Methods for Genome-Wide Chromatin Interaction Analysis.

Recent analyses revealed the essential function of chromatin structure in maintaining and regulating genomic information. Advancements in microscopy, nuclear structure observation techniques, and the development of methods utilizing next-generation sequencers (NGSs) have significantly progressed these discoveries. Methods utilizing NGS enable genome-wide analysis, which is challenging with microscopy, and have elucidated concepts of important chromatin structures such as a loop structure, a domain structure called topologically associating domains (TADs), and compartments. In this chapter, I introduce chromatin interaction techniques using NGS and outline the principles and features of each method.

Read Mapping for Hi-C Analysis.

Hi-C is a popular ligation-based technique to detect 3D physical chromosome structure within the nucleus using cross-linking and next-generation sequencing. As an unbiased genome-wide assay based on chromosome conformation capture, it provides rich insights into chromosome structure, dynamic chromosome folding and interactions, and the regulatory state of a cell. Bioinformatics analyses of Hi-C data require dedicated protocols as most genome alignment tools assume that both paired-end reads will map to the same chromosome, resulting in large two-dimensional matrices as processed data. Here, we outline the necessary steps to generate high-quality aligned Hi-C data by separately mapping each read while correcting for biases from restriction enzyme digests. We introduce our own custom open-source pipeline, which enables users to select an aligner of their choosing with high accuracy and performance. This enables users to generate high-resolution datasets with fast turnaround and fewer unmapped reads. Finally, we discuss recent innovations in experimental techniques, bioinformatics techniques, and their applications in clinical testing for diagnostics.

Exploring Contact Distance Distributions with Google Colaboratory.

Hi-C and Micro-C are the three-dimensional (3D) genome assays that use high-throughput sequencing. In the analysis, the sequenced paired-end reads are mapped to a reference genome to generate a two-dimensional contact matrix for identifying topologically associating domains (TADs), chromatin loops, and chromosomal compartments. On the other hand, the distance distribution of the paired-end mapped reads also provides insight into the 3D genome structure by highlighting global contact frequency patterns at distances indicative of loops, TADs, and compartments. This chapter presents a basic workflow for visualizing and analyzing contact distance distributions from Hi-C data. The workflow can be run on Google Colaboratory, which provides a ready-to-use Python environment accessible through a web browser. The notebook that demonstrates the workflow is available in the GitHub repository at https://github.com/rnakato/Springer_contact_distance_plot.

Systematic Inference of Multi-scale Chromatin Sub-compartments Using Calder2.

The compartmentalization of chromatin reflects its underlying biological activities. Inferring chromatin sub-compartments using Hi-C data is challenged by data resolution constraints. Consequently, comprehensive characterizations of sub-compartments have been limited to a select number of Hi-C experiments, with systematic comparisons across a wide range of tissues and conditions still lacking. Our original Calder algorithm marked a significant advancement in this field, enabling the identification of multi-scale sub-compartments at various data resolutions and facilitating the inference and comparison of chromatin architecture in over 100 datasets. Building on this foundation, we introduce Calder2, an updated version of Calder that brings notable improvements. These include expanded support for a wider array of genomes and organisms, an optimized bin size selection approach for more accurate chromatin compartment detection, and extended support for input and output formats. Calder2 thus stands as a refined analysis tool, significantly advancing genome-wide studies of 3D chromatin architecture and its functional implications.

CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.

Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline .

Step-by-Step Protocol to Generate Hi-C Contact Maps Using the rfy_hic2 Pipeline.

The Hi-C method has emerged as an indispensable tool for analyzing the 3D organization of the genome, becoming increasingly accessible and frequently utilized in chromatin research. To effectively leverage 3D genomics data obtained through advanced technologies, it is crucial to understand what processes are undertaken and what aspects require special attention within the bioinformatics pipeline. This protocol aims to demystify the Hi-C data analysis process for field newcomers. In a step-by-step manner, we describe how to process Hi-C data, from the initial sequencing of the Hi-C library to the final visualization of Hi-C contact data as heatmaps. Each step of the analysis is clearly explained to ensure an understanding of the procedures and their objectives. By the end of this chapter, readers will be equipped with the knowledge to transform raw Hi-C reads into informative visual representations, facilitating a deeper comprehension of the spatial genomic structures critical to cellular functions.

Supervised Chromatin Loop Detection Using Peakachu Version 2.

Peakachu is a supervised-learning-based approach that identifies chromatin loops from chromatin contact data. Here, we present Peakachu version 2, an updated version that significantly improves extensibility, usability, and computational efficiency compared to its predecessor. It features pretrained models tailored for a wide range of experimental platforms, such as Hi-C, Micro-C, ChIA-PET, HiChIP, HiCAR, and TrAC-loop. This chapter offers a step-by-step tutorial guiding users through the process of training Peakachu models from scratch and utilizing pretrained models to predict chromatin loops across various platforms.

Integrative Modeling of 3D Genome Organization by Bayesian Molecular Dynamics Simulations with Hi-C Metainference.

Polymer modeling has been playing an increasingly important role in complementing 3D genome experiments, both to aid their interpretation and to reveal the underlying molecular mechanisms. This chapter illustrates an application of Hi-C metainference, a Bayesian approach to explore the 3D organization of a target genomic region by integrating experimental contact frequencies into a prior model of chromatin. The method reconstructs the conformational ensemble of the target locus by combining molecular dynamics simulation and Monte Carlo sampling from the posterior probability distribution given the data. Using prior chromatin models at both 1 kb and nucleosome resolution, we apply this approach to a 30 kb locus of mouse embryonic stem cells consisting of two well-defined domains linking several gene promoters together. Retaining the advantages of both physics-based and data-driven strategies, Hi-C metainference can provide an experimentally consistent representation of the system while at the same time retaining molecular details necessary to derive physical insights.

Prediction of Enhancer-Gene Interactions Using Chromatin-Conformation Capture and Epigenome Data Using STARE.

Disentangling the relationship of enhancers and genes is an ongoing challenge in epigenomics. We present STARE, our software to quantify the strength of enhancer-gene interactions based on enhancer activity and chromatin contact data. It implements the generalized Activity-by-Contact (gABC) score, which allows predicting putative target genes of candidate enhancers over any desired genomic distance. The only requirement for its application is a measurement of enhancer activity. In addition to regulatory interactions, STARE calculates transcription factor (TF) affinities on gene level. We illustrate its usage on a public single-cell data set of the human heart by predicting regulatory interactions on cell type level, by giving examples on how to integrate them with other data modalities, and by constructing TF affinity matrices.

4D Genome Analysis Using PHi-C2.

Hi-C methods reveal 3D genome features but lack correspondence to dynamic chromatin behavior. PHi-C2, Python software, addresses this gap by transforming Hi-C data into polymer models. After the optimization algorithm, it enables us to calculate 3D conformations and conduct dynamic simulations, providing insights into chromatin dynamics, including the mean-squared displacement and rheological properties. This chapter introduces PHi-C2 usage, offering a tutorial for comprehensive 4D genome analysis.

Three-Dimensional Simulation of Whole-Genome Structuring Through the Transition from Anaphase to Interphase.

In order to analyze the three-dimensional genome architecture, it is important to simulate how the genome is structured through the cell cycle progression. In this chapter, we present the usage of our computation codes for simulating how the human genome is formed as the cell transforms from anaphase to interphase. We do not use the global Hi-C data as an input into the genome simulation but represent all chromosomes as linear polymers annotated by the neighboring region contact index (NCI), which classifies the A/B type of each local chromatin region. The simulated mitotic chromosomes heterogeneously expand upon entry to the G1 phase, which induces phase separation of A and B chromatin regions, establishing chromosome territories, compartments, and lamina and nucleolus associations in the interphase nucleus. When the appropriate one-dimensional chromosomal annotation is possible, using the protocol of this chapter, one can quantitatively simulate the three-dimensional genome structure and dynamics of human cells of interest.

Exploring Cohesin-Related Multiomics Information in the Web Browser.

Cohesin is a protein complex that plays a key role in regulating chromosome structure and gene expression. While next-generation sequencing technologies have provided extensive information on various aspects of cohesin, integrating and exploring the vast datasets associated with cohesin are not straightforward. CohesinDB ( https://cohesindb.iqb.u-tokyo.ac.jp ) offers a web-based interface for browsing, searching, analyzing, visualizing, and downloading comprehensive multiomics cohesin information in human cells. In this protocol, we introduce how to utilize CohesinDB to facilitate research on transcriptional regulation and chromatin organization.

Machine and Deep Learning Methods for Predicting 3D Genome Organization.

Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers and transcription factor binding site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, and TAD boundaries) and analyze their pros and cons. We also point out obstacles to the computational prediction of 3D interactions and suggest future research directions.

Improved simultaneous mapping of epigenetic features and 3D chromatin structure via ViCAR.

Methods to measure chromatin contacts at genomic regions bound by histone modifications or proteins are important tools to investigate chromatin organization. However, such methods do not capture the possible involvement of other epigenomic features such as G-quadruplex DNA secondary structures (G4s). To bridge this gap, we introduce ViCAR (viewpoint HiCAR), for the direct antibody-based capture of chromatin interactions at folded G4s. Through ViCAR, we showcase the first G4-3D interaction landscape. Using histone marks, we also demonstrate how ViCAR improves on earlier approaches yielding increased signal-to-noise. ViCAR is a practical and powerful tool to explore epigenetic marks and 3D genome interactomes.

Unraveling the key role of chromatin structure in cancer development through epigenetic landscape characterization of oral cancer.

Epigenetic alterations, such as those in chromatin structure and DNA methylation, have been extensively studied in a number of tumor types. But oral cancer, particularly oral adenocarcinoma, has received far less attention. Here, we combined laser-capture microdissection and muti-omics mini-bulk sequencing to systematically characterize the epigenetic landscape of oral cancer, including chromatin architecture, DNA methylation, H3K27me3 modification, and gene expression. In carcinogenesis, tumor cells exhibit reorganized chromatin spatial structures, including compromised compartment structures and altered gene-gene interaction networks. Notably, some structural alterations are observed in phenotypically non-malignant paracancerous but not in normal cells. We developed transformer models to identify the cancer propensity of individual genome loci, thereby determining the carcinogenic status of each sample. Insights into cancer epigenetic landscapes provide evidence that chromatin reorganization is an important hallmark of oral cancer progression, which is also linked with genomic alterations and DNA methylation reprogramming. In particular, regions of frequent copy number alternations in cancer cells are associated with strong spatial insulation in both cancer and normal samples. Aberrant methylation reprogramming in oral squamous cell carcinomas is closely related to chromatin structure and H3K27me3 signals, which are further influenced by intrinsic sequence properties. Our findings indicate that structural changes are both significant and conserved in two distinct types of oral cancer, closely linked to transcriptomic alterations and cancer development. Notably, the structural changes remain markedly evident in oral adenocarcinoma despite the considerably lower incidence of genomic copy number alterations and lesser extent of methylation alterations compared to squamous cell carcinoma. We expect that the comprehensive analysis of epigenetic reprogramming of different types and subtypes of primary oral tumors can provide additional guidance to the design of novel detection and therapy for oral cancer.

Spectral-based detection of chromatin loops in multiplexed super-resolution FISH data.

Involved in mitotic condensation, interaction of transcriptional regulatory elements and isolation of structural domains, loop formation has become a paradigm in the deciphering of chromatin architecture and its functional role. Despite the emergence of increasingly powerful genome visualization techniques, the high variability in cell populations and the randomness of conformations still make loop detection a challenge. We introduce an approach for determining the presence and frequency of loops in a collection of experimental conformations obtained by multiplexed super-resolution imaging. Based on a spectral approach, in conjunction with neural networks, this method offers a powerful tool to detect loops in large experimental data sets, both at the population and single-cell levels. The method's performance is confirmed on experimental FISH data where Hi-C and other loop detection results are available. The method is then applied to recently published experimental data, where it provides a detailed and statistically quantified description of the global architecture of the chromosomal region under study.

Extracting regulatory active chromatin footprint from cell-free DNA.

Cell-free DNA (cfDNA) has emerged as a pivotal player in precision medicine, revolutionizing the diagnostic and therapeutic landscape. While its clinical applications have significantly increased in recent years, current cfDNA assays have limited ability to identify the active transcriptional programs that govern complex disease phenotypes and capture the heterogeneity of the disease. To address these limitations, we have developed a non-invasive platform to enrich and examine the active chromatin fragments (cfDNAac) in peripheral blood. The deconvolution of the cfDNAac signal from traditional nucleosomal chromatin fragments (cfDNAnuc) yields a catalog of features linking these circulating chromatin signals in blood to specific regulatory elements across the genome, including enhancers, promoters, and highly transcribed genes, mirroring the epigenetic data from the ENCODE project. Notably, these cfDNAac counts correlate strongly with RNA polymerase II activity and exhibit distinct expression patterns for known circadian genes. Additionally, cfDNAac signals across gene bodies and promoters show strong correlations with whole blood gene expression levels defined by GTEx. This study illustrates the utility of cfDNAac analysis for investigating epigenomics and gene expression, underscoring its potential for a wide range of clinical applications in precision medicine.

Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers.

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type-specific features. Using organ-matched healthy tissues, we identified the "nearest healthy" cell types in diverse cancers, demonstrating that the chromatin signature of basal-like-subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.