A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions

Modeling properties of chromosome territories using polymer filaments in diverse confinement geometries

Interphase chromosomes reside within distinct nuclear regions known as chromosome territories (CTs). Recent observations from Hi-C analyses, a method mapping chromosomal interactions, have revealed varied decay in contact probabilities among different chromosomes. Our study explores the relationship between this contact decay and the particular shapes of the chromosome territories they occupy. For this, we employed molecular dynamics (MD) simulations to examine how confined polymers, resembling chromosomes, behave within different confinement geometries similar to chromosome territory boundaries. Our simulations unveil so far unreported relationships between contact probabilities and end-to-end distances varying based on different confinement geometries. These findings highlight the crucial impact of chromosome territories on shaping the larger-scale properties of 3D genome organization. They emphasize the intrinsic connection between the shapes of these territories and the contact behaviors exhibited by chromosomes. Understanding these correlations is key to accurately interpret Hi-C and microscopy data, and offers vital insights into the foundational principles governing genomic organization.

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction

Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.

MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops

Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.

An in silico procedure for generating protein-mediated chromatin interaction data and comparison of significant interaction calling methods

The ability to simulate high-throughput data with high fidelity to real experimental data is fundamental for benchmarking methods used to detect true long-range chromatin interactions mediated by a specific protein. Yet, such tools are not currently available. To fill this gap, we develop an in silico experimental procedure, ChIA-Sim, which imitates the experimental procedures that produce real ChIA-PET, Hi-ChIP, or PLAC-seq data. We show the fidelity of ChIA-Sim to real data by using guiding characteristics of several real datasets to generate data using the simulation procedure. We also used ChIA-Sim data to demonstrate the use of our in silico procedure in benchmarking methods for significant interactions analysis by evaluating four methods for significant interaction calling (SIC). In particular, we assessed each method's performance in terms of correct identification of long-range interactions. We further analyzed four experimental datasets from publicly available databases and shew that the trend of the results are consistent with those seen in data generated from ChIA-Sim. This serves as additional evidence that ChIA-Sim closely resembles data produced from the experimental protocols it models after.

Stage-specific dynamic reorganization of genome topology shapes transcriptional neighborhoods in developing human retinal organoids

We have generated a high-resolution Hi-C map of developing human retinal organoids to elucidate spatiotemporal dynamics of genomic architecture and its relationship with gene expression patterns. We demonstrate progressive stage-specific alterations in DNA topology and correlate these changes with transcription of cell-type-restricted gene markers during retinal differentiation. Temporal Hi-C reveals a shift toward A compartment for protein-coding genes and B compartment for non-coding RNAs, displaying high and low expression, respectively. Notably, retina-enriched genes are clustered near lost boundaries of topologically associated domains (TADs), and higher-order assemblages (i.e., TAD cliques) localize in active chromatin regions with binding sites for eye-field transcription factors. These genes gain chromatin contacts at their transcription start site as organoid differentiation proceeds. Our study provides a global view of chromatin architecture dynamics associated with diversification of cell types during retinal development and serves as a foundational resource for in-depth functional investigations of retinal developmental traits.

SilenceREIN: seeking silencers on anchors of chromatin loops by deep graph neural networks

Silencers are repressive cis-regulatory elements that play crucial roles in transcriptional regulation. Experimental methods for identifying silencers are always costly and time-consuming. Computational methods, which relies on genomic sequence features, have been introduced as alternative approaches. However, silencers do not have significant epigenomic signature. Therefore, we explore a new way to computationally identify silencers, by incorporating chromatin structural information. We propose the SilenceREIN method, which focuses on finding silencers on anchors of chromatin loops. By using graph neural networks, we extracted chromatin structural information from a regulatory element interaction network. SilenceREIN integrated the chromatin structural information with linear genomic signatures to find silencers. The predictive performance of SilenceREIN is comparable or better than other states-of-the-art methods. We performed a genome-wide scanning to systematically find silencers in human genome. Results suggest that silencers are widespread on anchors of chromatin loops. In addition, enrichment analysis of transcription factor binding motif support our prediction results. As far as we can tell, this is the first attempt to incorporate chromatin structural information in finding silencers. All datasets and source codes of SilenceREIN have been deposited in a GitHub repository (https://github.com/JianHPan/SilenceREIN).

The dynamic role of cohesin in maintaining human genome architecture

Recent advances in genomic and imaging techniques have revealed the complex manner of organizing billions of base pairs of DNA necessary for maintaining their functionality and ensuring the proper expression of genetic information. The SMC proteins and cohesin complex primarily contribute to forming higher-order chromatin structures, such as chromosomal territories, compartments, topologically associating domains (TADs) and chromatin loops anchored by CCCTC-binding factor (CTCF) protein or other genome organizers. Cohesin plays a fundamental role in chromatin organization, gene expression and regulation. This review aims to describe the current understanding of the dynamic nature of the cohesin-DNA complex and its dependence on cohesin for genome maintenance. We discuss the current 3C technique and numerous bioinformatics pipelines used to comprehend structural genomics and epigenetics focusing on the analysis of Cohesin-centred interactions. We also incorporate our present comprehension of Loop Extrusion (LE) and insights from stochastic modelling.

ChIATAC is an efficient strategy for multi-omics mapping of 3D epigenomes from low-cell inputs

Connecting genes to their cis-regulatory elements has been enabled by genome-wide mapping of chromatin interactions using proximity ligation in ChIA-PET, Hi-C, and their derivatives. However, these methods require millions of input cells for high-quality data and thus are unsuitable for many studies when only limited cells are available. Conversely, epigenomic profiling via transposase digestion in ATAC-seq requires only hundreds to thousands of cells to robustly map open chromatin associated with transcription activity, but it cannot directly connect active genes to their distal enhancers. Here, we combine proximity ligation in ChIA-PET and transposase accessibility in ATAC-seq into ChIATAC to efficiently map interactions between open chromatin loci in low numbers of input cells. We validate ChIATAC in Drosophila cells and optimize it for mapping 3D epigenomes in human cells robustly. Applying ChIATAC to primary human T cells, we reveal mechanisms that topologically regulate transcriptional programs during T cell activation.

3D chromatin architecture and transcription regulation in cancer

Chromatin has distinct three-dimensional (3D) architectures important in key biological processes, such as cell cycle, replication, differentiation, and transcription regulation. In turn, aberrant 3D structures play a vital role in developing abnormalities and diseases such as cancer. This review discusses key 3D chromatin structures (topologically associating domain, lamina-associated domain, and enhancer-promoter interactions) and corresponding structural protein elements mediating 3D chromatin interactions [CCCTC-binding factor, polycomb group protein, cohesin, and Brother of the Regulator of Imprinted Sites (BORIS) protein] with a highlight of their associations with cancer. We also summarise the recent development of technologies and bioinformatics approaches to study the 3D chromatin interactions in gene expression regulation, including crosslinking and proximity ligation methods in the bulk cell population (ChIA-PET and HiChIP) or single-molecule resolution (ChIA-drop), and methods other than proximity ligation, such as GAM, SPRITE, and super-resolution microscopy techniques.

Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies

Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze these large and complex datasets, computational methods are undergoing rapid and expansive evolution. Thus, a thorough evaluation of these analytical pipelines is necessary to identify which commonly used algorithms and processing pipelines need to be improved. Here we present a comprehensive benchmark framework, Bacon, to evaluate the performance of several computational methods. Finally, we provide practical recommendations for users working with HiChIP and/or ChIA-PET analyses.

TAD cliques predict key features of chromatin organization

BACKGROUND

Mechanisms underlying genome 3D organization and domain formation in the mammalian nucleus are not completely understood. Multiple processes such as transcriptional compartmentalization, DNA loop extrusion and interactions with the nuclear lamina dynamically act on chromatin at multiple levels. Here, we explore long-range interaction patterns between topologically associated domains (TADs) in several cell types.

RESULTS

We find that TAD long-range interactions are connected to many key features of chromatin organization, including open and closed compartments, compaction and loop extrusion processes. Domains that form large TAD cliques tend to be repressive across cell types, when comparing gene expression, LINE/SINE repeat content and chromatin subcompartments. Further, TADs in large cliques are larger in genomic size, less dense and depleted of convergent CTCF motifs, in contrast to smaller and denser TADs formed by a loop extrusion process.

CONCLUSIONS

Our results shed light on the organizational principles that govern repressive and active domains in the human genome.

Oncogenic extrachromosomal DNA functions as mobile enhancers to globally amplify chromosomal transcription

Extrachromosomal, circular DNA (ecDNA) is emerging as a prevalent yet less characterized oncogenic alteration in cancer genomes. We leverage ChIA-PET and ChIA-Drop chromatin interaction assays to characterize genome-wide ecDNA-mediated chromatin contacts that impact transcriptional programs in cancers. ecDNAs in glioblastoma patient-derived neurosphere and prostate cancer cell cultures are marked by widespread intra-ecDNA and genome-wide chromosomal interactions. ecDNA-chromatin contact foci are characterized by broad and high-level H3K27ac signals converging predominantly on chromosomal genes of increased expression levels. Prostate cancer cells harboring synthetic ecDNA circles composed of characterized enhancers result in the genome-wide activation of chromosomal gene transcription. Deciphering the chromosomal targets of ecDNAs at single-molecule resolution reveals an association with actively expressed oncogenes spatially clustered within ecDNA-directed interaction networks. Our results suggest that ecDNA can function as mobile transcriptional enhancers to promote tumor progression and manifest a potential synthetic aneuploidy mechanism of transcription control in cancer.

ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments.

Accurate loop calling for 3D genomic data with cLoops

MOTIVATION

Sequencing-based 3D genome mapping technologies can identify loops formed by interactions between regulatory elements hundreds of kilobases apart. Existing loop-calling tools are mostly restricted to a single data type, with accuracy dependent on a predefined resolution contact matrix or called peaks, and can have prohibitive hardware costs.

RESULTS

Here, we introduce cLoops ('see loops') to address these limitations. cLoops is based on the clustering algorithm cDBSCAN that directly analyzes the paired-end tags (PETs) to find candidate loops and uses a permuted local background to estimate statistical significance. These two data-type-independent processes enable loops to be reliably identified for both sharp and broad peak data, including but not limited to ChIA-PET, Hi-C, HiChIP and Trac-looping data. Loops identified by cLoops showed much less distance-dependent bias and higher enrichment relative to local regions than existing tools. Altogether, cLoops improves accuracy of detecting of 3D-genomic loops from sequencing data, is versatile, flexible, efficient, and has modest hardware requirements.

AVAILABILITY AND IMPLEMENTATION

cLoops with documentation and example data are freely available at: https://github.com/YaqiangCao/cLoops.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MACPET: model-based analysis for ChIA-PET

We present model-based analysis for ChIA-PET (MACPET), which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET and searches for binding sites in a two-dimensional space, while taking into account different noise levels in different genomic regions. MACPET shows favorable results compared with MACS in terms of motif occurrence and spatial resolution. Furthermore, significant binding sites discovered by MACPET are involved in a higher number of significant three-dimensional interactions than those discovered by MACS. MACPET is freely available on Bioconductor. ChIA-PET; MACPET; Model-based clustering; Paired-end tags; Peak-calling algorithm.

Mapping the Global Chromatin Connectivity Network for Sox2 Function in Neural Stem Cell Maintenance

The SOX2 transcription factor is critical for neural stem cell (NSC) maintenance and brain development. Through chromatin immunoprecipitation (ChIP) and chromatin interaction analysis (ChIA-PET), we determined genome-wide SOX2-bound regions and Pol II-mediated long-range chromatin interactions in brain-derived NSCs. SOX2-bound DNA was highly enriched in distal chromatin regions interacting with promoters and carrying epigenetic enhancer marks. Sox2 deletion caused widespread reduction of Pol II-mediated long-range interactions and decreased gene expression. Genes showing reduced expression in Sox2-deleted cells were significantly enriched in interactions between promoters and SOX2-bound distal enhancers. Expression of one such gene, Suppressor of Cytokine Signaling 3 (Socs3), rescued the self-renewal defect of Sox2-ablated NSCs. Our work identifies SOX2 as a major regulator of gene expression through connections to the enhancer network in NSCs. Through the definition of such a connectivity network, our study shows the way to the identification of genes and enhancers involved in NSC maintenance and neurodevelopmental disorders.

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Sox2-bound enhancers are enriched within long-range interactions in neural stem cells

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SOX2 loss decreases chromatin interactivity genome-wide

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Sox2-bound enhancers from interactions activate reporter genes in zebrafish forebrain

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Socs3, a gene downregulated in Sox2 mutant NSCs, rescues their self-renewal

Chromatin interaction analyses elucidate the roles of PRC2-bound silencers in mouse development

In metazoan development, lineage specific gene expression is modulated by the delicate balance between transcription activation and repression. Despite much of our knowledge in the enhancer-centered transcription activation, silencers and their roles in normal development are poorly understood. Here, we performed chromatin interaction analyses of Polycomb repressive complex 2 (PRC2), a key regulator inducing transcriptional gene silencing, to uncover silencers, their molecular identity and associated chromatin connectivity. Systematic analysis of the cis-regulatory silencer elements reveals their chromatin features and gene targeting specificity. Deletion of these PRC2-bound silencers in mice results in transcriptional derepression of their interacting genes and pleiotropic developmental phenotypes, including embryonic lethality. While functioning as PRC2-bound silencers in pluripotent cells, they can transition into active tissue-specific enhancers during development, suggesting their regulatory versatility. Our study characterizes the molecular nature of silencers, their associated chromatin architectures, and offers the exciting possibility of targeted re-activation of epigenetically silenced genes.

MIA-Sig: multiplex chromatin interaction analysis by signal processing and statistical algorithms

The single-molecule multiplex chromatin interaction data are generated by emerging 3D genome mapping technologies such as GAM, SPRITE, and ChIA-Drop. These datasets provide insights into high-dimensional chromatin organization, yet introduce new computational challenges. Thus, we developed MIA-Sig, an algorithmic solution based on signal processing and information theory. We demonstrate its ability to de-noise the multiplex data, assess the statistical significance of chromatin complexes, and identify topological domains and frequent inter-domain contacts. On chromatin immunoprecipitation (ChIP)-enriched data, MIA-Sig can clearly distinguish the protein-associated interactions from the non-specific topological domains. Together, MIA-Sig represents a novel algorithmic framework for multiplex chromatin interaction analysis.

High resolution discovery of chromatin interactions

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a method for the genome-wide de novo discovery of chromatin interactions. Existing computational methods typically fail to detect weak or dynamic interactions because they use a peak-calling step that ignores paired-end linkage information. We have developed a novel computational method called Chromatin Interaction Discovery (CID) to overcome this limitation with an unbiased clustering approach for interaction discovery. CID outperforms existing chromatin interaction detection methods with improved sensitivity, replicate consistency, and concordance with other chromatin interaction datasets. In addition, CID also outperforms other methods in discovering chromatin interactions from HiChIP data. We expect that the CID method will be valuable in characterizing 3D chromatin interactions and in understanding the functional consequences of disease-associated distal genetic variations.

Identification of significant chromatin contacts from HiChIP data by FitHiChIP

HiChIP/PLAC-seq is increasingly becoming popular for profiling 3D chromatin contacts among regulatory elements and for annotating functions of genetic variants. Here we describe FitHiChIP, a computational method for loop calling from HiChIP/PLAC-seq data, which jointly models the non-uniform coverage and genomic distance scaling of contact counts to compute statistical significance estimates. We also develop a technique to filter putative bystander loops that can be explained by stronger adjacent loops. Compared to existing methods, FitHiChIP performs better in recovering contacts reported by Hi-C, promoter capture Hi-C and ChIA-PET experiments and in capturing previously validated promoter-enhancer interactions. FitHiChIP loop calls are reproducible among replicates and are consistent across different experimental settings. Our work also provides a framework for differential HiChIP analysis with an option to utilize ChIP-seq data for further characterizing differential loops. Even though designed for HiChIP, FitHiChIP is also applicable to other conformation capture assays.

HiChIP/PLAC-seq assay is popular for profiling 3D genome interactions among regulatory elements at kilobase resolution. Here the authors describe FitHiChIP an empirical null-based, flexible computational method for statistical significance estimation and loop calling from HiChIP data.

Chromatin Interaction Analysis with Updated ChIA-PET Tool (V3)

Understanding chromatin interactions is important because they create chromosome conformation and link the cis- and trans- regulatory elements to their target genes for transcriptional regulation. Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing is a genome-wide high-throughput technology that detects chromatin interactions associated with a specific protein of interest. We developed ChIA-PET Tool for ChIA-PET data analysis in 2010. Here, we present the updated version of ChIA-PET Tool (V3) as a computational package to process the next-generation sequence data generated from ChIA-PET experiments. It processes short-read and long-read ChIA-PET data with multithreading and generates statistics of results in an HTML file. In this paper, we provide a detailed demonstration of the design of ChIA-PET Tool V3 and how to install it and analyze RNA polymerase II (RNAPII) ChIA-PET data from human K562 cells with it. We compared our tool with existing tools, including ChiaSig, MICC, Mango and ChIA-PET2, by using the same public data set in the same computer. Most peaks detected by the ChIA-PET Tool V3 overlap with those of other tools. There is higher enrichment for significant chromatin interactions from ChIA-PET Tool V3 in aggregate peak analysis (APA) plots. The ChIA-PET Tool V3 is publicly available at GitHub.

ChIAPoP: a new tool for ChIA-PET data analysis

Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET) is a popular assay method for studying genome-wide chromatin interactions mediated by a protein of interest. The main goal of ChIA-PET data analysis is to detect interactions between DNA regions. Here, we propose a new method and the associated data analysis pipeline, ChIAPoP, to detect chromatin interactions from ChIA-PET data. We compared ChIAPoP with other popular methods, including a hypergeometric model (used in ChIA-PET tool), MICC (used in ChIA-PET2), ChiaSig and mango. The results showed that ChIA-PoP performed better than or at least as well as these top existing methods in detecting true chromatin interactions. ChIAPoP is freely available to the public at https://github.com/wh90999/ChIAPoP.

Long-range interactions between topologically associating domains shape the four-dimensional genome during differentiation

Genomic information is selectively used to direct spatial and temporal gene expression during differentiation. Interactions between topologically associating domains (TADs) and between chromatin and the nuclear lamina organize and position chromosomes in the nucleus. However, how these genomic organizers together shape genome architecture is unclear. Here, using a dual-lineage differentiation system, we report long-range TAD-TAD interactions that form constitutive and variable TAD cliques. A differentiation-coupled relationship between TAD cliques and lamina-associated domains suggests that TAD cliques stabilize heterochromatin at the nuclear periphery. We also provide evidence of dynamic TAD cliques during mouse embryonic stem-cell differentiation and somatic cell reprogramming and of inter-TAD associations in single-cell high-resolution chromosome conformation capture (Hi-C) data. TAD cliques represent a level of four-dimensional genome conformation that reinforces the silencing of repressed developmental genes.

Computational methods for analyzing and modeling genome structure and organization

Recent advances in chromosome conformation capture technologies have led to the discovery of previously unappreciated structural features of chromatin. Computational analysis has been critical in detecting these features and thereby helping to uncover the building blocks of genome architecture. Algorithms are being developed to integrate these architectural features to construct better three-dimensional (3D) models of the genome. These computational methods have revealed the importance of 3D genome organization to essential biological processes. In this article, we review the state of the art in analytic and modeling techniques with a focus on their application to answering various biological questions related to chromatin structure. We summarize the limitations of these computational techniques and suggest future directions, including the importance of incorporating multiple sources of experimental data in building a more comprehensive model of the genome. This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Genetic/Genomic Methods Models of Systems Properties and Processes > Mechanistic Models.

Characteristics of allelic gene expression in human brain cells from single-cell RNA-seq data analysis

Background

Monoallelic expression of autosomal genes has been implicated in human psychiatric disorders. However, there is a paucity of allelic expression studies in human brain cells at the single cell and genome wide levels.

Results

In this report, we reanalyzed a previously published single-cell RNA-seq dataset from several postmortem human brains and observed pervasive monoallelic expression in individual cells, largely in a random manner. Examining single nucleotide variants with a predicted functional disruption, we found that the “damaged” alleles were overall expressed in fewer brain cells than their counterparts, and at a lower level in cells where their expression was detected. We also identified many brain cell type-specific monoallelically expressed genes. Interestingly, many of these cell type-specific monoallelically expressed genes were enriched for functions important for those brain cell types. In addition, function analysis showed that genes displaying monoallelic expression and correlated expression across neuronal cells from different individual brains were implicated in the regulation of synaptic function.

Conclusions

Our findings suggest that monoallelic gene expression is prevalent in human brain cells, which may play a role in generating cellular identity and neuronal diversity and thus increasing the complexity and diversity of brain cell functions.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4261-x) contains supplementary material, which is available to authorized users.

Carcinogen susceptibility is regulated by genome architecture and predicts cancer mutagenesis

The development of many sporadic cancers is directly initiated by carcinogen exposure. Carcinogens induce malignancies by creating DNA lesions (i.e., adducts) that can result in mutations if left unrepaired. Despite this knowledge, there has been remarkably little investigation into the regulation of susceptibility to acquire DNA lesions. In this study, we present the first quantitative human genome-wide map of DNA lesions induced by ultraviolet (UV) radiation, the ubiquitous carcinogen in sunlight that causes skin cancer. Remarkably, the pattern of carcinogen susceptibility across the genome of primary cells significantly reflects mutation frequency in malignant melanoma. Surprisingly, DNase-accessible euchromatin is protected from UV, while lamina-associated heterochromatin at the nuclear periphery is vulnerable. Many cancer driver genes have an intrinsic increase in carcinogen susceptibility, including the BRAF oncogene that has the highest mutation frequency in melanoma. These findings provide a genome-wide snapshot of DNA injuries at the earliest stage of carcinogenesis. Furthermore, they identify carcinogen susceptibility as an origin of genome instability that is regulated by nuclear architecture and mirrors mutagenesis in cancer.

Chrom3D: three-dimensional genome modeling from Hi-C and nuclear lamin-genome contacts

Current three-dimensional (3D) genome modeling platforms are limited by their inability to account for radial placement of loci in the nucleus. We present Chrom3D, a user-friendly whole-genome 3D computational modeling framework that simulates positions of topologically-associated domains (TADs) relative to each other and to the nuclear periphery. Chrom3D integrates chromosome conformation capture (Hi-C) and lamin-associated domain (LAD) datasets to generate structure ensembles that recapitulate radial distributions of TADs detected in single cells. Chrom3D reveals unexpected spatial features of LAD regulation in cells from patients with a laminopathy-causing lamin mutation. Chrom3D is freely available on github.

Genome Annotation

The dynamic structure and functions of genomes are being revealed simultaneously with the progress of technologies and researches in genomics. Evidence indicating genome regional characteristics (genome annotations in a broad sense) provide the basis for further analyses. Target listing and screening can be effectively performed in silico using such data. Here, we describe steps to obtain publicly available genome annotations or to construct new annotations based on your own analyses, as well as an overview of the types of available genome annotations and corresponding resources.

An integrated 3-Dimensional Genome Modeling Engine for data-driven simulation of spatial genome organization

ChIA-PET is a high-throughput mapping technology that reveals long-range chromatin interactions and provides insights into the basic principles of spatial genome organization and gene regulation mediated by specific protein factors. Recently, we showed that a single ChIA-PET experiment provides information at all genomic scales of interest, from the high-resolution locations of binding sites and enriched chromatin interactions mediated by specific protein factors, to the low resolution of nonenriched interactions that reflect topological neighborhoods of higher-order chromosome folding. This multilevel nature of ChIA-PET data offers an opportunity to use multiscale 3D models to study structural-functional relationships at multiple length scales, but doing so requires a structural modeling platform. Here, we report the development of 3D-GNOME (3-Dimensional Genome Modeling Engine), a complete computational pipeline for 3D simulation using ChIA-PET data. 3D-GNOME consists of three integrated components: a graph-distance-based heat map normalization tool, a 3D modeling platform, and an interactive 3D visualization tool. Using ChIA-PET and Hi-C data derived from human B-lymphocytes, we demonstrate the effectiveness of 3D-GNOME in building 3D genome models at multiple levels, including the entire genome, individual chromosomes, and specific segments at megabase (Mb) and kilobase (kb) resolutions of single average and ensemble structures. Further incorporation of CTCF-motif orientation and high-resolution looping patterns in 3D simulation provided additional reliability of potential biologically plausible topological structures.

Statistical challenges in analyzing methylation and long-range chromosomal interaction data

With the rapid development of high throughput technologies such as array and next generation sequencing (NGS), genome-wide, nucleotide-resolution epigenomic data are increasingly available. In recent years, there has been particular interest in data on DNA methylation and 3-dimensional (3D) chromosomal organization, which are believed to hold keys to understand biological mechanisms, such as transcription regulation, that are closely linked to human health and diseases. However, small sample size, complicated correlation structure, substantial noise, biases, and uncertainties, all present difficulties for performing statistical inference. In this review, we present an overview of the new technologies that are frequently utilized in studying DNA methylation and 3D chromosomal organization. We focus on reviewing recent developments in statistical methodologies designed for better interrogating epigenomic data, pointing out statistical challenges facing the field whenever appropriate.

ChIA-PET2: a versatile and flexible pipeline for ChIA-PET data analysis

ChIA-PET2 is a versatile and flexible pipeline for analyzing different types of ChIA-PET data from raw sequencing reads to chromatin loops. ChIA-PET2 integrates all steps required for ChIA-PET data analysis, including linker trimming, read alignment, duplicate removal, peak calling and chromatin loop calling. It supports different kinds of ChIA-PET data generated from different ChIA-PET protocols and also provides quality controls for different steps of ChIA-PET analysis. In addition, ChIA-PET2 can use phased genotype data to call allele-specific chromatin interactions. We applied ChIA-PET2 to different ChIA-PET datasets, demonstrating its significantly improved performance as well as its ability to easily process ChIA-PET raw data. ChIA-PET2 is available at https://github.com/GuipengLi/ChIA-PET2.

4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation?

Genome-wide sequencing technologies enable investigations of the structural properties of the genome in various spatial dimensions. Here, we review computational techniques developed to model the three-dimensional genome in single cells versus ensembles of cells and assess their underlying assumptions. We further address approaches to study the spatio-temporal aspects of genome organization from single-cell data.

Activation of proto-oncogenes by disruption of chromosome neighborhoods

Oncogenes are activated through well-known chromosomal alterations such as gene fusion, translocation, and focal amplification. In light of recent evidence that the control of key genes depends on chromosome structures called insulated neighborhoods, we investigated whether proto-oncogenes occur within these structures and whether oncogene activation can occur via disruption of insulated neighborhood boundaries in cancer cells. We mapped insulated neighborhoods in T cell acute lymphoblastic leukemia (T-ALL) and found that tumor cell genomes contain recurrent microdeletions that eliminate the boundary sites of insulated neighborhoods containing prominent T-ALL proto-oncogenes. Perturbation of such boundaries in nonmalignant cells was sufficient to activate proto-oncogenes. Mutations affecting chromosome neighborhood boundaries were found in many types of cancer. Thus, oncogene activation can occur via genetic alterations that disrupt insulated neighborhoods in malignant cells.

ChIP-Seq Data Analysis to Define Transcriptional Regulatory Networks

The first step in the definition of transcriptional regulatory networks is to establish correct relationships between transcription factors (TFs) and their target genes, together with the effect of their regulatory activity (activator or repressor). Fundamental advances in this direction have been made possible by the introduction of experimental techniques such as Chromatin Immunoprecipitation, which, coupled with next-generation sequencing technologies (ChIP-Seq), permit the genome-wide identification of TF binding sites. This chapter provides a survey on how data of this kind are to be processed and integrated with expression and other types of data to infer transcriptional regulatory rules and codes.

GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data

Background

Precise quantitative and spatiotemporal control of gene expression is necessary to ensure proper cellular differentiation and the maintenance of homeostasis. The relationship between gene expression and the spatial organisation of chromatin is highly complex, interdependent and not completely understood. The development of experimental techniques to interrogate both the higher-order structure of chromatin and the interactions between regulatory elements has recently lead to important insights on how gene expression is controlled. The ability to gain these and future insights is critically dependent on computational tools for the analysis and visualisation of data produced by these techniques.

Results and conclusion

We have developed GenomicInteractions, a freely available R/Bioconductor package designed for processing, analysis and visualisation of data generated from various types of chromosome conformation capture experiments. The package allows the easy annotation and summarisation of large genome-wide datasets at both the level of individual interactions and sets of genomic features, and provides several different methods for interrogating and visualising this type of data. We demonstrate this package’s utility by showing example analyses performed on interaction datasets generated using Hi-C and ChIA-PET.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2140-x) contains supplementary material, which is available to authorized users.

MICC: an R package for identifying chromatin interactions from ChIA-PET data

ChIA-PET is rapidly emerging as an important experimental approach to detect chromatin long-range interactions at high resolution. Here, we present Model based Interaction Calling from ChIA-PET data (MICC), an easy-to-use R package to detect chromatin interactions from ChIA-PET sequencing data. By applying a Bayesian mixture model to systematically remove random ligation and random collision noise, MICC could identify chromatin interactions with a significantly higher sensitivity than existing methods at the same false discovery rate.

AVAILABILITY AND IMPLEMENTATION

http://bioinfo.au.tsinghua.edu.cn/member/xwwang/MICCusage

CONTACT

michael.zhang@utdallas.edu or xwwang@tsinghua.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mango: a bias-correcting ChIA-PET analysis pipeline

MOTIVATION

Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET) is an established method for detecting genome-wide looping interactions at high resolution. Current ChIA-PET analysis software packages either fail to correct for non-specific interactions due to genomic proximity or only address a fraction of the steps required for data processing. We present Mango, a complete ChIA-PET data analysis pipeline that provides statistical confidence estimates for interactions and corrects for major sources of bias including differential peak enrichment and genomic proximity.

RESULTS

Comparison to the existing software packages, ChIA-PET Tool and ChiaSig revealed that Mango interactions exhibit much better agreement with high-resolution Hi-C data. Importantly, Mango executes all steps required for processing ChIA-PET datasets, whereas ChiaSig only completes 20% of the required steps. Application of Mango to multiple available ChIA-PET datasets permitted the independent rediscovery of known trends in chromatin loops including enrichment of CTCF, RAD21, SMC3 and ZNF143 at the anchor regions of interactions and strong bias for convergent CTCF motifs.

AVAILABILITY AND IMPLEMENTATION

Mango is open source and distributed through github at https://github.com/dphansti/mango.

CONTACT

mpsnyder@standford.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing technology and application

Background

Long-range chromatin interactions play an important role in transcription regulation. Chromatin Interaction Analysis with Paired-End-Tag sequencing (ChIA-PET) is an emerging technology that has unique advantages in chromatin interaction analysis, and thus provides insight into the study of transcription regulation.

Results

This article introduces the experimental protocol and data analysis process of ChIA-PET, as well as discusses some applications using this technology. It also unveils the direction of future studies based on this technology.

Conclusions

Overall we show that ChIA-PET is the cornerstone to explore the three-dimensional (3D) chromatin structure, and certainly will lead the forthcoming wave of 3D genomics studies.