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Current models propose that boundaries of mammalian topologically associating domains (TADs) arise from the ability of the CTCF protein to stop extrusion of chromatin loops by cohesin. While the orientation of CTCF motifs determines which pairs of CTCF sites preferentially stabilize loops, the molecular basis of this polarity remains unclear. By combining ChIP-seq and single molecule live imaging we report that CTCF positions cohesin, but does not control its overall binding dynamics on chromatin. Using an inducible complementation system, we find that CTCF mutants lacking the N-terminus cannot insulate TADs properly. Cohesin remains at CTCF sites in this mutant, albeit with reduced enrichment. Given the orientation of CTCF motifs presents the N-terminus towards cohesin as it translocates from the interior of TADs, these observations explain how the orientation of CTCF binding sites translates into genome folding patterns.

During meiotic prophase, chromosomes organise into a series of chromatin loops emanating from a proteinaceous axis, but the mechanisms of assembly remain unclear. Here we use Saccharomyces cerevisiae to explore how this elaborate three-dimensional chromosome organisation is linked to genomic sequence. As cells enter meiosis, we observe that strong cohesin-dependent grid-like Hi-C interaction patterns emerge, reminiscent of mammalian interphase organisation, but with distinct regulation. Meiotic patterns agree with simulations of loop extrusion with growth limited by barriers, in which a heterogeneous population of expanding loops develop along the chromosome. Importantly, CTCF, the factor that imposes similar features in mammalian interphase, is absent in S. cerevisiae, suggesting alternative mechanisms of barrier formation. While grid-like interactions emerge independently of meiotic chromosome synapsis, synapsis itself generates additional compaction that matures differentially according to telomere proximity and chromosome size. Collectively, our results elucidate fundamental principles of chromosome assembly and demonstrate the essential role of cohesin within this evolutionarily conserved process.

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. Author summary: Our study introduces pairtools, a computational suite for extracting pairwise contacts from Hi-C and the rapidly-expanding constellation of chromosome conformation protocols (3C+). These experiments use DNA sequencing to measure the 3D structure of chromosomes inside cells. However, specialized software is needed to extract chromosome contacts from the raw sequencing data. Pairtools provides fast, flexible, and modular command-line tools and a Python framework to bridge this gap. We show pairtools can process data from many Hi-C protocol variants beyond standard Hi-C and is easily integrated into pipelines for high-throughput 3D genome data processing. By converting sequence data into tables of chromosome contacts, pairtools facilitates statistical analysis and visualization. Pairtools represents a versatile new foundation for studying principles of 3D genome organization and their impacts on gene regulation and cellular phenotypes. [ABSTRACT FROM AUTHOR]

Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers' time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce cooltools (https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. Cooltools leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. Cooltools provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, cooltools enables the effective use of the latest and largest genome folding datasets. Author summary: Chromosome conformation capture (3C) experiments, including Hi-C, measure the 3D organization of chromosomes inside cells. As 3C datasets grow larger and higher-resolution, analyzing them poses computational challenges. Our open-source Python package cooltools meets these challenges. Cooltools integrates smoothly with cooler, a software library for storing and accessing very large Hi-C datasets. Cooltools enables users to extract key features seen in 3C maps, including: the decay of contact frequency with genomic distance, plaid patterns of active and inactive compartments, domains, and dots. Cooltools quantifies these folding features from raw contact data efficiently, handling chromosome-scale datasets too large to fit in memory. The command-line and Python interfaces make cooltools easy to integrate into pipelines or customized for new analyses. By overcoming data size and memory hurdles, cooltools allows researchers to harness 3C's full potential for understanding principles of genome folding and function. [ABSTRACT FROM AUTHOR]

Motivation Genomic intervals are one of the most prevalent data structures in computational genome biology, and used to represent features ranging from genes, to DNA binding sites, to disease variants. Operations on genomic intervals provide a language for asking questions about relationships between features. While there are excellent interval arithmetic tools for the command line, they are not smoothly integrated into Python, one of the most popular general-purpose computational and visualization environments. Results Bioframe is a library to enable flexible and performant operations on genomic interval dataframes in Python. Bioframe extends the Python data science stack to use cases for computational genome biology by building directly on top of two of the most commonly-used Python libraries, NumPy and Pandas. The bioframe API enables flexible name and column orders, and decouples operations from data formats to avoid unnecessary conversions, a common scourge for bioinformaticians. Bioframe achieves these goals while maintaining high performance and a rich set of features. Availability and implementation Bioframe is open-source under MIT license, cross-platform, and can be installed from the Python Package Index. The source code is maintained by Open2C on GitHub at https://github.com/open2c/bioframe. [ABSTRACT FROM AUTHOR]

Imaging and chromosome conformation capture studies have revealed several layers of chromosome organization, including segregation into megabase-sized active and inactive compartments, and partitioning into sub-megabase domains (TADs). It remains unclear, however, how these layers of organization form, interact with one another and influence genome function. Here we show that deletion of the cohesin-loading factor Nipbl in mouse liver leads to a marked reorganization of chromosomal folding. TADs and associated Hi-C peaks vanish globally, even in the absence of transcriptional changes. By contrast, compartmental segregation is preserved and even reinforced. Strikingly, the disappearance of TADs unmasks a finer compartment structure that accurately reflects the underlying epigenetic landscape. These observations demonstrate that the three-dimensional organization of the genome results from the interplay of two independent mechanisms: cohesin-independent segregation of the genome into fine-scale compartments, defined by chromatin state; and cohesin-dependent formation of TADs, possibly by loop extrusion, which helps to guide distant enhancers to their target genes., Author(s): Wibke Schwarzer [1]; Nezar Abdennur [2]; Anton Goloborodko [3]; Aleksandra Pekowska [4]; Geoffrey Fudenberg [5]; Yann Loe-Mie [6, 7]; Nuno A Fonseca [8]; Wolfgang Huber [4]; Christian H. Haering [...]

Metazoan genomes are spatially organized at multiple scales, from packaging of DNA around individual nucleosomes to segregation of whole chromosomes into distinct territories (1-5). At the intermediate scale of kilobases to megabases, which encompasses the sizes of genes, gene clusters and regulatory domains, the three-dimensional (3D) organization of DNA is implicated in multiple gene regulatory mechanisms (2-4,6-8), but understanding this organization remains a challenge. At this scale, the genome is partitioned into domains of different epigenetic states that are essential for regulating gene expression (9-11). Here we investigate the 3D organization of chromatin in different epigenetic states using super-resolution imaging. We classified genomic domains in Drosophila cells into transcriptionally active, inactive or Polycomb-repressed states, and observed distinct chromatin organizations for each state. All three types of chromatin domains exhibit power-law scaling between their physical sizes in 3D and their domain lengths, but each type has a distinct scaling exponent. Polycomb-repressed domains show the densest packing and most intriguing chromatin folding behaviour, in which chromatin packing density increases with domain length. Distinct from the self-similar organization displayed by transcriptionally active and inactive chromatin, the Polycomb-repressed domains are characterized by a high degree of chromatin intermixing within the domain. Moreover, compared to inactive domains, Polycomb-repressed domains spatially exclude neighbouring active chromatin to a much stronger degree. Computational modelling and knockdown experiments suggest that reversible chromatin interactions mediated by Polycomb-group proteins play an important role in these unique packaging properties of the repressed chromatin. Taken together, our super-resolution images reveal distinct chromatin packaging for different epigenetic states at the kilobase-to-megabase scale, a length scale that is directly relevant to genome regulation., Multiple lines of evidence suggest that the spatial organization of chromatin at the kilobase-to-megabase scale is important for genomic functions (2-4,6-8,10-12). The sizes of genes, gene clusters and regulatory domains [...]