between its targets. Investigating domain level architecture, we find that C. elegans chromatin domains defined by either active or repressive modifications form topologically associating domains (TADs) and that these domains interact to form A/B (active/inactive) compartment structure. ARC-C is a powerful new tool to interrogate genome architecture and regulatory interactions at high resolution.

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An integrative approach for fine-mapping chromatin interactions

Bioinformatics ◽

10.1093/bioinformatics/btz843 ◽

2019 ◽

Vol 36 (6) ◽

pp. 1704-1711

Author(s):

Artur Jaroszewicz ◽

Jason Ernst

Keyword(s):

Gene Regulation ◽

High Resolution ◽

Biological Significance ◽

Computational Method ◽

Supplementary Information ◽

Integrative Approach ◽

Genome Architecture ◽

Open Chromatin ◽

Chromatin Interactions ◽

Genome Wide

Abstract Motivation Chromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions. Results To predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show χ-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. χ-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible. Availability and implementation χ-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN). Supplementary information Supplementary data are available at Bioinformatics online.

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An Integrative Approach for Fine-Mapping Chromatin Interactions

10.1101/605576 ◽

2019 ◽

Author(s):

Artur Jaroszewicz ◽

Jason Ernst

Keyword(s):

High Resolution ◽

Binding Sites ◽

Biological Significance ◽

Computational Method ◽

Integrative Approach ◽

Genome Architecture ◽

Open Chromatin ◽

Chromatin Interactions ◽

Genome Wide ◽

Evolutionarily Conserved

AbstractChromatin interactions play an important role in genome architecture and regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g., 5-25kb), which is substantially larger than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions. To predict the sources of Hi-C identified interactions at a high resolution (e.g., 100bp), we developed a computational method that integrates ChIP-seq data of transcription factors and histone marks and DNase-seq data. Our method,χ-SCNN, uses this data to first train a Siamese Convolutional Neural Network (SCNN) to discriminate between called Hi-C interactions and non-interactions.χ-SCNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also showχ-SCNN predictions enrich for evolutionarily conserved bases, eQTLs, and CTCF motifs, supporting their biological significance.χ-SCNN provides an approach for analyzing important aspects of genome architecture and regulation at a higher resolution than previously possible.χ-SCNN software is available on GitHub (https://github.com/ernstlab/X-SCNN).

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Genome-Wide Regulatory Interactions in the Early Stages of Drosophila Speciation

10.36939/ir.202012151547 ◽

2020 ◽

Author(s):

◽

Alwyn Clark Go

Keyword(s):

Reproductive Isolation ◽

Expression Analysis ◽

Regulatory Elements ◽

Specific Expression ◽

Allele Specific Expression ◽

Regulatory Interactions ◽

Early Stages ◽

Genome Wide ◽

Allele Specific ◽

Incomplete Reproductive Isolation

Speciation occurs when reproductive barriers prevent the exchange of genetic information between individuals. A common form of reproductive barrier between species capable of interbreeding is hybrid sterility. Genomic incompatibilities between the divergent genomes of different species contribute to a reduction in hybrid fitness. These incompatibilities continue to accumulate after speciation, therefore, young divergent taxa with incomplete reproductive isolation are important in understating the genetics leading to speciation. Here, I use two Drosophila subspecies pairs. The first is D. willistoni consisting of D. w. willistoni and D. w. winge. The second subspecies pair is D. pseudoobscura, which is composed of D. p. pseudoobscura and D. p. bogotana. Both subspecies pairs are at the early stages of speciation and show incomplete reproductive isolation through unidirectional hybrid male sterility. In this thesis, I performed an exploratory survey of genome-wide expression analysis using RNA-sequencing on D. willistoni and determined the extent of regulatory divergence between the subspecies using allele-specific expression analysis. I found that misexpressed genes showed a degree of tissue specificity and that the sterile male hybrids had a higher proportion of misexpressed genes in the testes relative to the fertile hybrids. The analysis of regulatory divergence between this subspecies pair found a large (66-70%) proportion of genes with conserved regulatory elements. Of the genes showing evidence or regulatory divergence between subspecies, cis-regulatory divergence was more common than other types. In the D. pseudoobscura subspecies pair, I compared sequence and expression divergence and found no support for directional selection driving gene misexpression in their hybrids. Allele-specific expression analysis revealed that compensatory cis-trans mutations partly explained gene misexpression in the hybrids. The remaining hybrid misexpression occurs due to interacting gene networks or possible co-option of cis-regulatory elements by divergent transacting factors. Overall, the results of this thesis highlight the role of regulatory interactions in a hybrid genome and how these interactions could lead to hybrid breakdown by disrupting gene interaction networks.

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The native cistrome and sequence motif families of the maize ear

PLoS Genetics ◽

10.1371/journal.pgen.1009689 ◽

2021 ◽

Vol 17 (8) ◽

pp. e1009689

Author(s):

Savannah D. Savadel ◽

Thomas Hartwig ◽

Zachary M. Turpin ◽

Daniel L. Vera ◽

Pei-Yau Lung ◽

...

Keyword(s):

High Resolution ◽

Binding Sites ◽

Regulatory Networks ◽

Regulatory Elements ◽

Chromatin Interaction ◽

Sequence Motif ◽

Binding Prediction ◽

Interaction Sites ◽

Genome Wide ◽

Dna Regulatory Elements

Elucidating the transcriptional regulatory networks that underlie growth and development requires robust ways to define the complete set of transcription factor (TF) binding sites. Although TF-binding sites are known to be generally located within accessible chromatin regions (ACRs), pinpointing these DNA regulatory elements globally remains challenging. Current approaches primarily identify binding sites for a single TF (e.g. ChIP-seq), or globally detect ACRs but lack the resolution to consistently define TF-binding sites (e.g. DNAse-seq, ATAC-seq). To address this challenge, we developed MNase-defined cistrome-Occupancy Analysis (MOA-seq), a high-resolution (< 30 bp), high-throughput, and genome-wide strategy to globally identify putative TF-binding sites within ACRs. We used MOA-seq on developing maize ears as a proof of concept, able to define a cistrome of 145,000 MOA footprints (MFs). While a substantial majority (76%) of the known ATAC-seq ACRs intersected with the MFs, only a minority of MFs overlapped with the ATAC peaks, indicating that the majority of MFs were novel and not detected by ATAC-seq. MFs were associated with promoters and significantly enriched for TF-binding and long-range chromatin interaction sites, including for the well-characterized FASCIATED EAR4, KNOTTED1, and TEOSINTE BRANCHED1. Importantly, the MOA-seq strategy improved the spatial resolution of TF-binding prediction and allowed us to identify 215 motif families collectively distributed over more than 100,000 non-overlapping, putatively-occupied binding sites across the genome. Our study presents a simple, efficient, and high-resolution approach to identify putative TF footprints and binding motifs genome-wide, to ultimately define a native cistrome atlas.

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GAMtools: an automated pipeline for analysis of Genome Architecture Mapping data

10.1101/114710 ◽

2017 ◽

Cited By ~ 1

Author(s):

Robert A. Beagrie ◽

Markus Schueler

Keyword(s):

Next Generation Sequencing Data ◽

Genome Architecture ◽

Sequencing Data ◽

Cell Nuclei ◽

Automated Mapping ◽

Chromatin Interactions ◽

Genome Wide ◽

Quality Control Metrics ◽

Genomic Regions ◽

Analytical Tools

AbstractGenome Architecture Mapping (GAM) is a recently developed method for mapping chromatin interactions genome-wide. GAM is based on sequencing genomic DNA extracted from thin cryosections of cell nuclei. As a new approach, GAM datasets require specialized analytical tools and approaches. Here we present GAMtools, a pipeline for analysing GAM datasets. GAMtools covers the automated mapping of raw next-generation sequencing data generated by GAM, detection of genomic regions present in each nuclear slice, calculation of quality control metrics, generation of inferred proximity matrices, plotting of heatmaps and detection of genomic features for which chromatin interactions are enriched/depleted.

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HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks

Genes ◽

10.3390/genes10110862 ◽

2019 ◽

Vol 10 (11) ◽

pp. 862

Author(s):

Tong Liu ◽

Zheng Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

High Resolution ◽

Convolutional Neural Network ◽

Mean Squared Error ◽

Correlation Coefficients ◽

Chromatin Interactions ◽

Squared Error ◽

Genome Wide

We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different deep learning architecture: HiCNN2-1 is based on one single convolutional neural network (ConvNet); HiCNN2-2 consists of an ensemble of two different ConvNets; and HiCNN2-3 is an ensemble of three different ConvNets. Our evaluation results indicate that HiCNN2-enhanced high-resolution Hi-C data achieve smaller mean squared error and higher Pearson’s correlation coefficients with experimental high-resolution Hi-C data compared with existing methods HiCPlus and HiCNN. Moreover, all of the three HiCNN2 methods can recover more significant interactions detected by Fit-Hi-C compared to HiCPlus and HiCNN. Based on our evaluation results, we would recommend using HiCNN2-1 and HiCNN2-3 if recovering more significant interactions from Hi-C data is of interest, and HiCNN2-2 and HiCNN if the goal is to achieve higher reproducibility scores between the enhanced Hi-C matrix and the real high-resolution Hi-C matrix.

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Genome-wide binding and mechanistic analyses of Smchd1-mediated epigenetic regulation

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1504232112 ◽

2015 ◽

Vol 112 (27) ◽

pp. E3535-E3544 ◽

Cited By ~ 55

Author(s):

Kelan Chen ◽

Jiang Hu ◽

Darcy L. Moore ◽

Ruijie Liu ◽

Sarah A. Kessans ◽

...

Keyword(s):

Epigenetic Regulation ◽

Transcriptional Control ◽

Regulatory Elements ◽

Ctcf Binding ◽

Dna And Rna ◽

Chromatin Interactions ◽

Genome Wide ◽

Binding Factor ◽

Structural Maintenance Of Chromosomes ◽

Hinge Domain

Structural maintenance of chromosomes flexible hinge domain containing 1 (Smchd1) is an epigenetic repressor with described roles in X inactivation and genomic imprinting, but Smchd1 is also critically involved in the pathogenesis of facioscapulohumeral dystrophy. The underlying molecular mechanism by which Smchd1 functions in these instances remains unknown. Our genome-wide transcriptional and epigenetic analyses show that Smchd1 binds cis-regulatory elements, many of which coincide with CCCTC-binding factor (Ctcf) binding sites, for example, the clustered protocadherin (Pcdh) genes, where we show Smchd1 and Ctcf act in opposing ways. We provide biochemical and biophysical evidence that Smchd1–chromatin interactions are established through the homodimeric hinge domain of Smchd1 and, intriguingly, that the hinge domain also has the capacity to bind DNA and RNA. Our results suggest Smchd1 imparts epigenetic regulation via physical association with chromatin, which may antagonize Ctcf-facilitated chromatin interactions, resulting in coordinated transcriptional control.

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Decoding functional regulatory maps via genomic evolutionary footprints in 63 green plants

10.1101/484493 ◽

2018 ◽

Author(s):

Feng Tian ◽

De-Chang Yang ◽

Yu-Qi Meng ◽

Jinpu Jin ◽

Ge Gao

Keyword(s):

Binding Sites ◽

Regulatory System ◽

Regulatory Elements ◽

Binding Affinities ◽

Green Plants ◽

Regulatory Interactions ◽

Regulatory Systems ◽

Genome Wide ◽

Transcriptional Regulatory ◽

Systematic Identification

AbstractSystematic identification of functional transcriptional regulatory interactions is essential for understanding regulatory systems. Here, we firstly established genome-wide conservation landscapes for 63 green plants of seven lineages and then developed an algorithm FunTFBS to screen for functional regulatory elements and interactions by coupling base-varied binding affinities of transcription factors with the evolutionary footprints on their binding sites. Using the FunTFBS and the conservation landscapes, we further identified over two million functional interactions for 21,346 TFs, charting functional regulatory maps of these 63 plants. Our work provides plant community with valuable resources to decode plant transcriptional regulatory system and genome sequences.

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