Integrative analysis of the 3D genome and epigenome in mouse embryonic tissues.

While a rich set of putative cis-regulatory sequences involved in mouse fetal development have been annotated recently on the basis of chromatin accessibility and histone modification patterns, delineating their role in developmentally regulated gene expression continues to be challenging. To fill this gap, here we mapped chromatin contacts between gene promoters and distal sequences across the genome in seven mouse fetal tissues and across six developmental stages of the forebrain. We identified 248,620 long-range chromatin interactions centered at 14,138 protein-coding genes and characterized their tissue-to-tissue variations and developmental dynamics.

Combinatorial transcription factor binding encodes cis-regulatory wiring of mouse forebrain GABAergic neurogenesis.

Transcription factors (TFs) bind combinatorially to cis-regulatory elements, orchestrating transcriptional programs. Although studies of chromatin state and chromosomal interactions have demonstrated dynamic neurodevelopmental cis-regulatory landscapes, parallel understanding of TF interactions lags. To elucidate combinatorial TF binding driving mouse basal ganglia development, we integrated chromatin immunoprecipitation sequencing (ChIP-seq) for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and gene expression data, and functional enhancer assays.

HPTAD: A computational method to identify topologically associating domains from HiChIP and PLAC-seq datasets.

High-throughput chromatin conformation capture technologies, such as Hi-C and Micro-C, have enabled genome-wide view of chromatin spatial organization. Most recently, Hi-C-derived enrichment-based technologies, including HiChIP and PLAC-seq, offer attractive alternatives due to their high signal-to-noise ratio and low cost. While a series of computational tools have been developed for Hi-C data, methods tailored for HiChIP and PLAC-seq data are still under development.

Combinatorial transcription factor binding encodes cis-regulatory wiring of forebrain GABAergic neurogenesis.

Transcription factors (TFs) bind combinatorially to genomic cis-regulatory elements (cREs), orchestrating transcription programs. While studies of chromatin state and chromosomal interactions have revealed dynamic neurodevelopmental cRE landscapes, parallel understanding of the underlying TF binding lags. To elucidate the combinatorial TF-cRE interactions driving mouse basal ganglia development, we integrated ChIP-seq for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and transcriptional state, and transgenic enhancer assays.

SnapHiC2: A computationally efficient loop caller for single cell Hi-C data.

Single cell Hi-C (scHi-C) technologies enable the study of chromatin spatial organization directly from complex tissues at single cell resolution. However, the identification of chromatin loops from single cells is challenging, largely due to the extremely sparse data. Our recently developed SnapHiC pipeline provides the first tool to map chromatin loops from scHi-C data, but it is computationally intensive.

A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data.

The three-dimensional organization of chromatin plays a critical role in gene regulation. Recently developed technologies, such as HiChIP and proximity ligation-assisted ChIP-Seq (PLAC-seq) (hereafter referred to as HP for brevity), can measure chromosome spatial organization by interrogating chromatin interactions mediated by a protein of interest. While offering cost-efficiency over genome-wide unbiased high-throughput chromosome conformation capture (Hi-C) data, HP data remain sparse at kilobase (Kb) resolution with the current sequencing depth in the order of 108 reads per sample.

Transcriptional network orchestrating regional patterning of cortical progenitors.

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 (, and ), which enabled us to define a cortical regionalization TF network (CRTFN).

HPRep: Quantifying Reproducibility in HiChIP and PLAC-Seq Datasets.

HiChIP and PLAC-Seq are emerging technologies for studying genome-wide long-range chromatin interactions mediated by the protein of interest, enabling more sensitive and cost-efficient interrogation of protein-centric chromatin conformation. However, due to the unbalanced read distribution introduced by protein immunoprecipitation, existing reproducibility measures developed for Hi-C data are not appropriate for the analysis of HiChIP and PLAC-Seq data. Here, we present HPRep, a stratified and weighted correlation metric derived from normalized contact counts, to quantify reproducibility in HiChIP and PLAC-Seq data.

Parallel characterization of cis-regulatory elements for multiple genes using CRISPRpath.

Current pooled CRISPR screens for cis-regulatory elements (CREs), based on transcriptional output changes, are typically limited to characterizing CREs of only one gene. Here, we describe CRISPRpath, a scalable screening strategy for parallelly characterizing CREs of genes linked to the same biological pathway and converging phenotypes. We demonstrate the ability of CRISPRpath for simultaneously identifying functional enhancers of six genes in the 6-thioguanine–induced DNA mismatch repair pathway using both CRISPR interference (CRISPRi) and CRISPR nuclease (CRISPRn) approaches.

MUNIn: A statistical framework for identifying long-range chromatin interactions from multiple samples.

Chromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions.

SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data.

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.

Proximity Ligation-Assisted ChIP-Seq (PLAC-Seq).

Proximity ligation-assisted ChIP-Seq (PLAC-Seq), also known as HiChIP, is a method to detect and quantify chromatin contacts anchored at genomic regions bound by specific proteins or histone modifications. By combining in situ Hi-C and chromatin immunoprecipitation (ChIP) using antibodies against transcription factors (TFs) or histone marks of interest, the method achieves targeted interrogation of chromatin organization at a subset of genomic regions. PLAC-Seq is able to identify long-range chromatin interactions at kilobase-scale resolution with significantly reduced sequencing cost.

HiC-ACT: improved detection of chromatin interactions from Hi-C data via aggregated Cauchy test.

Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.

FIREcaller: Detecting frequently interacting regions from Hi-C data.

Hi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking.

Cell-type-specific 3D epigenomes in the developing human cortex.

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. 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.

Global analysis of histone modifications and long-range chromatin interactions revealed the differential cistrome changes and novel transcriptional players in human dilated cardiomyopathy.

Background: Acetylation and methylation of histones alter the chromatin structure and accessibility that affect transcriptional regulators binding to enhancers and promoters. The binding of transcriptional regulators enables the interaction between enhancers and promoters, thus affecting gene expression. However, our knowledge of these epigenetic alternations in patients with heart failure remains limited.

An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome.

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.

Publisher Correction: Sensory experience remodels genome architecture in neural circuit to drive motor learning.

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.

Sensory experience remodels genome architecture in neural circuit to drive motor learning.

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. 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.

MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments.

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.

Alignment-free clustering of large data sets of unannotated protein conserved regions using minhashing.

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.

A Fast Alignment-Free Approach for De Novo Detection of Protein Conserved Regions.

Background: 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.