Hi-C analysis: from data generation to integration

HiCDiff: single-cell Hi-C data denoising with diffusion models

The genome-wide single-cell chromosome conformation capture technique, i.e. single-cell Hi-C (ScHi-C), was recently developed to interrogate the conformation of the genome of individual cells. However, single-cell Hi-C data are much sparser than bulk Hi-C data of a population of cells, and noise in single-cell Hi-C makes it difficult to apply and analyze them in biological research. Here, we developed the first generative diffusion models (HiCDiff) to denoise single-cell Hi-C data in the form of chromosomal contact matrices. HiCDiff uses a deep residual network to remove the noise in the reverse process of diffusion and can be trained in both unsupervised and supervised learning modes. Benchmarked on several single-cell Hi-C test datasets, the diffusion models substantially remove the noise in single-cell Hi-C data. The unsupervised HiCDiff outperforms most supervised non-diffusion deep learning methods and achieves the performance comparable to the state-of-the-art supervised deep learning method in terms of multiple metrics, demonstrating that diffusion models are a useful approach to denoising single-cell Hi-C data. Moreover, its good performance holds on denoising bulk Hi-C data.

Analysis of long-range chromatin contacts, compartments and looping between mouse embryonic stem cells, lens epithelium and lens fibers

BACKGROUND

Nuclear organization of interphase chromosomes involves individual chromosome territories, "open" and "closed" chromatin compartments, topologically associated domains (TADs) and chromatin loops. The DNA- and RNA-binding transcription factor CTCF together with the cohesin complex serve as major organizers of chromatin architecture. Cellular differentiation is driven by temporally and spatially coordinated gene expression that requires chromatin changes of individual loci of various complexities. Lens differentiation represents an advantageous system to probe transcriptional mechanisms underlying tissue-specific gene expression including high transcriptional outputs of individual crystallin genes until the mature lens fiber cells degrade their nuclei.

RESULTS

Chromatin organization between mouse embryonic stem (ES) cells, newborn (P0.5) lens epithelium and fiber cells were analyzed using Hi-C. Localization of CTCF in both lens chromatins was determined by ChIP-seq and compared with ES cells. Quantitative analyses show major differences between number and size of TADs and chromatin loop size between these three cell types. In depth analyses show similarities between lens samples exemplified by overlaps between compartments A and B. Lens epithelium-specific CTCF peaks are found in mostly methylated genomic regions while lens fiber-specific and shared peaks occur mostly within unmethylated DNA regions. Major differences in TADs and loops are illustrated at the ~ 500 kb Pax6 locus, encoding the critical lens regulatory transcription factor and within a larger ~ 15 Mb WAGR locus, containing Pax6 and other loci linked to human congenital diseases. Lens and ES cell Hi-C data (TADs and loops) together with ATAC-seq, CTCF, H3K27ac, H3K27me3 and ENCODE cis-regulatory sites are shown in detail for the Pax6, Sox1 and Hif1a loci, multiple crystallin genes and other important loci required for lens morphogenesis. The majority of crystallin loci are marked by unexpectedly high CTCF-binding across their transcribed regions.

CONCLUSIONS

Our study has generated the first data on 3-dimensional (3D) nuclear organization in lens epithelium and lens fibers and directly compared these data with ES cells. These findings generate novel insights into lens-specific transcriptional gene control, open new research avenues to study transcriptional condensates in lens fiber cells, and enable studies of non-coding genetic variants linked to cataract and other lens and ocular abnormalities.

Epigenetic programming of stochastic olfactory receptor choice

The mammalian sense of smell relies upon a vast array of receptor proteins to detect odorant compounds present in the environment. The proper deployment of these receptor proteins in olfactory sensory neurons is orchestrated by a suite of epigenetic processes that remodel the olfactory genes in differentiating neuronal progenitors. The goal of this review is to elucidate the central role of gene regulatory processes acting in neuronal progenitors of olfactory sensory neurons that lead to a singular expression of an odorant receptor in mature olfactory sensory neurons. We begin by describing the principal features of odorant receptor gene expression in mature olfactory sensory neurons. Next, we delineate our current understanding of how these features emerge from multiple gene regulatory mechanisms acting in neuronal progenitors. Finally, we close by discussing the key gaps in our understanding of how these regulatory mechanisms work and how they interact with each other over the course of differentiation.

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.

Contrasting new and available reference genomes to highlight uncertainties in assemblies and areas for future improvement: an example with monodontid species

BACKGROUND

Reference genomes provide a foundational framework for evolutionary investigations, ecological analysis, and conservation science, yet uncertainties in the assembly of reference genomes are difficult to assess, and by extension rarely quantified. Reference genomes for monodontid cetaceans span a wide spectrum of data types and analytical approaches, providing the context to derive broader insights related to discrepancies and regions of uncertainty in reference genome assembly. We generated three beluga (Delphinapterus leucas) and one narwhal (Monodon monoceros) reference genomes and contrasted these with published chromosomal scale assemblies for each species to quantify discrepancies associated with genome assemblies.

RESULTS

The new reference genomes achieved chromosomal scale assembly using a combination of PacBio long reads, Illumina short reads, and Hi-C scaffolding data. For beluga, we identified discrepancies in the order and orientation of contigs in 2.2-3.7% of the total genome depending on the pairwise comparison of references. In addition, unsupported higher order scaffolding was identified in published reference genomes. In contrast, we estimated 8.2% of the compared narwhal genomes featured discrepancies, with inversions being notably abundant (5.3%). Discrepancies were linked to repetitive elements in both species.

CONCLUSIONS

We provide several new reference genomes for beluga (Delphinapterus leucas), while highlighting potential avenues for improvements. In particular, additional layers of data providing information on ultra-long genomic distances are needed to resolve persistent errors in reference genome construction. The comparative analyses of monodontid reference genomes suggested that the three new reference genomes for beluga are more accurate compared to the currently published reference genome, but that the new narwhal genome is less accurate than one published. We also present a conceptual summary for improving the accuracy of reference genomes with relevance to end-user needs and how they relate to levels of assembly quality and uncertainty.

Telomere-to-telomere and haplotype-resolved genome assembly of the Chinese cork oak (Quercus variabilis)

The Quercus variabilis, a deciduous broadleaved tree species, holds significant ecological and economical value. While a chromosome-level genome for this species has been made available, it remains riddled with unanchored sequences and gaps. In this study, we present a nearly complete comprehensive telomere-to-telomere (T2T) and haplotype-resolved reference genome for Q. variabilis. This was achieved through the integration of ONT ultra-long reads, PacBio HiFi long reads, and Hi-C data. The resultant two haplotype genomes measure 789 Mb and 768 Mb in length, with a contig N50 of 65 Mb and 56 Mb, and were anchored to 12 allelic chromosomes. Within this T2T haplotype-resolved assembly, we predicted 36,830 and 36,370 protein-coding genes, with 95.9% and 96.0% functional annotation for each haplotype genome. The availability of the T2T and haplotype-resolved reference genome lays a solid foundation, not only for illustrating genome structure and functional genomics studies but also to inform and facilitate genetic breeding and improvement of cultivated Quercus species.

The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data

The three-dimensional organization of chromatin plays a crucial role in gene regulation and cellular processes like deoxyribonucleic acid (DNA) transcription, replication and repair. Hi-C and related techniques provide detailed views of spatial proximities within the nucleus. However, data analysis is challenging partially due to a lack of well-defined, underpinning mathematical frameworks. Recently, recognizing and analyzing geometric patterns in Hi-C data has emerged as a powerful approach. This review provides a summary of algorithms for automatic recognition and analysis of geometric patterns in Hi-C data and their correspondence with chromatin structure. We classify existing algorithms on the basis of the data representation and pattern recognition paradigm they make use of. Finally, we outline some of the challenges ahead and promising future directions.

Spatial mapping of the DNA adducts in cancer

DNA adducts and strand breaks are induced by various exogenous and endogenous agents. Accumulation of DNA damage is implicated in many disease processes, including cancer, aging, and neurodegeneration. The continuous acquisition of DNA damage from exogenous and endogenous stressors coupled with defects in DNA repair pathways contribute to the accumulation of DNA damage within the genome and genomic instability. While mutational burden offers some insight into the level of DNA damage a cell may have experienced and subsequently repaired, it does not quantify DNA adducts and strand breaks. Mutational burden also infers the identity of the DNA damage. With advances in DNA adduct detection and quantification methods, there is an opportunity to identify DNA adducts driving mutagenesis and correlate with a known exposome. However, most DNA adduct detection methods require isolation or separation of the DNA and its adducts from the context of the nuclei. Mass spectrometry, comet assays, and other techniques precisely quantify lesion types but lose the nuclear context and even tissue context of the DNA damage. The growth in spatial analysis technologies offers a novel opportunity to leverage DNA damage detection with nuclear and tissue context. However, we lack a wealth of techniques capable of detecting DNA damage in situ. Here, we review the limited existing in situ DNA damage detection methods and examine their potential to offer spatial analysis of DNA adducts in tumors or other tissues. We also offer a perspective on the need for spatial analysis of DNA damage in situ and highlight Repair Assisted Damage Detection (RADD) as an in situ DNA adduct technique with the potential to integrate with spatial analysis and the challenges to be addressed.

Chromosome-Level Genome Assembly of the Blue Mussel Mytilus chilensis Reveals Molecular Signatures Facing the Marine Environment

The blue mussel Mytilus chilensis is an endemic and key socioeconomic species inhabiting the southern coast of Chile. This bivalve species supports a booming aquaculture industry, which entirely relies on artificially collected seeds from natural beds that are translocated to diverse physical-chemical ocean farming conditions. Furthermore, mussel production is threatened by a broad range of microorganisms, pollution, and environmental stressors that eventually impact its survival and growth. Herein, understanding the genomic basis of the local adaption is pivotal to developing sustainable shellfish aquaculture. We present a high-quality reference genome of M. chilensis, which is the first chromosome-level genome for a Mytilidae member in South America. The assembled genome size was 1.93 Gb, with a contig N50 of 134 Mb. Through Hi-C proximity ligation, 11,868 contigs were clustered, ordered, and assembled into 14 chromosomes in congruence with the karyological evidence. The M. chilensis genome comprises 34,530 genes and 4795 non-coding RNAs. A total of 57% of the genome contains repetitive sequences with predominancy of LTR-retrotransposons and unknown elements. Comparative genome analysis of M. chilensis and M. coruscus was conducted, revealing genic rearrangements distributed into the whole genome. Notably, transposable Steamer-like elements associated with horizontal transmissible cancer were explored in reference genomes, suggesting putative relationships at the chromosome level in Bivalvia. Genome expression analysis was also conducted, showing putative genomic differences between two ecologically different mussel populations. The evidence suggests that local genome adaptation and physiological plasticity can be analyzed to develop sustainable mussel production. The genome of M. chilensis provides pivotal molecular knowledge for the Mytilus complex.

Approaches for Modes of Action Study of Long Non-Coding RNAs: From Single Verification to Genome-Wide Determination

Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides (nt) that are not translated into known functional proteins. This broad definition covers a large collection of transcripts with diverse genomic origins, biogenesis, and modes of action. Thus, it is very important to choose appropriate research methodologies when investigating lncRNAs with biological significance. Multiple reviews to date have summarized the mechanisms of lncRNA biogenesis, their localization, their functions in gene regulation at multiple levels, and also their potential applications. However, little has been reviewed on the leading strategies for lncRNA research. Here, we generalize a basic and systemic mind map for lncRNA research and discuss the mechanisms and the application scenarios of ‘up-to-date’ techniques as applied to molecular function studies of lncRNAs. Taking advantage of documented lncRNA research paradigms as examples, we aim to provide an overview of the developing techniques for elucidating lncRNA interactions with genomic DNA, proteins, and other RNAs. In the end, we propose the future direction and potential technological challenges of lncRNA studies, focusing on techniques and applications.

Interaction-integrated linear mixed model reveals 3D-genetic basis underlying Autism

Genetic interactions play critical roles in genotype-phenotype associations. We developed a novel interaction-integrated linear mixed model (ILMM) that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems of searching for combinations. To demonstrate its utility, with 3D genomic interactions (assessed by Hi-C experiments) as a priori, we applied ILMM to whole-genome sequencing data for Autism Spectrum Disorders (ASD) and brain transcriptome data, revealing the 3D-genetic basis of ASD and 3D-expression quantitative loci (3D-eQTLs) for brain tissues. Notably, we reported a potential mechanism involving distal regulation between FOXP2 and DNMT3A, conferring the risk of ASD.

Examining the biological mechanisms of human mental disorders resulting from gene-environment interdependence using novel functional genomic approaches

We explore how functional genomics approaches that integrate datasets from human and non-human model systems can improve our understanding of the effect of gene-environment interplay on the risk for mental disorders. We start by briefly defining the G-E paradigm and its challenges and then discuss the different levels of regulation of gene expression and the corresponding data existing in humans (genome wide genotyping, transcriptomics, DNA methylation, chromatin modifications, chromosome conformational changes, non-coding RNAs, proteomics and metabolomics), discussing novel approaches to the application of these data in the study of the origins of mental health. Finally, we discuss the multilevel integration of diverse types of data. Advance in the use of functional genomics in the context of a G-E perspective improves the detection of vulnerabilities, informing the development of preventive and therapeutic interventions.

Computational Methods for Single-cell DNA Methylome Analysis

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

Challenges and opportunities of strain diversity in gut microbiome research

Just because two things are related does not mean they are the same. In analyzing microbiome data, we are often limited to species-level analyses, and even with the ability to resolve strains, we lack comprehensive databases and understanding of the importance of strain-level variation outside of a limited number of model organisms. The bacterial genome is highly plastic with gene gain and loss occurring at rates comparable or higher than de novo mutations. As such, the conserved portion of the genome is often a fraction of the pangenome which gives rise to significant phenotypic variation, particularly in traits which are important in host microbe interactions. In this review, we discuss the mechanisms that give rise to strain variation and methods that can be used to study it. We identify that while strain diversity can act as a major barrier in interpreting and generalizing microbiome data, it can also be a powerful tool for mechanistic research. We then highlight recent examples demonstrating the importance of strain variation in colonization, virulence, and xenobiotic metabolism. Moving past taxonomy and the species concept will be crucial for future mechanistic research to understand microbiome structure and function.

Pattern recognition of topologically associating domains using deep learning

BACKGROUND

Recent increasing evidence indicates that three-dimensional chromosome structure plays an important role in genomic function. Topologically associating domains (TADs) are self-interacting regions that have been shown to be a chromosomal structural unit. During evolution, these are conserved based on checking synteny block cross species. Are there common TAD patterns across species or cell lines?

RESULTS

To address the above question, we propose a novel task-TAD recognition-as opposed to traditional TAD identification. Specifically, we treat Hi-C maps as images, thus re-casting TAD recognition as image pattern recognition, for which we use a convolutional neural network and a residual neural network. In addition, we propose an elegant way to generate non-TAD data for binary classification. We demonstrate deep learning performance which is quite promising, AUC > 0.80, through cross-species and cell-type validation.

CONCLUSIONS

TADs have been shown to be conserved during evolution. Interestingly, our results confirm that the TAD recognition model is practical across species, which indicates that TADs between human and mouse show common patterns from an image classification point of view. Our approach could be a new way to identify TAD variations or patterns among Hi-C maps. For example, TADs of two Hi-C maps are conserved if the two classification models are exchangeable.

ParticleChromo3D: a Particle Swarm Optimization algorithm for chromosome 3D structure prediction from Hi-C data

Background

The three-dimensional (3D) structure of chromatin has a massive effect on its function. Because of this, it is desirable to have an understanding of the 3D structural organization of chromatin. To gain greater insight into the spatial organization of chromosomes and genomes and the functions they perform, chromosome conformation capture (3C) techniques, particularly Hi-C, have been developed. The Hi-C technology is widely used and well-known because of its ability to profile interactions for all read pairs in an entire genome. The advent of Hi-C has greatly expanded our understanding of the 3D genome, genome folding, gene regulation and has enabled the development of many 3D chromosome structure reconstruction methods.

Results

Here, we propose a novel approach for 3D chromosome and genome structure reconstruction from Hi-C data using Particle Swarm Optimization (PSO) approach called ParticleChromo3D. This algorithm begins with a grouping of candidate solution locations for each chromosome bin, according to the particle swarm algorithm, and then iterates its position towards a global best candidate solution. While moving towards the optimal global solution, each candidate solution or particle uses its own local best information and a randomizer to choose its path. Using several metrics to validate our results, we show that ParticleChromo3D produces a robust and rigorous representation of the 3D structure for input Hi-C data. We evaluated our algorithm on simulated and real Hi-C data in this work. Our results show that ParticleChromo3D is more accurate than most of the existing algorithms for 3D structure reconstruction.

Conclusions

Our results also show that constructed ParticleChromo3D structures are very consistent, hence indicating that it will always arrive at the global solution at every iteration. The source code for ParticleChromo3D, the simulated and real Hi-C datasets, and the models generated for these datasets are available here: https://github.com/OluwadareLab/ParticleChromo3D

"Omics" data unveil early molecular response underlying limb regeneration in the Chinese mitten crab, Eriocheir sinensis

Limb regeneration is a fascinating and medically interesting trait that has been well preserved in arthropod lineages, particularly in crustaceans. However, the molecular mechanisms underlying arthropod limb regeneration remain largely elusive. The Chinese mitten crab Eriocheir sinensis shows strong regenerative capacity, a trait that has likely allowed it to become a worldwide invasive species. Here, we report a chromosome-level genome of E. sinensis as well as large-scale transcriptome data during the limb regeneration process. Our results reveal that arthropod-specific genes involved in signal transduction, immune response, histone methylation, and cuticle development all play fundamental roles during the regeneration process. Particularly, Innexin2-mediated signal transduction likely facilitates the early stage of the regeneration process, while an effective crustacean-specific prophenoloxidase system (ProPo-AS) plays crucial roles in the initial immune response. Collectively, our findings uncover novel genetic pathways pertaining to arthropod limb regeneration and provide valuable resources for studies on regeneration from a comparative perspective.

Genome architecture plasticity underlies DNA replication timing dynamics in cell differentiation

During the S-phase of eukaryotic cell cycle, DNA is replicated in a dedicatedly regulated temporal order, with regions containing active and inactive genes replicated early and late, respectively. Recent advances in sequencing technology allow us to explore the connection between replication timing (RT), histone modifications, and three-dimensional (3D) chromatin structure in diverse cell types. To characterize the dynamics during cell differentiation, corresponding sequencing data for human embryonic stem cells and four differentiated cell types were collected. By comparing RT and its extent of conservation before and after germ layer specification, the human genome was partitioned into distinct categories. Each category is then subject to comparisons on genomic, epigenetic, and chromatin 3D structural features. As expected, while constitutive early and late replication regions showed active and inactive features, respectively, dynamic regions with switched RT showed intermediate features. Surprisingly, although early-to-late replication and late-to-early replication regions showed similar histone modification patterns in hESCs, their structural preferences were opposite. Specifically, in hESCs, early-to-late replication regions tended to appear in the B compartment and large topologically associated domains, while late-to-early replication regions showed the opposite. Our results uncover the coordinated regulation of RT and 3D genome structure that underlies the loss of pluripotency and lineage commitment and indicate the importance and potential roles of genome architecture in biological processes.

Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants

Genome-wide association studies (GWAS) have identified a vast number of variants associated with various complex human diseases and traits. However, most of these GWAS variants reside in non-coding regions producing no proteins, making the interpretation of these variants a daunting challenge. Prior evidence indicates that a subset of non-coding variants detected within or near cis-regulatory elements (e.g., promoters, enhancers, silencers, and insulators) might play a key role in disease etiology by regulating gene expression. Advanced sequencing- and imaging-based technologies, together with powerful computational methods, enabling comprehensive characterization of regulatory DNA interactions, have substantially improved our understanding of the three-dimensional (3D) genome architecture. Recent literature witnesses plenty of examples where using chromosome conformation capture (3C)-based technologies successfully links non-coding variants to their target genes and prioritizes relevant tissues or cell types. These examples illustrate the critical capability of 3D genome organization in annotating non-coding GWAS variants. This review discusses how 3D genome organization information contributes to elucidating the potential roles of non-coding GWAS variants in disease etiology.

Predicting 3D chromatin interactions from DNA sequence using Deep Learning

Gene regulation in eukaryotes is profoundly shaped by the 3D organization of chromatin within the cell nucleus. Distal regulatory interactions between enhancers and their target genes are widespread and many causal loci underlying heritable agricultural or clinical traits have been mapped to distal cis-regulatory elements. Dissecting the sequence features that mediate such distal interactions is key to understanding their underlying biology. Deep Learning (DL) models coupled with genome-wide 3C-based sequencing data have emerged as powerful tools to infer the DNA sequence grammar underlying such distal interactions. In this review we show that most DL models have remarkably high prediction accuracy, which indicates that DNA sequence features are important determinants of chromatin looping. However, DL model training has so far been limited to a small set of human cell lines, raising questions about the generalization of these predictions to other tissue-types and species. Furthermore, we find that the model architecture seems less relevant for model performance than the training strategy and the data preparation step. Transfer learning, coupled with functionally curated interactions, appear to be the most promising approach to learn cell-type specific and possibly species- specific sequence features in future applications.

Computational Approaches for Understanding Sequence Variation Effects on the 3D Genome Architecture

Decoding how genomic sequence and its variations affect 3D genome architecture is indispensable for understanding the genetic architecture of various traits and diseases. The 3D genome organization can be significantly altered by genome variations and in turn impact the function of the genomic sequence. Techniques for measuring the 3D genome architecture across spatial scales have opened up new possibilities for understanding how the 3D genome depends upon the genomic sequence and how it can be altered by sequence variations. Computational methods have become instrumental in analyzing and modeling the sequence effects on 3D genome architecture, and recent development in deep learning sequence models have opened up new opportunities for studying the interplay between sequence variations and the 3D genome. In this review, we focus on computational approaches for both the detection and modeling of sequence variation effects on the 3D genome, and we discuss the opportunities presented by these approaches. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Lymphocyte activation gene-3-associated protein networks are associated with HDL-cholesterol and mortality in the Trans-omics for Precision Medicine program

Deficiency of the immune checkpoint lymphocyte activation gene-3 (LAG3) protein is significantly associated with both elevated HDL-cholesterol (HDL-C) and myocardial infarction risk. We determined the association of genetic variants within ±500 kb of LAG3 with plasma LAG3 and defined LAG3-associated plasma proteins with HDL-C and clinical outcomes. Whole genome sequencing and plasma proteomics were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA) and the Framingham Heart Study (FHS) cohorts as part of the Trans-Omics for Precision Medicine program. In situ Hi-C chromatin capture was performed in EBV-transformed cell lines isolated from four MESA participants. Genetic association analyses were performed in MESA using multivariate regression models, with validation in FHS. A LAG3-associated protein network was tested for association with HDL-C, coronary heart disease, and all-cause mortality. We identify an association between the LAG3 rs3782735 variant and plasma LAG3 protein. Proteomics analysis reveals 183 proteins significantly associated with LAG3 with four proteins associated with HDL-C. Four proteins discovered for association with all-cause mortality in FHS shows nominal associations in MESA. Chromatin capture analysis reveals significant cis interactions between LAG3 and C1S, LRIG3, TNFRSF1A, and trans interactions between LAG3 and B2M. A LAG3-associated protein network has significant associations with HDL-C and mortality.

Rodriguez et al. use whole genome sequencing and plasma proteomics from the Multi-Ethnic Study of Atherosclerosis (MESA) and the Framingham Heart Study (FHS) cohorts of the Trans-Omics for Precision Medicine program and perform in situ Hi-C chromatin capture in cell lines isolated from four MESA participants. They demonstrate that lymphocyte activation gene-3 protein networks are associated with HDL-cholesterol and mortality, which could guide the development of precision medicine.

Comparison of Capture Hi-C Analytical Pipelines

It is now evident that DNA forms an organized nuclear architecture, which is essential to maintain the structural and functional integrity of the genome. Chromatin organization can be systematically studied due to the recent boom in chromosome conformation capture technologies (e.g., 3C and its successors 4C, 5C and Hi-C), which is accompanied by the development of computational pipelines to identify biologically meaningful chromatin contacts in such data. However, not all tools are applicable to all experimental designs and all structural features. Capture Hi-C (CHi-C) is a method that uses an intermediate hybridization step to target and select predefined regions of interest in a Hi-C library, thereby increasing effective sequencing depth for those regions. It allows researchers to investigate fine chromatin structures at high resolution, for instance promoter-enhancer loops, but it introduces additional biases with the capture step, and therefore requires specialized pipelines. Here, we compare multiple analytical pipelines for CHi-C data analysis. We consider the effect of retaining multi-mapping reads and compare the efficiency of different statistical approaches in both identifying reproducible interactions and determining biologically significant interactions. At restriction fragment level resolution, the number of multi-mapping reads that could be rescued was negligible. The number of identified interactions varied widely, depending on the analytical method, indicating large differences in type I and type II error rates. The optimal pipeline depends on the project-specific tolerance level of false positive and false negative chromatin contacts.

Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity

Parkinson's disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.

3D genome organization links non-coding disease-associated variants to genes

Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.

Discrete regulatory modules instruct hematopoietic lineage commitment and differentiation

Lineage commitment and differentiation is driven by the concerted action of master transcriptional regulators at their target chromatin sites. Multiple efforts have characterized the key transcription factors (TFs) that determine the various hematopoietic lineages. However, the temporal interactions between individual TFs and their chromatin targets during differentiation and how these interactions dictate lineage commitment remains poorly understood. Here we perform dense, daily, temporal profiling of chromatin accessibility (DNase I-seq) and gene expression changes (total RNA-seq) along ex vivo human erythropoiesis to comprehensively define developmentally regulated DNase I hypersensitive sites (DHSs) and transcripts. We link both distal DHSs to their target gene promoters and individual TFs to their target DHSs, revealing that the regulatory landscape is organized in distinct sequential regulatory modules that regulate lineage restriction and maturation. Finally, direct comparison of transcriptional dynamics (bulk and single-cell) and lineage potential between erythropoiesis and megakaryopoiesis uncovers differential fate commitment dynamics between the two lineages as they exit the stem and progenitor stage. Collectively, these data provide insights into the temporally regulated synergy of the cis- and the trans-regulatory components underlying hematopoietic lineage commitment and differentiation.

FISH Going Meso-Scale: A Microscopic Search for Chromatin Domains

The intimate relationships between genome structure and function direct efforts toward deciphering three-dimensional chromatin organization within the interphase nuclei at different genomic length scales. For decades, major insights into chromatin structure at the level of large-scale euchromatin and heterochromatin compartments, chromosome territories, and subchromosomal regions resulted from the evolution of light microscopy and fluorescence in situ hybridization. Studies of nanoscale nucleosomal chromatin organization benefited from a variety of electron microscopy techniques. Recent breakthroughs in the investigation of mesoscale chromatin structures have emerged from chromatin conformation capture methods (C-methods). Chromatin has been found to form hierarchical domains with high frequency of local interactions from loop domains to topologically associating domains and compartments. During the last decade, advances in super-resolution light microscopy made these levels of chromatin folding amenable for microscopic examination. Here we are reviewing recent developments in FISH-based approaches for detection, quantitative measurements, and validation of contact chromatin domains deduced from C-based data. We specifically focus on the design and application of Oligopaint probes, which marked the latest progress in the imaging of chromatin domains. Vivid examples of chromatin domain FISH-visualization by means of conventional, super-resolution light and electron microscopy in different model organisms are provided.

Leveraging three-dimensional chromatin architecture for effective reconstruction of enhancer-target gene regulatory interactions

A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer-target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the hierarchy of structural domains which define chromatin three-dimensional (3D) architecture. Here we introduce a change of perspective based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work (i) we develop a computational and statistical framework to reconstruct a comprehensive map of ETG pairs leveraging functional genomics data; (ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy and (iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research.

Assigning Co-Regulated Human Genes and Regulatory Gene Clusters

Elucidating the role of genetic variation in the regulation of gene expression is key to understanding the pathobiology of complex diseases which, in consequence, is crucial in devising targeted treatment options. Expression quantitative trait locus (eQTL) analysis correlates a genetic variant with the strength of gene expression, thus defining thousands of regulated genes in a multitude of human cell types and tissues. Some eQTL may not act independently of each other but instead may be regulated in a coordinated fashion by seemingly independent genetic variants. To address this issue, we combined the approaches of eQTL analysis and colocalization studies. Gene expression was determined in datasets comprising 49 tissues from the Genotype-Tissue Expression (GTEx) project. From about 33,000 regulated genes, over 14,000 were found to be co-regulated in pairs and were assembled across all tissues to almost 15,000 unique clusters containing up to nine regulated genes affected by the same eQTL signal. The distance of co-regulated eGenes was, on average, 112 kilobase pairs. Of 713 genes known to express clinical symptoms upon haploinsufficiency, 231 (32.4%) are part of at least one of the identified clusters. This calls for caution should treatment approaches aim at an upregulation of a haploinsufficient gene. In conclusion, we present an unbiased approach to identifying co-regulated genes in and across multiple tissues. Knowledge of such common effects is crucial to appreciate implications on biological pathways involved, specifically when a treatment option targets a co-regulated disease gene.

Opportunity to improve livestock traits using 3D genomics

The advent of high-throughput chromosome conformation capture and sequencing (Hi-C) has enabled researchers to probe the 3D architecture of the mammalian genome in a genome-wide manner. Simultaneously, advances in epigenomic assays, such as chromatin immunoprecipitation and sequencing (ChIP-seq) and DNase-seq, have enabled researchers to study cis-regulatory interactions and chromatin accessibility across the same genome-wide scale. The use of these data has revealed many unique insights into gene regulation and disease pathomechanisms in several model organisms. With the advent of these high-throughput sequencing technologies, there has been an ever-increasing number of datasets available for study; however, this is often limited to model organisms. Livestock species play critical roles in the economies of developing and developed nations alike. Despite this, they are greatly underrepresented in the 3D genomics space; Hi-C and related technologies have the potential to revolutionise livestock breeding by enabling a more comprehensive understanding of how production traits are controlled. The growth in human and model organism Hi-C data has seen a surge in the availability of computational tools for use in 3D genomics, with some tools using machine learning techniques to predict features and improve dataset quality. In this review, we provide an overview of the 3D genome and discuss the status of 3D genomics in livestock before delving into advancing the field by drawing inspiration from research in human and mouse. We end by offering future directions for livestock research in the field of 3D genomics.

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

Understanding 3D genome organization by multidisciplinary methods

Understanding how chromatin is folded in the nucleus is fundamental to understanding its function. Although 3D genome organization has been historically difficult to study owing to a lack of relevant methodologies, major technological breakthroughs in genome-wide mapping of chromatin contacts and advances in imaging technologies in the twenty-first century considerably improved our understanding of chromosome conformation and nuclear architecture. In this Review, we discuss methods of 3D genome organization analysis, including sequencing-based techniques, such as Hi-C and its derivatives, Micro-C, DamID and others; microscopy-based techniques, such as super-resolution imaging coupled with fluorescence in situ hybridization (FISH), multiplex FISH, in situ genome sequencing and live microscopy methods; and computational and modelling approaches. We describe the most commonly used techniques and their contribution to our current knowledge of nuclear architecture and, finally, we provide a perspective on up-and-coming methods that open possibilities for future major discoveries.

Alignments of biomolecular contact maps

Alignments of discrete objects can be constructed in a very general setting as super-objects from which the constituent objects are recovered by means of projections. Here, we focus on contact maps, i.e. undirected graphs with an ordered set of vertices. These serve as natural discretizations of RNA and protein structures. In the general case, the alignment problem for vertex-ordered graphs is NP-complete. In the special case of RNA secondary structures, i.e. crossing-free matchings, however, the alignments have a recursive structure. The alignment problem then can be solved by a variant of the Sankoff algorithm in polynomial time. Moreover, the tree or forest alignments of RNA secondary structure can be understood as the alignments of ordered edge sets.

HCMB: A stable and efficient algorithm for processing the normalization of highly sparse Hi-C contact data

The high-throughput genome-wide chromosome conformation capture (Hi-C) method has recently become an important tool to study chromosomal interactions where one can extract meaningful biological information including P(s) curve, topologically associated domains, A/B compartments, and other biologically relevant signals. Normalization is a critical pre-processing step of downstream analyses for the elimination of systematic and technical biases from chromatin contact matrices due to different mappability, GC content, and restriction fragment lengths. Especially, the problem of high sparsity puts forward a huge challenge on the correction, indicating the urgent need for a stable and efficient method for Hi-C data normalization. Recently, some matrix balancing methods have been developed to normalize Hi-C data, such as the Knight-Ruiz (KR) algorithm, but it failed to normalize contact matrices with high sparsity. Here, we presented an algorithm, Hi-C Matrix Balancing (HCMB), based on an iterative solution of equations, combining with linear search and projection strategy to normalize the Hi-C original interaction data. Both the simulated and experimental data demonstrated that HCMB is robust and efficient in normalizing Hi-C data and preserving the biologically relevant Hi-C features even facing very high sparsity. HCMB is implemented in Python and is freely accessible to non-commercial users at GitHub: https://github.com/HUST-DataMan/HCMB.

Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines

BACKGROUND

Sequencing of patient-derived xenograft (PDX) mouse models allows investigation of the molecular mechanisms of human tumor samples engrafted in a mouse host. Thus, both human and mouse genetic material is sequenced. Several methods have been developed to remove mouse sequencing reads from RNA-seq or exome sequencing PDX data and improve the downstream signal. However, for more recent chromatin conformation capture technologies (Hi-C), the effect of mouse reads remains undefined.

RESULTS

We evaluated the effect of mouse read removal on the quality of Hi-C data using in silico created PDX Hi-C data with 10% and 30% mouse reads. Additionally, we generated 2 experimental PDX Hi-C datasets using different library preparation strategies. We evaluated 3 alignment strategies (Direct, Xenome, Combined) and 3 pipelines (Juicer, HiC-Pro, HiCExplorer) on Hi-C data quality.

CONCLUSIONS

Removal of mouse reads had little-to-no effect on data quality as compared with the results obtained with the Direct alignment strategy. Juicer extracted more valid chromatin interactions for Hi-C matrices, regardless of the mouse read removal strategy. However, the pipeline effect was minimal, while the library preparation strategy had the largest effect on all quality metrics. Together, our study presents comprehensive guidelines on PDX Hi-C data processing.

Learning the distribution of single-cell chromosome conformations in bacteria reveals emergent order across genomic scales

The order and variability of bacterial chromosome organization, contained within the distribution of chromosome conformations, are unclear. Here, we develop a fully data-driven maximum entropy approach to extract single-cell 3D chromosome conformations from Hi-C experiments on the model organism Caulobacter crescentus. The predictive power of our model is validated by independent experiments. We find that on large genomic scales, organizational features are predominantly present along the long cell axis: chromosomal loci exhibit striking long-ranged two-point axial correlations, indicating emergent order. This organization is associated with large genomic clusters we term Super Domains (SuDs), whose existence we support with super-resolution microscopy. On smaller genomic scales, our model reveals chromosome extensions that correlate with transcriptional and loop extrusion activity. Finally, we quantify the information contained in chromosome organization that may guide cellular processes. Our approach can be extended to other species, providing a general strategy to resolve variability in single-cell chromosomal organization.

High-continuity genome assembly of the jellyfish Chrysaora quinquecirrha

The Atlantic sea nettle (Chrysaora quinquecirrha) has an important evolutionary position due to its high ecological value. However, due to limited sequencing technologies and complex jellyfish genomic sequences, the current C. quinquecirrha genome assembly is highly fragmented. Here, we used the most advanced high-throughput chromosome conformation capture (Hi-C) technology to obtain high-coverage sequencing data of the C. quinquecirrha genome. We then anchored these data to the previously published contig-level assembly to improve the genome. Finally, a high-continuity genome sequence of C. quinquecirrha was successfully assembled, which contained 1 882 scaffolds with a N50 length of 3.83 Mb. The N50 length of the genome assembly was 5.23 times longer than the previously released one, and additional analysis revealed that it had a high degree of genomic continuity and accuracy. Acquisition of the high-continuity genome sequence of C. quinquecirrha not only provides a basis for the study of jellyfish evolution through comparative genomics but also provides an important resource for studies on jellyfish growth and development.

Chromosome-scale genome assembly of the sea louse Caligus rogercresseyi by SMRT sequencing and Hi-C analysis

Caligus rogercresseyi, commonly known as sea louse, is an ectoparasite copepod that impacts the salmon aquaculture in Chile, causing losses of hundreds of million dollars per year. In this study, we report a chromosome-scale assembly of the sea louse (C. rogercresseyi) genome based on single-molecule real-time sequencing (SMRT) and proximity ligation (Hi-C) analysis. Coding RNAs and non-coding RNAs, and specifically long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) were identified through whole transcriptome sequencing from different life stages. A total of 23,686 protein-coding genes and 12,558 non-coding RNAs were annotated. In addition, 6,308 lncRNAs and 5,774 miRNAs were found to be transcriptionally active from larvae to adult stages. Taken together, this genomic resource for C. rogercresseyi represents a valuable tool to develop sustainable control strategies in the salmon aquaculture industry.

FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data

Chromosome conformation capture data, particularly from high-throughput approaches such as Hi-C, are typically very complex to analyse. Existing analysis tools are often single-purpose, or limited in compatibility to a small number of data formats, frequently making Hi-C analyses tedious and time-consuming. Here, we present FAN-C, an easy-to-use command-line tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data ( https://github.com/vaquerizaslab/fanc ). Due to its compatibility with the most prevalent Hi-C storage formats, FAN-C can be used in combination with a large number of existing analysis tools, thus greatly simplifying Hi-C matrix analysis.

Benchmark of software tools for prokaryotic chromosomal interaction domain identification

Motivation

The application of genome-wide chromosome conformation capture (3C) methods to prokaryotes provided insights into the spatial organization of their genomes and identified patterns conserved across the tree of life, such as chromatin compartments and contact domains. Prokaryotic genomes vary in GC content and the density of restriction sites along the chromosome, suggesting that these properties should be considered when planning experiments and choosing appropriate software for data processing. Diverse algorithms are available for the analysis of eukaryotic chromatin contact maps, but their potential application to prokaryotic data has not yet been evaluated.

Results

Here, we present a comparative analysis of domain calling algorithms using available single-microbe experimental data. We evaluated the algorithms’ intra-dataset reproducibility, concordance with other tools and sensitivity to coverage and resolution of contact maps. Using RNA-seq as an example, we showed how orthogonal biological data can be utilized to validate the reliability and significance of annotated domains. We also suggest that in silico simulations of contact maps can be used to choose optimal restriction enzymes and estimate theoretical map resolutions before the experiment. Our results provide guidelines for researchers investigating microbes and microbial communities using high-throughput 3C assays such as Hi-C and 3C-seq.

Availability and implementation

The code of the analysis is available at https://github.com/magnitov/prokaryotic_cids.

Supplementary information

Supplementary data are available at Bioinformatics online.

Dynamic Chromatin Structure and Epigenetics Control the Fate of Malaria Parasites

Multiple hosts and various life cycle stages prompt the human malaria parasite, Plasmodium falciparum, to acquire sophisticated molecular mechanisms to ensure its survival, spread, and transmission to its next host. To face these environmental challenges, increasing evidence suggests that the parasite has developed complex and complementary layers of regulatory mechanisms controlling gene expression. Here, we discuss the recent developments in the discovery of molecular components that contribute to cell replication and differentiation and highlight the major contributions of epigenetics, transcription factors, and nuclear architecture in controlling gene regulation and life cycle progression in Plasmodium spp.

Broadening our understanding of the genetics of Juvenile Idiopathic Arthritis (JIA): Interrogation of three dimensional chromatin structures and genetic regulatory elements within JIA-associated risk loci

Objective

The risk loci for juvenile idiopathic arthritis (JIA) consist of extended haplotypes that include functional elements in addition to canonical coding genes. As with most autoimmune diseases, the risk haplotypes for JIA are highly enriched for H3K4me1/H3K27ac histone marks, epigenetic signatures that typically identify poised or active enhancers. In this study, we test the hypothesis that genetic risk for JIA is exerted through altered enhancer-mediated gene regulation.

Methods

We mined publically available HiC and other chromatin conformation data to determine whether H3K27ac-marked regions in 25 JIA risk loci showed physical evidence of contact with gene promoters. We also used in vitro reporter assays to establish as proof-of-concept the idea that genetic variants in linkage disequilibrium with GWAS-identified tag SNPs alter enhancer function.

Results

All 25 loci examined showed multiple contact sites in the 4 different cell lines that we queried. These regions were characterized by HiC-defined loop structures that included 237 immune-related genes. Using in vitro assays, we found that a 657 bp, H3K4me1/H3K27-marked region within the first intron of IL2RA shows enhancer activity in reporter assays, and this activity is attenuated by SNPs on the IL2RA haplotype that we identified using whole genome sequencing of children with JIA. Similarly, we identified a 1,669 bp sequence in an intergenic region of the IL6R locus where SNPs identified in children with JIA increase enhancer function in reporter assays.

Conclusions

These studies provide evidence that altered enhancer function contributes to genetic risk in JIA. Further studies to identify the specific target genes of genetically altered enhancers are warranted.

Mixed secondary chromatin structure revealed by modeling radiation-induced DNA fragment length distribution

Spatial chromatin structure plays fundamental roles in many vital biological processes including DNA replication, transcription, damage and repair. However, the current understanding of the secondary structure of chromatin formed by local nucleosome-nucleosome interactions remains controversial, especially for the existence and conformation of 30 nm structure. Since chromatin structure influences the fragment length distribution (FLD) of ionizing radiation-induced DNA strand breaks, a 3D chromatin model fitting FLD patterns can help to distinguish different models of chromatin structure. Here, we developed a novel "30-C" model combining 30 nm chromatin structure models with Hi-C data, which measured the spatial contact frequency between different loci in the genome. We first reconstructed the 3D coordinates of the 25 kb bins from Hi-C heatmaps. Within the 25 kb bins, lower level chromatin structures supported by recent studies were filled. Simulated FLD patterns based on the 30-C model were compared to published FLD patterns induced by heavy ion radiation to validate the models. Importantly, the 30-C model predicted that the most probable chromatin fiber structure for human interphase fibroblasts in vivo was 45% zig-zag 30 nm fibers and 55% 10 nm fibers.

Using contact statistics to characterize structure transformation of biopolymer ensembles

As a unique subset of functional polymers, many biopolymers have a set of well-defined three-dimensional (3D) structural characteristics that can be described by spatial contacts between monomers. Statistical analysis of the contacts has been extremely productive in characterizing the biopolymer structural ensemble, such as for 3D chromosome structures. Often, native contacts and compartment structures are the focus of the studies, while the generic polymer aspect, such as the overall decaying of contacts with increasing sequence distance, is analyzed separately or preemptively removed. Here, we explore insights that can be gained by performing "compartment analysis" that keeps the distance decay, which we believe is particularly useful for characterizing the structure transformation of biopolymers. We tested contact analysis on several such transformations under physical perturbation or biological processes, including (1) unfolding of proteins induced by thermal denaturation, (2) chromosome conformation transition during the cell cycle, and (3) chromosome unpacking by physicochemical perturbations. Useful score functions were developed to further quantitatively characterize the transformation judging from the contact analysis. We also find that the sinusoidal undertone of eigenvector patterns (the "unwanted," low frequency signal, in contrast to the detailed A/B compartment) that had previously been attributed to biological effects of centromere proximal and distal interactions may in fact reflect a universal feature of polymers that have relatively weaker long-range contacts.

HiCBricks: building blocks for efficient handling of large Hi-C datasets

SUMMARY

Genome-wide chromosome conformation capture based on high-throughput sequencing (Hi-C) has been widely adopted to study chromatin architecture by generating datasets of ever-increasing complexity and size. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates functions for calling domain boundaries and functions for high quality data visualization.

AVAILABILITY

http://bioconductor.org/packages/devel/bioc/html/HiCBricks.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

HiCeekR: A Novel Shiny App for Hi-C Data Analysis

The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset.

Visualising three-dimensional genome organisation in two dimensions

The three-dimensional organisation of the genome plays a crucial role in developmental gene regulation. In recent years, techniques to investigate this organisation have become more accessible to labs worldwide due to improvements in protocols and decreases in the cost of high-throughput sequencing. However, the resulting datasets are complex and can be challenging to analyse and interpret. Here, we provide a guide to visualisation approaches that can aid the interpretation of such datasets and the communication of biological results.

3D Chromosomal Landscapes in Hematopoiesis and Immunity

Epigenetic dysregulation plays a profound role in the pathogenesis of hematological malignancies, which is often the result of somatic mutations of chromatin regulators. Previously, these mutations were largely considered to alter gene expression in two dimensions, by activating or repressing chromatin states; however, research in the last decade has highlighted the increasing impact of the 3D organization of the genome in gene regulation and disease pathogenesis. Here, we summarize the current principles of 3D chromatin organization, how the integrity of the 3D genome governs immune cell development and malignant transformation, as well as how underlying (epi-)genetic drivers of 3D chromatin alterations might act as potential novel therapeutic targets for hematological malignancies.