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Single-cell Hi-C (scHi-C) analysis has been increasingly used to map chromatin architecture in diverse tissue contexts, but computational tools to define chromatin loops at high resolution from scHi-C data are still lacking. Here, we describe Single-Nucleus Analysis Pipeline for Hi-C (SnapHiC), a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. Using scHi-C data from 742 mouse embryonic stem cells, we benchmark SnapHiC against a number of computational tools developed for mapping chromatin loops and interactions from bulk Hi-C. We further demonstrate its use by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells, which uncovers cell type-specific chromatin loops and predicts putative target genes for noncoding sequence variants associated with neuropsychiatric disorders. Our results indicate that SnapHiC could facilitate the analysis of cell type-specific chromatin architecture and gene regulatory programs in complex tissues.SnapHiC offers a computational tool for improving detection of chromatin loops from single-cell Hi-C data.

Simultaneous profiling of transcriptome and chromatin accessibility within single cells is a powerful approach to dissect gene regulatory programs in complex tissues. However, current tools are limited by modest throughput. We now describe an ultra high-throughput method, Paired-seq, for parallel analysis of transcriptome and accessible chromatin in millions of single cells. We demonstrate the utility of Paired-seq for analyzing the dynamic and cell-type-specific gene regulatory programs in complex tissues by applying it to mouse adult cerebral cortex and fetal forebrain. The joint profiles of a large number of single cells allowed us to deconvolute the transcriptome and open chromatin landscapes in the major cell types within these brain tissues, infer putative target genes of candidate enhancers, and reconstruct the trajectory of cellular lineages within the developing forebrain.

Lineage-specific epigenomic changes during human corticogenesis have been difficult to study owing to challenges with sample availability and tissue heterogeneity. For example, previous studies using single-cell RNA sequencing identified at least 9 major cell types and up to 26 distinct subtypes in the dorsal cortex alone 1 , 2 . Here we characterize cell-type-specific cis -regulatory chromatin interactions, open chromatin peaks, and transcriptomes for radial glia, intermediate progenitor cells, excitatory neurons, and interneurons isolated from mid-gestational samples of the human cortex. We show that chromatin interactions underlie several aspects of gene regulation, with transposable elements and disease-associated variants enriched at distal interacting regions in a cell-type-specific manner. In addition, promoters with increased levels of chromatin interactivity—termed super-interactive promoters—are enriched for lineage-specific genes, suggesting that interactions at these loci contribute to the fine-tuning of transcription. Finally, we develop CRISPRview, a technique that integrates immunostaining, CRISPR interference, RNAscope, and image analysis to validate cell-type-specific cis -regulatory elements in heterogeneous populations of primary cells. Our findings provide insights into cell-type-specific gene expression patterns in the developing human cortex and advance our understanding of gene regulation and lineage specification during this crucial developmental window. Analysis of cis -regulatory chromatin interactions, open chromatin and transcriptomes for different cell types isolated from mid-gestational human cortex samples provides insights into gene regulation during development.

Hi-C and chromatin immunoprecipitation (ChIP) have been combined to identify long-range chromatin interactions genome-wide at reduced cost and enhanced resolution, but extracting information from the resulting datasets has been challenging. Here we describe a computational method, MAPS, Model-based Analysis of PLAC-seq and HiChIP, to process the data from such experiments and identify long-range chromatin interactions. MAPS adopts a zero-truncated Poisson regression framework to explicitly remove systematic biases in the PLAC-seq and HiChIP datasets, and then uses the normalized chromatin contact frequencies to identify significant chromatin interactions anchored at genomic regions bound by the protein of interest. MAPS shows superior performance over existing software tools in the analysis of chromatin interactions from multiple PLAC-seq and HiChIP datasets centered on different transcriptional factors and histone marks. MAPS is freely available at https://github.com/ijuric/MAPS.

Background Clustering of protein sequences is of key importance in predicting the structure and function of newly sequenced proteins and is also of use for their annotation. With the advent of multiple high-throughput sequencing technologies, new protein sequences are becoming available at an extraordinary rate. The rapid growth rate has impeded deployment of existing protein clustering/annotation tools which depend largely on pairwise sequence alignment. Results In this paper, we propose an alignment-free clustering approach, coreClust, for annotating protein sequences using detected conserved regions. The proposed algorithm uses Min-Wise Independent Hashing for identifying similar conserved regions. Min-Wise Independent Hashing works by generating a (w,c)-sketch for each document and comparing these sketches. Our algorithm fits well within the MapReduce framework, permitting scalability. We show that coreClust generates results comparable to existing known methods. In particular, we show that the clusters generated by our algorithm capture the subfamilies of the Pfam domain families for which the sequences in a cluster have a similar domain architecture. We show that for a data set of 90,000 sequences (about 250,000 domain regions), the clusters generated by our algorithm give a 75% average weighted F 1 score, our accuracy metric, when compared to the clusters generated by a semi-exhaustive pairwise alignment algorithm. Conclusions The new clustering algorithm can be used to generate meaningful clusters of conserved regions. It is a scalable method that when paired with our prior work, NADDA for detecting conserved regions, provides a complete end-to-end pipeline for annotating protein sequences.

Identifying conserved regions in protein sequences is a fundamental operation, occurring in numerous sequence-driven analysis pipelines. It is used as a way to decode domain-rich regions within proteins, to compute protein clusters, to annotate sequence function, and to compute evolutionary relationships among protein sequences. A number of approaches exist for identifying and characterizing protein families based on their domains, and because domains represent conserved portions of a protein sequence, the primary computation involved in protein family characterization is identification of such conserved regions. However, identifying conserved regions from large collections (millions) of protein sequences presents significant challenges. In this paper we present a new, alignment-free method for detecting conserved regions in protein sequences called NADDA (No-Alignment Domain Detection Algorithm). Our method exploits the abundance of exact matching short subsequences (k-mers) to quickly detect conserved regions, and the power of machine learning is used to improve the prediction accuracy of detection. We present a parallel implementation of NADDA using the MapReduce framework and show that our method is highly scalable. We have compared NADDA with Pfam and InterPro databases. For known domains annotated by Pfam, accuracy is 83%, sensitivity 96%, and specificity 44%. For sequences with new domains not present in the training set an average accuracy of 63% is achieved when compared to Pfam. A boost in results in comparison with InterPro demonstrates the ability of NADDA to capture conserved regions beyond those present in Pfam. We have also compared NADDA with ADDA and MKDOM2, assuming Pfam as ground-truth. On average NADDA shows comparable accuracy, more balanced sensitivity and specificity, and being alignment-free, is significantly faster. Excluding the one-time cost of training, runtimes on a single processor were 49s, 10,566s, and 456s for NADDA, ADDA, and MKDOM2, respectively, for a data set comprised of approximately 2500 sequences.

In this Letter, ‘≥’ should be ‘≤’ in the sentence: “Intra-chromosomal reads were further split into short-range reads (≥1 kb) and long-range reads (>1 kb)”. This error has been corrected online. An amendment to this paper has been published and can be accessed via a link at the top of the paper

Oncogenes are commonly amplified on particles of extrachromosomal DNA (ecDNA) in cancer 1 , 2 , but our understanding of the structure of ecDNA and its effect on gene regulation is limited. Here, by integrating ultrastructural imaging, long-range optical mapping and computational analysis of whole-genome sequencing, we demonstrate the structure of circular ecDNA. Pan-cancer analyses reveal that oncogenes encoded on ecDNA are among the most highly expressed genes in the transcriptome of the tumours, linking increased copy number with high transcription levels. Quantitative assessment of the chromatin state reveals that although ecDNA is packaged into chromatin with intact domain structure, it lacks higher-order compaction that is typical of chromosomes and displays significantly enhanced chromatin accessibility. Furthermore, ecDNA is shown to have a significantly greater number of ultra-long-range interactions with active chromatin, which provides insight into how the structure of circular ecDNA affects oncogene function, and connects ecDNA biology with modern cancer genomics and epigenetics. Imaging and sequencing approaches are combined to show that extrachromosomal DNA (ecDNA) in cancer is circular and has unique chromatin structure that amplifies oncogene output.

Neuronal-activity-dependent transcription couples sensory experience to adaptive responses of the brain including learning and memory. Mechanisms of activity-dependent gene expression including alterations of the epigenome have been characterized 1 – 8 . However, the fundamental question of whether sensory experience remodels chromatin architecture in the adult brain in vivo to induce neural code transformations and learning and memory remains to be addressed. Here we use in vivo calcium imaging, optogenetics and pharmacological approaches to show that granule neuron activation in the anterior dorsal cerebellar vermis has a crucial role in a delay tactile startle learning paradigm in mice. Of note, using large-scale transcriptome and chromatin profiling, we show that activation of the motor-learning-linked granule neuron circuit reorganizes neuronal chromatin including through long-distance enhancer–promoter and transcriptionally active compartment interactions to orchestrate distinct granule neuron gene expression modules. Conditional CRISPR knockout of the chromatin architecture regulator cohesin in anterior dorsal cerebellar vermis granule neurons in adult mice disrupts enhancer–promoter interactions, activity-dependent transcription and motor learning. These findings define how sensory experience patterns chromatin architecture and neural circuit coding in the brain to drive motor learning. The authors identify a role for genome architecture reorganization in anterior dorsal cerebellar vermis granule neurons in learning a conditioned startle paradigm in mice.

We uncovered a transcription factor (TF) network that regulates cortical regional patterning in radial glial stem cells. Screening the expression of hundreds of TFs in the developing mouse cortex identified 38 TFs that are expressed in gradients in the ventricular zone (VZ). We tested whether their cortical expression was altered in mutant mice with known patterning defects (Emx2, Nr2f1, and Pax6), which enabled us to define a cortical regionalization TF network (CRTFN). To identify genomic programming underlying this network, we performed TF ChIP-seq and chromatin-looping conformation to identify enhancer–gene interactions. To map enhancers involved in regional patterning of cortical progenitors, we performed assays for epigenomic marks and DNA accessibility in VZ cells purified from wild-type and patterning mutant mice. This integrated approach has identified a CRTFN and VZ enhancers involved in cortical regional patterning in the mouse.