Assessing relationships between chromatin interactions and regulatory genomic activities using the self-organizing map.

Few existing methods enable the visualization of relationships between regulatory genomic activities and genome organization as captured by Hi-C experimental data. Genome-wide Hi-C datasets are often displayed using "heatmap" matrices, but it is difficult to intuit from these heatmaps which biochemical activities are compartmentalized together. High-dimensional Hi-C data vectors can alternatively be projected onto three-dimensional space using dimensionality reduction techniques. The resulting three-dimensional structures can serve as scaffolds for projecting other forms of genomic information, thereby enabling the exploration of relationships between genome organization and various genome annotations. However, while three-dimensional models are contextually appropriate for chromatin interaction data, some analyses and visualizations may be more intuitively and conveniently performed in two-dimensional space. We present a novel approach to the visualization and analysis of chromatin organization based on the Self-Organizing Map (SOM). The SOM algorithm provides a two-dimensional manifold which adapts to represent the high dimensional chromatin interaction space. The resulting data structure can then be used to assess relationships between regulatory genomic activities and chromatin interactions. For example, given a set of genomic coordinates corresponding to a given biochemical activity, the degree to which this activity is segregated or compartmentalized in chromatin interaction space can be intuitively visualized on the 2D SOM grid and quantified using Lorenz curve analysis. We demonstrate our approach for exploratory analysis of genome compartmentalization in a high-resolution Hi-C dataset from the human GM12878 cell line. Our SOM-based approach provides an intuitive visualization of the large-scale structure of Hi-C data and serves as a platform for integrative analyses of the relationships between various genomic activities and genome organization.

Widespread and precise reprogramming of yeast protein-genome interactions in response to heat shock.

Gene expression is controlled by a variety of proteins that interact with the genome. Their precise organization and mechanism of action at every promoter remains to be worked out. To better understand the physical interplay among genome-interacting proteins, we examined the temporal binding of a functionally diverse subset of these proteins: nucleosomes (H3), H2AZ (Htz1), SWR (Swr1), RSC (Rsc1, Rsc3, Rsc58, Rsc6, Rsc9, Sth1), SAGA (Spt3, Spt7, Ubp8, Sgf11), Hsf1, TFIID (Spt15/TBP and Taf1), TFIIB (Sua7), TFIIH (Ssl2), FACT (Spt16), Pol II (Rpb3), and Pol II carboxyl-terminal domain (CTD) phosphorylation at serines 2, 5, and 7. They were examined under normal and acute heat shock conditions, using the ultrahigh resolution genome-wide ChIP-exo assay in Saccharomyces cerevisiae Our findings reveal a precise positional organization of proteins bound at most genes, some of which rapidly reorganize within minutes of heat shock. This includes more precise positional transitions of Pol II CTD phosphorylation along the 5' ends of genes than previously seen. Reorganization upon heat shock includes colocalization of SAGA with promoter-bound Hsf1, a change in RSC subunit enrichment from gene bodies to promoters, and Pol II accumulation within promoter/+1 nucleosome regions. Most of these events are widespread and not necessarily coupled to changes in gene expression. Together, these findings reveal protein-genome interactions that are robustly reprogrammed in precise and uniform ways far beyond what is elicited by changes in gene expression.

miniMDS: 3D structural inference from high-resolution Hi-C data.

MOTIVATION: Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods. RESULTS: We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 kbp). AVAILABILITY AND IMPLEMENTATION: A Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS . CONTACT: mahony@psu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Diverse Class 2 CRISPR Effectors as Active Nucleases with Expanded Targeting Capabilities.

CRISPR-Cas systems have proven effective in a variety of applications due to their ease of use and relatively high editing efficiency. Yet, any individual CRISPR-Cas system has inherent limitations, necessitating a diversity of RNA-guided nucleases to suit applications with distinct needs. We searched through metagenomic sequences to identify RNA-guided nucleases and found enzymes from diverse CRISPR-Cas types and subtypes, the most promising of which we developed into gene-editing platforms. Based on prior annotations of the metagenomic sequences, we establish the likely taxa and sampling locations where Class 2 CRISPR-Cas systems active in eukaryotes may be found. The newly discovered systems show robust capabilities as gene editors and base editors.

Synthetic regulatory reconstitution reveals principles of mammalian Hox cluster regulation.

Precise Hox gene expression is crucial for embryonic patterning. Intra-Hox transcription factor binding and distal enhancer elements have emerged as the major regulatory modules controlling Hox gene expression. However, quantifying their relative contributions has remained elusive. Here, we introduce "synthetic regulatory reconstitution," a conceptual framework for studying gene regulation, and apply it to the HoxA cluster. We synthesized and delivered variant rat HoxA clusters (130 to 170 kilobases) to an ectopic location in the mouse genome. We found that a minimal HoxA cluster recapitulated correct patterns of chromatin remodeling and transcription in response to patterning signals, whereas the addition of distal enhancers was needed for full transcriptional output. Synthetic regulatory reconstitution could provide a generalizable strategy for deciphering the regulatory logic of gene expression in complex genomes.

Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types.

BACKGROUND: Comparisons of Hi-C data sets between cell types and conditions have revealed differences in topologically associated domains (TADs) and A/B compartmentalization, which are correlated with differences in gene regulation. However, previous comparisons have focused on known forms of 3D organization while potentially neglecting other functionally relevant differences. We aimed to create a method to quantify all locus-specific differences between two Hi-C data sets. RESULTS: We developed MultiMDS to jointly infer and align 3D chromosomal structures from two Hi-C data sets, thereby enabling a new way to comprehensively quantify relocalization of genomic loci between cell types. We demonstrate this approach by comparing Hi-C data across a variety of cell types. We consistently find relocalization of loci with minimal difference in A/B compartment score. For example, we identify compartment-independent relocalizations between GM12878 and K562 cells that involve loci displaying enhancer-associated histone marks in one cell type and polycomb-associated histone marks in the other. CONCLUSIONS: MultiMDS is the first tool to identify all loci that relocalize between two Hi-C data sets. Our method can identify 3D localization differences that are correlated with cell-type-specific regulatory activities and which cannot be identified using other methods.