Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution

Pairtools: From sequencing data to chromosome contacts

The field of 3D genome organization produces large amounts of sequencing data from Hi-C and a rapidly-expanding set of other chromosome conformation protocols (3C+). Massive and heterogeneous 3C+ data require high-performance and flexible processing of sequenced reads into contact pairs. To meet these challenges, we present pairtools-a flexible suite of tools for contact extraction from sequencing data. Pairtools provides modular command-line interface (CLI) tools that can be flexibly chained into data processing pipelines. The core operations provided by pairtools are parsing of.sam alignments into Hi-C pairs, sorting and removal of PCR duplicates. In addition, pairtools provides auxiliary tools for building feature-rich 3C+ pipelines, including contact pair manipulation, filtration, and quality control. Benchmarking pairtools against popular 3C+ data pipelines shows advantages of pairtools for high-performance and flexible 3C+ analysis. Finally, pairtools provides protocol-specific tools for restriction-based protocols, haplotype-resolved contacts, and single-cell Hi-C. The combination of CLI tools and tight integration with Python data analysis libraries makes pairtools a versatile foundation for a broad range of 3C+ pipelines.

Molecular and computational approaches to map regulatory elements in 3D chromatin structure

Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects.

Serpentine: a flexible 2D binning method for differential Hi-C analysis

Motivation

Hi-C contact maps reflect the relative contact frequencies between pairs of genomic loci, quantified through deep sequencing. Differential analyses of these maps enable downstream biological interpretations. However, the multi-fractal nature of the chromatin polymer inside the cellular envelope results in contact frequency values spanning several orders of magnitude: contacts between loci pairs separated by large genomic distances are much sparser than closer pairs. The same is true for poorly covered regions, such as repeated sequences. Both distant and poorly covered regions translate into low signal-to-noise ratios. There is no clear consensus to address this limitation.

Results

We present Serpentine, a fast, flexible procedure operating on raw data, which considers the contacts in each region of a contact map. Binning is performed only when necessary on noisy regions, preserving informative ones. This results in high-quality, low-noise contact maps that can be conveniently visualized for rigorous comparative analyses.

Availability and implementation

Serpentine is available on the PyPI repository and https://github.com/koszullab/serpentine; documentation and tutorials are provided at https://serpentine.readthedocs.io/en/latest/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Toward understanding the dynamic state of 3D genome

The three-dimensional (3D) genome organization and its role in biological activities have been investigated for over a decade in the field of cell biology. Recent studies using live-imaging and polymer simulation have suggested that the higher-order chromatin structures are dynamic; the stochastic fluctuations of nucleosomes and genomic loci cannot be captured by bulk-based chromosome conformation capture techniques (Hi-C). In this review, we focus on the physical nature of the 3D genome architecture. We first describe how to decode bulk Hi-C data with polymer modeling. We then introduce our recently developed PHi-C method, a computational tool for modeling the fluctuations of the 3D genome organization in the presence of stochastic thermal noise. We also present another new method that analyzes the dynamic rheology property (represented as microrheology spectra) as a measure of the flexibility and rigidity of genomic regions over time. By applying these methods to real Hi-C data, we highlighted a temporal hierarchy embedded in the 3D genome organization; chromatin interaction boundaries are more rigid than the boundary interior, while functional domains emerge as dynamic fluctuations within a particular time interval. Our methods may bridge the gap between live-cell imaging and Hi-C data and elucidate the nature of the dynamic 3D genome organization.

Cumulative contact frequency of a chromatin region is an intrinsic property linked to its function

Regulation of gene transcription is a complex process controlled by many factors, including the conformation of chromatin in the nucleus. Insights into chromatin conformation on both local and global scales can be provided by the Hi-C (high-throughput chromosomes conformation capture) method. One of the drawbacks of Hi-C analysis and interpretation is the presence of systematic biases, such as different accessibility to enzymes, amplification, and mappability of DNA regions, which all result in different visibility of the regions. Iterative correction (IC) is one of the most popular techniques developed for the elimination of these systematic biases. IC is based on the assumption that all chromatin regions have an equal number of observed contacts in Hi-C. In other words, the IC procedure is equalizing the experimental visibility approximated by the cumulative contact frequency (CCF) for all genomic regions. However, the differences in experimental visibility might be explained by biological factors such as chromatin openness, which is characteristic of distinct chromatin states. Here we show that CCF is positively correlated with active transcription. It is associated with compartment organization, since compartment A demonstrates higher CCF and gene expression levels than compartment B. Notably, this observation holds for a wide range of species, including human, mouse, and Drosophila. Moreover, we track the CCF state for syntenic blocks between human and mouse and conclude that active state assessed by CCF is an intrinsic property of the DNA region, which is independent of local genomic and epigenomic context. Our findings establish a missing link between Hi-C normalization procedures removing CCF from the data and poorly investigated and possibly relevant biological factors contributing to CCF.

Technologies to study spatial genome organization: beyond 3C

The way that chromatin is organized in three-dimensional nuclear space is now acknowledged as a factor critical for the major cell processes, like transcription, replication and cell division. Researchers have been armed with new molecular and imaging technologies to study this structure-to-function link of genomes, spearheaded by the introduction of the 'chromosome conformation capture' technology more than a decade ago. However, this technology is not without shortcomings, and novel variants and orthogonal approaches are being developed to overcome these. As a result, the field of nuclear organization is constantly fueled by methods of increasing resolution and/or throughput that strive to eliminate systematic biases and increase precision. In this review, we attempt to highlight the most recent advances in technology that promise to provide novel insights on how chromosomes fold and function.

Chromatin architecture reorganization in murine somatic cell nuclear transfer embryos

The oocyte cytoplasm can reprogram the somatic cell nucleus into a totipotent state, but with low efficiency. The spatiotemporal chromatin organization of somatic cell nuclear transfer (SCNT) embryos remains elusive. Here, we examine higher order chromatin structures of mouse SCNT embryos using a low-input Hi-C method. We find that donor cell chromatin transforms to the metaphase state rapidly after SCNT along with the dissolution of typical 3D chromatin structure. Intriguingly, the genome undergoes a mitotic metaphase-like to meiosis metaphase II-like transition following activation. Subsequently, weak chromatin compartments and topologically associating domains (TADs) emerge following metaphase exit. TADs are further removed until the 2-cell stage before being progressively reestablished. Obvious defects including stronger TAD boundaries, aberrant super-enhancer and promoter interactions are found in SCNT embryos. These defects are partially caused by inherited H3K9me3, and can be rescued by Kdm4d overexpression. These observations provide insight into chromatin architecture reorganization during SCNT embryo development.

The organisation of chromatin in somatic cell nuclear transfer (SCNT) embryos remains poorly understood. Here, the authors examine higher order chromatin structures of mouse SCNT embryos and provide insights into chromatin architecture reorganisation during SCNT embryo development.

HIPPIE2: a method for fine-scale identification of physically interacting chromatin regions

Most regulatory chromatin interactions are mediated by various transcription factors (TFs) and involve physically interacting elements such as enhancers, insulators or promoters. To map these elements and interactions at a fine scale, we developed HIPPIE2 that analyzes raw reads from high-throughput chromosome conformation (Hi-C) experiments to identify precise loci of DNA physically interacting regions (PIRs). Unlike standard genome binning approaches (e.g. 10-kb to 1-Mb bins), HIPPIE2 dynamically infers the physical locations of PIRs using the distribution of restriction sites to increase analysis precision and resolution. We applied HIPPIE2 to in situ Hi-C datasets across six human cell lines (GM12878, IMR90, K562, HMEC, HUVEC, NHEK) with matched ENCODE/Roadmap functional genomic data. HIPPIE2 detected 1042 738 distinct PIRs, with high resolution (average PIR length of 1006 bp) and high reproducibility (92.3% in GM12878). PIRs are enriched for epigenetic marks (H3K27ac, H3K4me1) and open chromatin, suggesting active regulatory roles. HIPPIE2 identified 2.8 million significant PIR–PIR interactions, 27.2% of which were enriched for TF binding sites. 50 608 interactions were enhancer–promoter interactions and were enriched for 33 TFs, including known DNA looping/long-range mediators. These findings demonstrate that the novel dynamic approach of HIPPIE2 (https://bitbucket.com/wanglab-upenn/HIPPIE2) enables the characterization of chromatin and regulatory interactions with high resolution and reproducibility.

Hi-C analysis: from data generation to integration

In the epigenetics field, large-scale functional genomics datasets of ever-increasing size and complexity have been produced using experimental techniques based on high-throughput sequencing. In particular, the study of the 3D organization of chromatin has raised increasing interest, thanks to the development of advanced experimental techniques. In this context, Hi-C has been widely adopted as a high-throughput method to measure pairwise contacts between virtually any pair of genomic loci, thus yielding unprecedented challenges for analyzing and handling the resulting complex datasets. In this review, we focus on the increasing complexity of available Hi-C datasets, which parallels the adoption of novel protocol variants. We also review the complexity of the multiple data analysis steps required to preprocess Hi-C sequencing reads and extract biologically meaningful information. Finally, we discuss solutions for handling and visualizing such large genomics datasets.