S3norm: simultaneous normalization of sequencing depth and signal-to-noise ratio in epigenomic data

Robust chromatin state annotation

With the goal of mapping genomic activity, international projects have recently measured epigenetic activity in hundreds of cell and tissue types. Chromatin state annotations produced by segmentation and genome annotation (SAGA) methods have emerged as the predominant way to summarize these epigenomic data sets in order to annotate the genome. These chromatin state annotations are essential for many genomic tasks, including identifying active regulatory elements and interpreting disease-associated genetic variation. However, despite the widespread applications of SAGA methods, no principled approach exists to evaluate the statistical significance of chromatin state assignments. Here, we propose the first method for assigning calibrated confidence scores to chromatin state annotations. Toward this goal, we performed a comprehensive evaluation of the reproducibility of the two most widely used existing SAGA methods, ChromHMM and Segway. We found that their predictions are frequently irreproducible. For example, when applying the same SAGA method on two sets of experimental replicates, 27%-69% of predicted enhancers fail to replicate. This suggests that a substantial fraction of predicted elements in existing chromatin state annotations cannot be relied upon. To remedy this problem, we introduce SAGAconf, a method for assigning a measure of confidence (r-value) to chromatin state annotations. SAGAconf works with any SAGA method and assigns an r-value to each genomic bin of a chromatin state annotation that represents the probability that the label of this bin will be reproduced in a replicated experiment. Thus, SAGAconf allows a researcher to select only the reliable predictions from a chromatin annotation for use in downstream analyses.

Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes

Knowledge of locations and activities of cis-regulatory elements (CREs) is needed to decipher basic mechanisms of gene regulation and to understand the impact of genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining epigenetic states and identifying cCREs in blood cell types to generate regulatory maps that are comparable between species, using integrative modeling of eight epigenetic features jointly in human and mouse in our Validated Systematic Integration (VISION) Project. The resulting catalogs of cCREs are useful resources for further studies of gene regulation in blood cells, indicated by high overlap with known functional elements and strong enrichment for human genetic variants associated with blood cell phenotypes. The contribution of each epigenetic state in cCREs to gene regulation, inferred from a multivariate regression, was used to estimate epigenetic state Regulatory Potential (esRP) scores for each cCRE in each cell type, which were used to categorize dynamic changes in cCREs. Groups of cCREs displaying similar patterns of regulatory activity in human and mouse cell types, obtained by joint clustering on esRP scores, harbored distinctive transcription factor binding motifs that were similar between species. An interspecies comparison of cCREs revealed both conserved and species-specific patterns of epigenetic evolution. Finally, we showed that comparisons of the epigenetic landscape between species can reveal elements with similar roles in regulation, even in the absence of genomic sequence alignment.

The Density of Regulatory Information Is a Major Determinant of Evolutionary Constraint on Noncoding DNA in Drosophila

Evolutionary analyses have estimated that ∼60% of nucleotides in intergenic regions of the Drosophila melanogaster genome are functionally relevant, suggesting that regulatory information may be encoded more densely in intergenic regions than has been revealed by most functional dissections of regulatory DNA. Here, we approached this issue through a functional dissection of the regulatory region of the gene shavenbaby (svb). Most of the ∼90 kb of this large regulatory region is highly conserved in the genus Drosophila, though characterized enhancers occupy a small fraction of this region. By analyzing the regulation of svb in different contexts of Drosophila development, we found that the regulatory information that drives svb expression in the abdominal pupal epidermis is organized in a different way than the elements that drive svb expression in the embryonic epidermis. While in the embryonic epidermis svb is activated by compact enhancers separated by large inactive DNA regions, svb expression in the pupal epidermis is driven by regulatory information distributed over broader regions of svb cis-regulatory DNA. In the same vein, we observed that other developmental genes also display a dense distribution of putative regulatory elements in their regulatory regions. Furthermore, we found that a large percentage of conserved noncoding DNA of the Drosophila genome is contained within regions of open chromatin. These results suggest that part of the evolutionary constraint on noncoding DNA of Drosophila is explained by the density of regulatory information, which may be greater than previously appreciated.

JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics

Combinatorial patterns of epigenetic features reflect transcriptional states and functions of genomic regions. While many epigenetic features have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally correlated epigenetic features and hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple epigenetic features across cell types, species, and experimental conditions by leveraging information from partially correlated epigenetic features. We demonstrate that JMnorm-normalized data can better preserve cross-epigenetic-feature correlations across different cell types and enhance consistency between biological replicates than data normalized by other methods. Additionally, we show that JMnorm-normalized data can consistently improve the performance of various downstream analyses, which include candidate cis-regulatory element clustering, cross-cell-type gene expression prediction, detection of transcription factor binding and changes upon perturbations. These findings suggest that JMnorm effectively minimizes technical noise while preserving true biologically significant relationships between epigenetic datasets. We anticipate that JMnorm will enhance integrative and comparative epigenomics.

Cell-type differential targeting of SETDB1 prevents aberrant CTCF binding, chromatin looping, and cis-regulatory interactions

SETDB1 is an essential histone methyltransferase that deposits histone H3 lysine 9 trimethylation (H3K9me3) to transcriptionally repress genes and repetitive elements. The function of differential H3K9me3 enrichment between cell-types remains unclear. Here, we demonstrate mutual exclusivity of H3K9me3 and CTCF across mouse tissues from different developmental timepoints. We analyze SETDB1 depleted cells and discover that H3K9me3 prevents aberrant CTCF binding independently of DNA methylation and H3K9me2. Such sites are enriched with SINE B2 retrotransposons. Moreover, analysis of higher-order genome architecture reveals that large chromatin structures including topologically associated domains and subnuclear compartments, remain intact in SETDB1 depleted cells. However, chromatin loops and local 3D interactions are disrupted, leading to transcriptional changes by modifying pre-existing chromatin landscapes. Specific genes with altered expression show differential interactions with dysregulated cis-regulatory elements. Collectively, we find that cell-type specific targets of SETDB1 maintain cellular identities by modulating CTCF binding, which shape nuclear architecture and transcriptomic networks.

Predicting the effect of CRISPR-Cas9-based epigenome editing

Epigenetic regulation orchestrates mammalian transcription, but functional links between them remain elusive. To tackle this problem, we here use epigenomic and transcriptomic data from 13 ENCODE cell types to train machine learning models to predict gene expression from histone post-translational modifications (PTMs), achieving transcriptome-wide correlations of ~ 0.70 - 0.79 for most samples. In addition to recapitulating known associations between histone PTMs and expression patterns, our models predict that acetylation of histone subunit H3 lysine residue 27 (H3K27ac) near the transcription start site (TSS) significantly increases expression levels. To validate this prediction experimentally and investigate how engineered vs. natural deposition of H3K27ac might differentially affect expression, we apply the synthetic dCas9-p300 histone acetyltransferase system to 8 genes in the HEK293T cell line. Further, to facilitate model building, we perform MNase-seq to map genome-wide nucleosome occupancy levels in HEK293T. We observe that our models perform well in accurately ranking relative fold changes among genes in response to the dCas9-p300 system; however, their ability to rank fold changes within individual genes is noticeably diminished compared to predicting expression across cell types from their native epigenetic signatures. Our findings highlight the need for more comprehensive genome-scale epigenome editing datasets, better understanding of the actual modifications made by epigenome editing tools, and improved causal models that transfer better from endogenous cellular measurements to perturbation experiments. Together these improvements would facilitate the ability to understand and predictably control the dynamic human epigenome with consequences for human health.

An NFIX-mediated regulatory network governs the balance of hematopoietic stem and progenitor cells during hematopoiesis

The transcription factor (TF) nuclear factor I-X (NFIX) is a positive regulator of hematopoietic stem and progenitor cell (HSPC) transplantation. Nfix-deficient HSPCs exhibit a severe loss of repopulating activity, increased apoptosis, and a loss of colony-forming potential. However, the underlying mechanism remains elusive. Here, we performed cellular indexing of transcriptomes and epitopes by high-throughput sequencing (CITE-seq) on Nfix-deficient HSPCs and observed a loss of long-term hematopoietic stem cells and an accumulation of megakaryocyte and myelo-erythroid progenitors. The genome-wide binding profile of NFIX in primitive murine hematopoietic cells revealed its colocalization with other hematopoietic TFs, such as PU.1. We confirmed the physical interaction between NFIX and PU.1 and demonstrated that the 2 TFs co-occupy super-enhancers and regulate genes implicated in cellular respiration and hematopoietic differentiation. In addition, we provide evidence suggesting that the absence of NFIX negatively affects PU.1 binding at some genomic loci. Our data support a model in which NFIX collaborates with PU.1 at super-enhancers to promote the differentiation and homeostatic balance of hematopoietic progenitors.

The canonical E2Fs together with RETINOBLASTOMA-RELATED are required to establish quiescence during plant development

Maintaining stable and transient quiescence in differentiated and stem cells, respectively, requires repression of the cell cycle. The plant RETINOBLASTOMA-RELATED (RBR) has been implicated in stem cell maintenance, presumably by forming repressor complexes with E2F transcription factors. Surprisingly we find that mutations in all three canonical E2Fs do not hinder the cell cycle, but similarly to RBR silencing, result in hyperplasia. Contrary to the growth arrest that occurs when exit from proliferation to differentiation is inhibited upon RBR silencing, the e2fabc mutant develops enlarged organs with supernumerary stem and differentiated cells as quiescence is compromised. While E2F, RBR and the M-phase regulatory MYB3Rs are part of the DREAM repressor complexes, and recruited to overlapping groups of targets, they regulate distinct sets of genes. Only the loss of E2Fs but not the MYB3Rs interferes with quiescence, which might be due to the ability of E2Fs to control both G1-S and some key G2-M targets. We conclude that collectively the three canonical E2Fs in complex with RBR have central roles in establishing cellular quiescence during organ development, leading to enhanced plant growth.

Single-cell analysis reveals transcriptomic and epigenomic impacts on the maternal-fetal interface following SARS-CoV-2 infection

During pregnancy the maternal-fetal interface plays vital roles in fetal development. Its disruption is frequently found in pregnancy complications. Recent studies show increased incidences of adverse pregnancy outcomes in patients with COVID-19; however, the mechanism remains unclear. Here we analysed the molecular impacts of SARS-CoV-2 infection on the maternal-fetal interface. Generating bulk and single-nucleus transcriptomic and epigenomic profiles from patients with COVID-19 and control samples, we discovered aberrant immune activation and angiogenesis patterns in distinct cells from patients. Surprisingly, retrotransposons were also dysregulated in specific cell types. Notably, reduced enhancer activities of LTR8B elements were functionally linked to the downregulation of pregnancy-specific glycoprotein genes in syncytiotrophoblasts. Our findings revealed that SARS-CoV-2 infection induced substantial changes to the epigenome and transcriptome at the maternal-fetal interface, which may be associated with pregnancy complications.

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.

Snapshot: a package for clustering and visualizing epigenetic history during cell differentiation

BACKGROUND

Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity of epigenetic data pose significant challenges for biologists to identify the regulatory events controlling cell differentiation.

RESULTS

To reduce the complexity, we developed a package, called Snapshot, for clustering and visualizing candidate cis-regulatory elements (cCREs) based on their epigenetic signals during cell differentiation. This package first introduces a binarized indexing strategy for clustering the cCREs. It then provides a series of easily interpretable figures for visualizing the signal and epigenetic state patterns of the cCREs clusters during the cell differentiation. It can also use different hierarchies of cell types to highlight the epigenetic history specific to any particular cell lineage. We demonstrate the utility of Snapshot using data from a consortium project for ValIdated Systematic IntegratiON (VISION) of epigenomic data in hematopoiesis.

CONCLUSION

The package Snapshot can identify all distinct clusters of genomic locations with unique epigenetic signal patterns during cell differentiation. It outperforms other methods in terms of interpreting and reproducing the identified cCREs clusters. The package of Snapshot is available at GitHub: https://github.com/guanjue/Snapshot .

Chromatin-bound protein colocalization analysis using bedGraph2Cluster and PanChIP

Computational pipelines for chromatin immunoprecipitation sequencing analysis can neglect colocalization events that occur in a mere subset of the genome. Here, we detail a streamlined approach for assessing colocalization of chromatin-bound proteins using the bedGraph2Cluster and PanChIP algorithms. Using histone modifications as an example, bedGraph2Cluster performs clustering analysis on chromatin binding patterns of target proteins. PanChIP then compares these clusters with a reference library of chromatin binding patterns and measures the overlap in peaks, capturing the heterogeneity in chromatin binding and colocalization patterns. For complete details on the use and execution of this protocol, please refer to Sanidas et al. (2022).1.

Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure

RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome and a subsequent motif enrichment analysis suggest potential links of RNA structural variation and mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome.

The authors present DiffScan, an advanced tool for normalization and differential analysis of RNA structure probing experiments, combining their power in deciphering the dynamic RNA structurome and facilitating the discovery of RNA regulatory functions.

Activation of γ-globin expression by hypoxia-inducible factor 1α

Around birth, globin expression in human red blood cells (RBCs) shifts from γ-globin to β-globin, which results in fetal haemoglobin (HbF, α₂γ₂) being gradually replaced by adult haemoglobin (HbA, α₂β₂)¹. This process has motivated the development of innovative approaches to treat sickle cell disease and β-thalassaemia by increasing HbF levels in postnatal RBCs². Here we provide therapeutically relevant insights into globin gene switching obtained through a CRISPR-Cas9 screen for ubiquitin-proteasome components that regulate HbF expression. In RBC precursors, depletion of the von Hippel-Lindau (VHL) E3 ubiquitin ligase stabilized its ubiquitination target, hypoxia-inducible factor 1α (HIF1α)^3,4, to induce γ-globin gene transcription. Mechanistically, HIF1α-HIF1β heterodimers bound cognate DNA elements in BGLT3, a long noncoding RNA gene located 2.7 kb downstream of the tandem γ-globin genes HBG1 and HBG2. This was followed by the recruitment of transcriptional activators, chromatin opening and increased long-range interactions between the γ-globin genes and their upstream enhancer. Similar induction of HbF occurred with hypoxia or with inhibition of prolyl hydroxylase domain enzymes that target HIF1α for ubiquitination by the VHL E3 ubiquitin ligase. Our findings link globin gene regulation with canonical hypoxia adaptation, provide a mechanism for HbF induction during stress erythropoiesis and suggest a new therapeutic approach for β-haemoglobinopathies.

Bioinformatics Methods for ChIP-seq Histone Analysis

The field of genomics and genome-wide analysis has exploded since around 2008 with the development of high-throughput omics approaches, largely driven by the emergence of the next-generation sequencing technologies. Among the different biological applications supported by recent sequencing technologies, ChIP-seq (Chromatin ImmunoPrecipitation followed by Sequencing) is one of the most powerful techniques which has dramatically changed our view of the epigenetics landscape of cells.In this chapter, I will present and discuss the main steps of bioinformatic and biostatistical analysis of ChIP-seq data (Fig. 1). While this technique has been widely used to study transcription factor binding sites, I will focus here on the analysis of histone modifications.

Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns

Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These algorithms take as input epigenomic datasets, such as chromatin immunoprecipitation-sequencing (ChIP-seq) measurements of histone modifications or transcription factor binding. They partition the genome and assign a label to each segment such that positions with the same label exhibit similar patterns of input data. SAGA algorithms discover categories of activity such as promoters, enhancers, or parts of genes without prior knowledge of known genomic elements. In this sense, they generally act in an unsupervised fashion like clustering algorithms, but with the additional simultaneous function of segmenting the genome. Here, we review the common methodological framework that underlies these methods, review variants of and improvements upon this basic framework, and discuss the outlook for future work. This review is intended for those interested in applying SAGA methods and for computational researchers interested in improving upon them.

Activation of γ-globin gene expression by GATA1 and NF-Y in hereditary persistence of fetal hemoglobin

Hereditary persistence of fetal hemoglobin (HPFH) ameliorates β-hemoglobinopathies by inhibiting the developmental switch from γ-globin (HBG1/HBG2) to β-globin (HBB) gene expression. Some forms of HPFH are associated with γ-globin promoter variants that either disrupt binding motifs for transcriptional repressors or create new motifs for transcriptional activators. How these variants sustain γ-globin gene expression postnatally remains undefined. We mapped γ-globin promoter sequences functionally in erythroid cells harboring different HPFH variants. Those that disrupt a BCL11A repressor binding element induce γ-globin expression by facilitating the recruitment of transcription factors NF-Y to a nearby proximal CCAAT box and GATA1 to an upstream motif. The proximal CCAAT element becomes dispensable for HPFH variants that generate new binding motifs for activators NF-Y or KLF1, but GATA1 recruitment remains essential. Our findings define distinct mechanisms through which transcription factors and their cis-regulatory elements activate γ-globin expression in different forms of HPFH, some of which are being recreated by therapeutic genome editing.

VSS: Variance-stabilized signals for sequencing-based genomic signals

MOTIVATION

A sequencing-based genomic assay such as ChIP-seq outputs a real-valued signal for each position in the genome that measures the strength of activity at that position. Most genomic signals lack the property of variance stabilization. That is, a difference between 0 and 100 reads usually has a very different statistical importance from a difference between 1,000 and 1,100 reads. A statistical model such as a negative binomial distribution can account for this pattern, but learning these models is computationally challenging. Therefore, many applications - including imputation and segmentation and genome annotation (SAGA) - instead use Gaussian models and use a transformation such as log or inverse hyperbolic sine (asinh) to stabilize variance.

RESULTS

We show here that existing transformations do not fully stabilize variance in genomic data sets. To solve this issue, we propose VSS, a method that produces variance-stabilized signals for sequencing-based genomic signals. VSS learns the empirical relationship between the mean and variance of a given signal data set and produces transformed signals that normalize for this dependence. We show that VSS successfully stabilizes variance and that doing so improves downstream applications such as SAGA. VSS will eliminate the need for downstream methods to implement complex mean-variance relationship models, and will enable genomic signals to be easily understood by eye.

AVAILABILITY

https://github.com/faezeh-bayat/VSS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

S3V2-IDEAS: a package for normalizing, denoising and integrating epigenomic datasets across different cell types

SUMMARY

Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic datasets have been generated under different biological contexts to provide insights into regulatory processes. However, the technical noise in epigenomic datasets and the many dimensions (features) examined make it challenging to effectively extract biologically meaningful inferences from these datasets. We developed a package that reduces noise while normalizing the epigenomic data by a novel normalization method, followed by integrative dimensional reduction by learning and assigning epigenetic states. This package, called S3V2-IDEAS, can be used to identify epigenetic states for multiple features, or identify discretized signal intensity levels and a master peak list across different cell types for a single feature. We illustrate the outputs and performance of S3V2-IDEAS using 137 epigenomics datasets from the VISION project that provides ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis.

AVAILABILITY AND IMPLEMENTATION

S3V2-IDEAS pipeline is freely available as open source software released under an MIT license at: https://github.com/guanjue/S3V2_IDEAS_ESMP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Distinct properties and functions of CTCF revealed by a rapidly inducible degron system

CCCTC-binding factor (CTCF) is a conserved zinc finger transcription factor implicated in a wide range of functions, including genome organization, transcription activation, and elongation. To explore the basis for CTCF functional diversity, we coupled an auxin-induced degron system with precision nuclear run-on. Unexpectedly, oriented CTCF motifs in gene bodies are associated with transcriptional stalling in a manner independent of bound CTCF. Moreover, CTCF at different binding sites (CBSs) displays highly variable resistance to degradation. Motif sequence does not significantly predict degradation behavior, but location at chromatin boundaries and chromatin loop anchors, as well as co-occupancy with cohesin, are associated with delayed degradation. Single-molecule tracking experiments link chromatin residence time to CTCF degradation kinetics, which has ramifications regarding architectural CTCF functions. Our study highlights the heterogeneity of CBSs, uncovers properties specific to architecturally important CBSs, and provides insights into the basic processes of genome organization and transcription regulation.

An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis

Thousands of epigenomic data sets have been generated in the past decade, but it is difficult for researchers to effectively use all the data relevant to their projects. Systematic integrative analysis can help meet this need, and the VISION project was established for validated systematic integration of epigenomic data in hematopoiesis. Here, we systematically integrated extensive data recording epigenetic features and transcriptomes from many sources, including individual laboratories and consortia, to produce a comprehensive view of the regulatory landscape of differentiating hematopoietic cell types in mouse. By using IDEAS as our integrative and discriminative epigenome annotation system, we identified and assigned epigenetic states simultaneously along chromosomes and across cell types, precisely and comprehensively. Combining nuclease accessibility and epigenetic states produced a set of more than 200,000 candidate cis-regulatory elements (cCREs) that efficiently capture enhancers and promoters. The transitions in epigenetic states of these cCREs across cell types provided insights into mechanisms of regulation, including decreases in numbers of active cCREs during differentiation of most lineages, transitions from poised to active or inactive states, and shifts in nuclease accessibility of CTCF-bound elements. Regression modeling of epigenetic states at cCREs and gene expression produced a versatile resource to improve selection of cCREs potentially regulating target genes. These resources are available from our VISION website to aid research in genomics and hematopoiesis.