Predictive Polymer Modeling Reveals Coupled Fluctuations in Chromosome Conformation and Transcription

AbstractFundamental features of 3D genome organization are established de novo in the early embryo, including clustering of pericentromeric regions, the folding of chromosome arms and the segregation of chromosomes into active (A-) and inactive (B-) compartments. However, the molecular mechanisms that drive de novo organization remain unknown1,2. Here, by combining chromosome conformation capture (Hi-C), chromatin immunoprecipitation with high-throughput sequencing (ChIP–seq), 3D DNA fluorescence in situ hybridization (3D DNA FISH) and polymer simulations, we show that heterochromatin protein 1a (HP1a) is essential for de novo 3D genome organization during Drosophila early development. The binding of HP1a at pericentromeric heterochromatin is required to establish clustering of pericentromeric regions. Moreover, HP1a binding within chromosome arms is responsible for overall chromosome folding and has an important role in the formation of B-compartment regions. However, depletion of HP1a does not affect the A-compartment, which suggests that a different molecular mechanism segregates active chromosome regions. Our work identifies HP1a as an epigenetic regulator that is involved in establishing the global structure of the genome in the early embryo.

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Genome Assembly of the Canadian Two-row Malting Barley Cultivar AAC Synergy

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab031 ◽

2021 ◽

Author(s):

Wayne Xu ◽

James R Tucker ◽

Wubishet A Bekele ◽

Frank M You ◽

Yong-Bi Fu ◽

...

Keyword(s):

Reference Genome ◽

Single Copy ◽

Barley Cultivar ◽

Malting Barley ◽

Orthologous Genes ◽

Hordeum Vulgare L ◽

Chromosome Conformation ◽

Mate Pair ◽

Genome Assemblies ◽

First Time

Abstract Barley (Hordeum vulgare L.) is one of the most important global crops. The six-row barley cultivar Morex reference genome has been used by the barley research community worldwide. However, this reference genome can have limitations when used for genomic and genetic diversity analysis studies, gene discovery, and marker development when working in two-row germplasm that is more common to Canadian barley. Here we assembled, for the first time, the genome sequence of a Canadian two-row malting barley, cultivar AAC Synergy. We applied deep Illumina paired-end reads, long mate-pair reads, PacBio sequences, 10X chromium linked read libraries, and chromosome conformation capture sequencing (Hi-C) to generate a contiguous assembly. The genome assembled from super-scaffolds had a size of 4.85 Gb, N50 of 2.32 Mb and an estimated 93.9% of complete genes from a plant database (BUSCO, benchmarking universal single-copy orthologous genes). After removal of small scaffolds (< 300 Kb), the assembly was arranged into pseudomolecules of 4.14 Gb in size with seven chromosomes plus unanchored scaffolds. The completeness and annotation of the assembly were assessed by comparing it with the updated version of six-row Morex and recently released two-row Golden Promise genome assemblies.

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3D reconstruction of genomic regions from sparse interaction data

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab017 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Julen Mendieta-Esteban ◽

Marco Di Stefano ◽

David Castillo ◽

Irene Farabella ◽

Marc A Marti-Renom

Keyword(s):

3D Models ◽

Genomic Region ◽

Gene Promoters ◽

Chromosome Conformation ◽

The Matrix ◽

Chromatin Structural ◽

A Cell ◽

Similar Accuracy ◽

Genomic Regions ◽

Interaction Matrices

Abstract Chromosome conformation capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterize short- and long-range enhancer–promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organization from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalization, detection of significant interactions and reconstruction of the full 3D organization of the genomic region despite of the data sparseness. Specifically, it builds, with as low as the 2–3% of the data from the matrix, reliable 3D models of similar accuracy of those based on dense interaction matrices. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organizational features such as the formation of different networks of active gene communities.

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Advantages of using graph databases to explore chromatin conformation capture experiments

BMC Bioinformatics ◽

10.1186/s12859-020-03937-0 ◽

2021 ◽

Vol 22 (S2) ◽

Author(s):

Daniele D’Agostino ◽

Pietro Liò ◽

Marco Aldinucci ◽

Ivan Merelli

Keyword(s):

Web Application ◽

High Throughput Sequencing ◽

Cell Types ◽

Graph Database ◽

Graph Databases ◽

Sources Of Information ◽

Chromosome Conformation ◽

Wide Scale ◽

User Friendly ◽

Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

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