Features that define the best ChIP-seq peak calling algorithms

Differential Analysis of Protein-DNA Binding Using ChIP-Seq Data

Chromatin immunoprecipitation in combination with next-generation sequencing (ChIP-Seq) allows probing of protein-DNA binding in a rapid and genome-wide fashion. Herein we describe the required steps to preprocess ChIP-Seq data and to analyze the differential binding of proteins to DNA for perturbation experiments. In these experiments, different conditions are compared to find the underlying biological mechanisms caused by the stimulus or treatment. In addition, we provide a sample analysis using the steps outlined in the chapter.

PAPerFly: Partial Assembly-based Peak Finder for ab initio binding site reconstruction

BACKGROUND

The specific recognition of a DNA locus by a given transcription factor is a widely studied issue. It is generally agreed that the recognition can be influenced not only by the binding motif but by the larger context of the binding site. In this work, we present a novel heuristic algorithm that can reconstruct the unique binding sites captured in a sequencing experiment without using the reference genome.

RESULTS

We present PAPerFly, the Partial Assembly-based Peak Finder, a tool for the binding site and binding context reconstruction from the sequencing data without any prior knowledge. This tool operates without the need to know the reference genome of the respective organism. We employ algorithmic approaches that are used during genome assembly. The proposed algorithm constructs a de Bruijn graph from the sequencing data. Based on this graph, sequences and their enrichment are reconstructed using a novel heuristic algorithm. The reconstructed sequences are aligned and the peaks in the sequence enrichment are identified. Our approach was tested by processing several ChIP-seq experiments available in the ENCODE database and comparing the results of Paperfly and standard methods.

CONCLUSIONS

We show that PAPerFly, an algorithm tailored for experiment analysis without the reference genome, yields better results than an aggregation of ChIP-seq agnostic tools. Our tool is freely available at https://github.com/Caeph/paperfly/ or on Zenodo ( https://doi.org/10.5281/zenodo.7116424 ).

Rescuing biologically relevant consensus regions across replicated samples

BACKGROUND

Protein-DNA binding sites of ChIP-seq experiments are identified where the binding affinity is significant based on a given threshold. The choice of the threshold is a trade-off between conservative region identification and discarding weak, but true binding sites.

RESULTS

We rescue weak binding sites using MSPC, which efficiently exploits replicates to lower the threshold required to identify a site while keeping a low false-positive rate, and we compare it to IDR, a widely used post-processing method for identifying highly reproducible peaks across replicates. We observe several master transcription regulators (e.g., SP1 and GATA3) and HDAC2-GATA1 regulatory networks on rescued regions in K562 cell line.

CONCLUSIONS

We argue the biological relevance of weak binding sites and the information they add when rescued by MSPC. An implementation of the proposed extended MSPC methodology and the scripts to reproduce the performed analysis are freely available at https://genometric.github.io/MSPC/ ; MSPC is distributed as a command-line application and an R package available from Bioconductor ( https://doi.org/doi:10.18129/B9.bioc.rmspc ).

DamID-seq: A Genome-Wide DNA Methylation Method that Captures Both Transient and Stable TF-DNA Interactions in Plant Cells

Capturing the dynamic and transient interactions of a transcription factor (TF) with its genome-wide targets whose regulation leads to plants' adaptation to their changing environment is a major technical challenge. This is a widespread problem with biochemical methods such as chromatin immunoprecipitation-sequencing (ChIP-seq) which are biased towards capturing stable TF-target gene interactions. Herein, we describe how DNA adenine methyltransferase identification and sequencing (DamID-seq) can be used to capture both transient and stable TF-target interactions by DNA methylation. The DamID technique uses a TF protein fused to a DNA adenine methyltransferase (Dam) from E. coli. When expressed in a plant cell, the Dam-TF fusion protein will methylate adenine (A) bases near the sites of TF-DNA interactions. In this way, DamID results in a permanent, stable DNA methylation mark on TF-target gene promoters, even if the target gene is only transiently "touched" by the Dam-TF fusion protein. Here we provide a step-by-step protocol to perform DamID-seq experiments in isolated plant cells for any Dam-TF fusion protein of interest. We also provide information that will enable researchers to analyze DamID-seq data to identify TF-binding sites in the genome. Our protocol includes instructions for vector cloning of the Dam-TF fusion proteins, plant cell protoplast transfections, DamID preps, library preparation, and sequencing data analysis. The protocol outlined in this chapter is performed in Arabidopsis thaliana, however, the DamID-seq workflow developed in this guide is broadly applicable to other plants and organisms.

CisCross: A gene list enrichment analysis to predict upstream regulators in Arabidopsis thaliana

Having DNA-binding profiles for a sufficient number of genome-encoded transcription factors (TFs) opens up the perspectives for systematic evaluation of the upstream regulators for the gene lists. Plant Cistrome database, a large collection of TF binding profiles detected using the DAP-seq method, made it possible for Arabidopsis. Here we re-processed raw DAP-seq data with MACS2, the most popular peak caller that leads among other ones according to quality metrics. In the benchmarking study, we confirmed that the improved collection of TF binding profiles supported a more precise gene list enrichment procedure, and resulted in a more relevant ranking of potential upstream regulators. Moreover, we consistently recovered the TF binding profiles that were missing in the previous collection of DAP-seq peak sets. We developed the CisCross web service (https://plamorph.sysbio.ru/ciscross/) that gives more flexibility in the analysis of potential upstream TF regulators for Arabidopsis thaliana genes.

GoPeaks: histone modification peak calling for CUT&Tag

Genome-wide mapping of histone modifications is critical to understanding transcriptional regulation. CUT&Tag is a new method for profiling histone modifications, offering improved sensitivity and decreased cost compared with ChIP-seq. Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. We compare the performance of GoPeaks against commonly used peak calling algorithms to detect histone modifications that display a range of peak profiles and are frequently used in epigenetic studies. We find that GoPeaks robustly detects genome-wide histone modifications and, notably, identifies a substantial number of H3K27ac peaks with improved sensitivity compared to other standard algorithms.

Cat8 Response to Nutritional Changes and Interaction With Ehrlich Pathway Related Factors

Cat8 is an important transcription factor regulating the utilization of non-fermentative carbon sources in Saccharomyces cerevisiae. However, our previous studies found that Cat8 may play a critical role in nitrogen metabolism, but the regulatory mechanism has not been elucidated. In this study, the nuclear localization and analysis of regulatory activity showed that the Cat8 function relies on Snf1 kinase. In the fermentation with glucose or glycerol as carbon sources under phenylalanine (Phe) induction, by comparing the changes of cellular gene expression and Cat8 target gene binding profiles after Cat8 overexpression, enhanced transcription was shown among key genes involved in the Ehrlich pathway (e.g., ARO9, ARO10, and ADH2) and its upstream and downstream related factors (e.g., GAP1, AGP1, GAT1, PDR12, and ESPB6), indicating that Cat8 participated in the regulation of nitrogen metabolism. Moreover, highly active Cat8 interacts with transcriptional activator Aro80 and GATA activator Gat1 coordinately to regulate the transcription of ARO10. Altogether, our results showed that Cat8 may act as a global transcription factor in response to nutritional changes, regulating both carbon and nitrogen utilization. This provides a new insight for us to explore the regulation of cell nutrient metabolism networks in yeast.

BET Protein Inhibition Regulates Macrophage Chromatin Accessibility and Microbiota-Dependent Colitis

Introduction

In colitis, macrophage functionality is altered compared to normal homeostatic conditions. Loss of IL-10 signaling results in an inappropriate chronic inflammatory response to bacterial stimulation. It remains unknown if inhibition of bromodomain and extra-terminal domain (BET) proteins alters usage of DNA regulatory elements responsible for driving inflammatory gene expression. We determined if the BET inhibitor, (+)-JQ1, could suppress inflammatory activation of macrophages in Il10-/- mice.

Methods

We performed ATAC-seq and RNA-seq on Il10-/- bone marrow-derived macrophages (BMDMs) cultured in the presence and absence of lipopolysaccharide (LPS) with and without treatment with (+)-JQ1 and evaluated changes in chromatin accessibility and gene expression. Germ-free Il10-/- mice were treated with (+)-JQ1, colonized with fecal slurries and underwent histological and molecular evaluation 14-days post colonization.

Results

Treatment with (+)-JQ1 suppressed LPS-induced changes in chromatin at distal regulatory elements associated with inflammatory genes, particularly in regions that contain motifs for AP-1 and IRF transcription factors. This resulted in attenuation of inflammatory gene expression. Treatment with (+)-JQ1 in vivo resulted in a mild reduction in colitis severity as compared with vehicle-treated mice.

Conclusion

We identified the mechanism of action associated with a new class of compounds that may mitigate aberrant macrophage responses to bacteria in colitis.

Integration of Count Difference and Curve Similarity in Negative Regulatory Element Detection

Negative regulatory elements (NREs) down-regulate gene expression by inhibiting the activities of promoters or enhancers. The repressing activity of NREs can be measured globally by massively parallel reporter assays (MPRAs). However, most existing algorithms are designed for the statistical detection of positively enriched signals in MPRA datasets. To identify reduced signals in MPRA experiments, we designed a NRE identification program, fast-NR, by integrating the count and graphic features of sequenced reads to detect NREs using datasets generated by experiments of self-transcribing active regulatory region sequencing (STARR-seq). Fast-NR identified hundreds of silencers in human K562 cells that can be validated by independent methods.

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.

ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis

Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is a technique used to identify protein–DNA interaction sites through antibody pull-down, sequencing and analysis; with enrichment ‘peak’ calling being the most critical analytical step. Benchmarking studies have consistently shown that peak callers have distinct selectivity and specificity characteristics that are not additive and seldom completely overlap in many scenarios, even after parameter optimization. We therefore developed ChIP-AP, an integrated ChIP-seq analysis pipeline utilizing four independent peak callers, which seamlessly processes raw sequencing files to final result. This approach enables (1) better gauging of peak confidence through detection by multiple algorithms, and (2) more thoroughly surveys the binding landscape by capturing peaks not detected by individual callers. Final analysis results are then integrated into a single output table, enabling users to explore their data by applying selectivity and sensitivity thresholds that best address their biological questions, without needing any additional reprocessing. ChIP-AP therefore presents investigators with a more comprehensive coverage of the binding landscape without requiring additional wet-lab observations.

Panobinostat Effectively Increases Histone Acetylation and Alters Chromatin Accessibility Landscape in Canine Embryonic Fibroblasts but Does Not Enhance Cellular Reprogramming

Robust and reproducible protocols to efficiently reprogram adult canine cells to induced pluripotent stem cells are still elusive. Somatic cell reprogramming requires global chromatin remodeling that is finely orchestrated spatially and temporally. Histone acetylation and deacetylation are key regulators of chromatin condensation, mediated by histone acetyltransferases and histone deacetylases (HDACs), respectively. HDAC inhibitors have been used to increase histone acetylation, chromatin accessibility, and somatic cell reprogramming in human and mice cells. We hypothesized that inhibition of HDACs in canine fibroblasts would increase their reprogramming efficiency by altering the epigenomic landscape and enabling greater chromatin accessibility. We report that a combined treatment of panobinostat (LBH589) and vitamin C effectively inhibits HDAC function and increases histone acetylation in canine embryonic fibroblasts in vitro, with no significant cytotoxic effects. We further determined the effect of this treatment on global chromatin accessibility via Assay for Transposase-Accessible Chromatin using sequencing. Finally, the treatment did not induce any significant increase in cellular reprogramming efficiency. Although our data demonstrate that the unique epigenetic landscape of canine cells does not make them amenable to cellular reprogramming through the proposed treatment, it provides a rationale for a targeted, canine-specific, reprogramming approach by enhancing the expression of transcription factors such as CEBP.

Integrative analysis of epigenetics data identifies gene-specific regulatory elements

Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.

Differential ATAC-seq and ChIP-seq peak detection using ROTS

Changes in cellular chromatin states fine-tune transcriptional output and ultimately lead to phenotypic changes. Here we propose a novel application of our reproducibility-optimized test statistics (ROTS) to detect differential chromatin states (ATAC-seq) or differential chromatin modification states (ChIP-seq) between conditions. We compare the performance of ROTS to existing and widely used methods for ATAC-seq and ChIP-seq data using both synthetic and real datasets. Our results show that ROTS outperformed other commonly used methods when analyzing ATAC-seq data. ROTS also displayed the most accurate detection of small differences when modeling with synthetic data. We observed that two-step methods that require the use of a separate peak caller often more accurately called enrichment borders, whereas one-step methods without a separate peak calling step were more versatile in calling sub-peaks. The top ranked differential regions detected by the methods had marked correlation with transcriptional differences of the closest genes. Overall, our study provides evidence that ROTS is a useful addition to the available differential peak detection methods to study chromatin and performs especially well when applied to study differential chromatin states in ATAC-seq data.

Pterostilbene Changes Epigenetic Marks at Enhancer Regions of Oncogenes in Breast Cancer Cells

Epigenetic aberrations are linked to sporadic breast cancer. Interestingly, certain dietary polyphenols with anti-cancer effects, such as pterostilbene (PTS), have been shown to regulate gene expression by altering epigenetic patterns. Our group has proposed the involvement of DNA methylation and DNA methyltransferase 3B (DNMT3B) as vital players in PTS-mediated suppression of candidate oncogenes and suggested a role of enhancers as target regions. In the present study, we assess a genome-wide impact of PTS on epigenetic marks at enhancers in highly invasive MCF10CA1a breast cancer cells. Following chromatin immunoprecipitation (ChIP)-sequencing in MCF10CA1a cells treated with 7 μM PTS for 9 days, we discovered that PTS leads to increased binding of DNMT3B at enhancers of 77 genes, and 17 of those genes display an overlapping decrease in the occupancy of trimethylation at lysine 36 of histone 3 (H3K36me3), a mark of active enhancers. We selected two genes, PITPNC1 and LINC00910, and found that their enhancers are hypermethylated in response to PTS. These changes coincided with the downregulation of gene expression. Of importance, we showed that 6 out of 17 target enhancers, including PITPNC1 and LINC00910, are bound by an oncogenic transcription factor OCT1 in MCF10CA1a cells. Indeed, the six enhancers corresponded to genes with established or putative cancer-driving functions. PTS led to a decrease in OCT1 binding at those enhancers, and OCT1 depletion resulted in PITPNC1 and LINC00910 downregulation, further demonstrating a role for OCT1 in transcriptional regulation. Our findings provide novel evidence for the epigenetic regulation of enhancer regions by dietary polyphenols in breast cancer cells.

AnnoMiner is a new web-tool to integrate epigenetics, transcription factor occupancy and transcriptomics data to predict transcriptional regulators

Gene expression regulation requires precise transcriptional programs, led by transcription factors in combination with epigenetic events. Recent advances in epigenomic and transcriptomic techniques provided insight into different gene regulation mechanisms. However, to date it remains challenging to understand how combinations of transcription factors together with epigenetic events control cell-type specific gene expression. We have developed the AnnoMiner web-server, an innovative and flexible tool to annotate and integrate epigenetic, and transcription factor occupancy data. First, AnnoMiner annotates user-provided peaks with gene features. Second, AnnoMiner can integrate genome binding data from two different transcriptional regulators together with gene features. Third, AnnoMiner offers to explore the transcriptional deregulation of genes nearby, or within a specified genomic region surrounding a user-provided peak. AnnoMiner’s fourth function performs transcription factor or histone modification enrichment analysis for user-provided gene lists by utilizing hundreds of public, high-quality datasets from ENCODE for the model organisms human, mouse, Drosophila and C. elegans. Thus, AnnoMiner can predict transcriptional regulators for a studied process without the strict need for chromatin data from the same process. We compared AnnoMiner to existing tools and experimentally validated several transcriptional regulators predicted by AnnoMiner to indeed contribute to muscle morphogenesis in Drosophila. AnnoMiner is freely available at http://chimborazo.ibdm.univ-mrs.fr/AnnoMiner/.

SpikChIP: a novel computational methodology to compare multiple ChIP-seq using spike-in chromatin

In order to evaluate cell- and disease-specific changes in the interacting strength of chromatin targets, ChIP-seq signal across multiple conditions must undergo robust normalization. However, this is not possible using the standard ChIP-seq scheme, which lacks a reference for the control of biological and experimental variabilities. While several studies have recently proposed different solutions to circumvent this problem, substantial analytical differences among methodologies could hamper the experimental reproducibility and quantitative accuracy. Here, we propose a computational method to accurately compare ChIP-seq experiments, with exogenous spike-in chromatin, across samples in a genome-wide manner by using a local regression strategy (spikChIP). In contrast to the previous methodologies, spikChIP reduces the influence of sequencing noise of spike-in material during ChIP-seq normalization, while minimizes the overcorrection of non-occupied genomic regions in the experimental ChIP-seq. We demonstrate the utility of spikChIP with both histone and non-histone chromatin protein, allowing us to monitor for experimental reproducibility and the accurate ChIP-seq comparison of distinct experimental schemes. spikChIP software is available on GitHub (https://github.com/eblancoga/spikChIP).

Characterizing Genetic Regulatory Elements in Ovine Tissues

The Ovine Functional Annotation of Animal Genomes (FAANG) project, part of the broader livestock species FAANG initiative, aims to identify and characterize gene regulatory elements in domestic sheep. Regulatory element annotation is essential for identifying genetic variants that affect health and production traits in this important agricultural species, as greater than 90% of variants underlying genetic effects are estimated to lie outside of transcribed regions. Histone modifications that distinguish active or repressed chromatin states, CTCF binding, and DNA methylation were used to characterize regulatory elements in liver, spleen, and cerebellum tissues from four yearling sheep. Chromatin immunoprecipitation with sequencing (ChIP-seq) was performed for H3K4me3, H3K27ac, H3K4me1, H3K27me3, and CTCF. Nine chromatin states including active promoters, active enhancers, poised enhancers, repressed enhancers, and insulators were characterized in each tissue using ChromHMM. Whole-genome bisulfite sequencing (WGBS) was performed to determine the complement of whole-genome DNA methylation with the ChIP-seq data. Hypermethylated and hypomethylated regions were identified across tissues, and these locations were compared with chromatin states to better distinguish and validate regulatory elements in these tissues. Interestingly, chromatin states with the poised enhancer mark H3K4me1 in the spleen and cerebellum and CTCF in the liver displayed the greatest number of hypermethylated sites. Not surprisingly, active enhancers in the liver and spleen, and promoters in the cerebellum, displayed the greatest number of hypomethylated sites. Overall, chromatin states defined by histone marks and CTCF occupied approximately 22% of the genome in all three tissues. Furthermore, the liver and spleen displayed in common the greatest percent of active promoter (65%) and active enhancer (81%) states, and the liver and cerebellum displayed in common the greatest percent of poised enhancer (53%), repressed enhancer (68%), hypermethylated sites (75%), and hypomethylated sites (73%). In addition, both known and de novo CTCF-binding motifs were identified in all three tissues, with the highest number of unique motifs identified in the cerebellum. In summary, this study has identified the regulatory regions of genes in three tissues that play key roles in defining health and economically important traits and has set the precedent for the characterization of regulatory elements in ovine tissues using the Rambouillet reference genome.

A flexible ChIP-sequencing simulation toolkit

Background

A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq.

Results

We present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips.

Conclusions

ChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04097-5.

Molecular and computational approaches to map regulatory elements in 3D chromatin structure

Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects.

F-Seq2: improving the feature density based peak caller with dynamic statistics

Genomic and epigenomic features are captured at a genome-wide level by using high-throughput sequencing (HTS) technologies. Peak calling delineates features identified in HTS experiments, such as open chromatin regions and transcription factor binding sites, by comparing the observed read distributions to a random expectation. Since its introduction, F-Seq has been widely used and shown to be the most sensitive and accurate peak caller for DNase I hypersensitive site (DNase-seq) data. However, the first release (F-Seq1) has two key limitations: lack of support for user-input control datasets, and poor test statistic reporting. These constrain its ability to capture systematic and experimental biases inherent to the background distributions in peak prediction, and to subsequently rank predicted peaks by confidence. To address these limitations, we present F-Seq2, which combines kernel density estimation and a dynamic 'continuous' Poisson test to account for local biases and accurately rank candidate peaks. The output of F-Seq2 is suitable for irreproducible discovery rate analysis as test statistics are calculated for individual candidate summits, allowing direct comparison of predictions across replicates. These improvements significantly boost the performance of F-Seq2 for ATAC-seq and ChIP-seq datasets, outperforming competing peak callers used by the ENCODE Consortium in terms of precision and recall.

WACS: improving ChIP-seq peak calling by optimally weighting controls

Background

Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq), initially introduced more than a decade ago, is widely used by the scientific community to detect protein/DNA binding and histone modifications across the genome. Every experiment is prone to noise and bias, and ChIP-seq experiments are no exception. To alleviate bias, the incorporation of control datasets in ChIP-seq analysis is an essential step. The controls are used to account for the background signal, while the remainder of the ChIP-seq signal captures true binding or histone modification. However, a recurrent issue is different types of bias in different ChIP-seq experiments. Depending on which controls are used, different aspects of ChIP-seq bias are better or worse accounted for, and peak calling can produce different results for the same ChIP-seq experiment. Consequently, generating “smart” controls, which model the non-signal effect for a specific ChIP-seq experiment, could enhance contrast and increase the reliability and reproducibility of the results.

Result

We propose a peak calling algorithm, Weighted Analysis of ChIP-seq (WACS), which is an extension of the well-known peak caller MACS2. There are two main steps in WACS: First, weights are estimated for each control using non-negative least squares regression. The goal is to customize controls to model the noise distribution for each ChIP-seq experiment. This is then followed by peak calling. We demonstrate that WACS significantly outperforms MACS2 and AIControl, another recent algorithm for generating smart controls, in the detection of enriched regions along the genome, in terms of motif enrichment and reproducibility analyses.

Conclusions

This ultimately improves our understanding of ChIP-seq controls and their biases, and shows that WACS results in a better approximation of the noise distribution in controls.

Epigenetic changes in human model KMT2A leukemias highlight early events during leukemogenesis

Chromosomal translocations involving the KMT2A gene are among the most common genetic alterations found in pediatric acute myeloid leukemias although the molecular mechanisms that initiate the disease remain incompletely defined. To elucidate these initiating events we used a human model system of acute myeloid leukemia driven by the KMT2A-MLLT3 (KM3) fusion. More specifically, we investigated changes in DNA methylation, histone modifications, and chromatin accessibility at each stage of our model system and correlated these with expression changes. We observed the development of a pronounced hypomethyl - ation phenotype in the early stages of leukemic transformation after KM3 addition along with loss of expression of stem-cell-associated genes and skewed expression of other genes, such as S100A8/9, implicated in leukemogenesis. In addition, early increases in the expression of the lysine demethylase KDM4B was functionally linked to these expression changes as well as other key transcription factors. Remarkably, our ATAC-sequencing data showed that there were relatively few leukemia-specific changes and that the vast majority corresponded to open chromatin regions and transcription factor clusters previously observed in other cell types. Integration of the gene expression and epigenetic changes revealed that the adenylate cyclase gene ADCY9 is an essential gene in KM3-acute myeloid leukemia, and suggested the potential for autocrine signaling through the chemokine receptor CCR1 and CCL23 ligand. Collectively, our results suggest that KM3 induces subtle changes in the epigenome while co-opting the normal transcriptional machinery to drive leukemogenesis.

Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation

Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.

scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data

Single-cell chromatin accessibility sequencing has become a powerful technology for understanding epigenetic heterogeneity of complex tissues. However, there is a lack of open-source software for comprehensive processing, analysis, and visualization of such data generated using all existing experimental protocols. Here, we present scATAC-pro for quality assessment, analysis, and visualization of single-cell chromatin accessibility sequencing data. scATAC-pro computes a range of quality control metrics for several key steps of experimental protocols, with a flexible choice of methods. It generates summary reports for both quality assessment and downstream analysis. scATAC-pro is available at https://github.com/tanlabcode/scATAC-pro.

Considerations in the analysis of plant chromatin accessibility data

Transcriptional control is exerted primarily through the binding of transcription factor proteins to regulatory elements in DNA. By virtue of eukaryotic DNA being complexed with histones, transcription factor binding to DNA alters or eliminates histone-DNA contacts, leading to increased accessibility of the DNA region to nuclease enzymes. This hypersensitivity to nuclease digestion has been used to define DNA binding events and regulatory elements across genomes, and to compare these attributes between cell types or conditions. These approaches make it possible to define the regulatory elements in a genome as well as to predict the regulatory networks of transcription factors and their target genes in a given cell state. As these chromatin accessibility assays are increasingly used, it is important to consider how to analyze the resulting data to avoid artifactual results or misinterpretation. In this review, we focus on some of the key technical and computational caveats associated with plant chromatin accessibility data, including strategies for sample preparation, sequencing, read mapping, and downstream analyses.

Integrating Peak Colocalization and Motif Enrichment Analysis for the Discovery of Genome-Wide Regulatory Modules and Transcription Factor Recruitment Rules

Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq) has opened new avenues of research in the genome-wide characterization of regulatory DNA-protein interactions at the genetic and epigenetic level. As a consequence, it has become the de facto standard for studies on the regulation of transcription, and literally thousands of data sets for transcription factors and cofactors in different conditions and species are now available to the scientific community. However, while pipelines and best practices have been established for the analysis of a single experiment, there is still no consensus on the best way to perform an integrated analysis of multiple datasets in the same condition, in order to identify the most relevant and widespread regulatory modules composed by different transcription factors and cofactors. We present here a computational pipeline for this task, that integrates peak summit colocalization, a novel statistical framework for the evaluation of its significance, and motif enrichment analysis. We show examples of its application to ENCODE data, that led to the identification of relevant regulatory modules composed of different factors, as well as the organization on DNA of the binding motifs responsible for their recruitment.

From reads to insight: a hitchhiker's guide to ATAC-seq data analysis

Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) is widely used in studying chromatin biology, but a comprehensive review of the analysis tools has not been completed yet. Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). We also review the reconstruction of transcriptional regulatory networks with multiomics data and highlight the current challenges of each step. Finally, we describe the potential of single-cell ATAC-seq and highlight the necessity of developing ATAC-seq specific analysis tools to obtain biologically meaningful insights.

A distal enhancer maintaining Hoxa1 expression orchestrates retinoic acid-induced early ESCs differentiation

Retinoic acid (RA) induces rapid differentiation of embryonic stem cells (ESCs), partly by activating expression of the transcription factor Hoxa1, which regulates downstream target genes that promote ESCs differentiation. However, mechanisms of RA-induced Hoxa1 expression and ESCs early differentiation remain largely unknown. Here, we identify a distal enhancer interacting with the Hoxa1 locus through a long-range chromatin loop. Enhancer deletion significantly inhibited expression of RA-induced Hoxa1 and endoderm master control genes such as Gata4 and Gata6. Transcriptome analysis revealed that RA-induced early ESCs differentiation was blocked in Hoxa1 enhancer knockout cells, suggesting a requirement for the enhancer. Restoration of Hoxa1 expression partly rescued expression levels of ∼40% of genes whose expression changed following enhancer deletion, and ∼18% of promoters of those rescued genes were directly bound by Hoxa1. Our data show that a distal enhancer maintains Hoxa1 expression through long-range chromatin loop and that Hoxa1 directly regulates downstream target genes expression and then orchestrates RA-induced early differentiation of ESCs. This discovery reveals mechanisms of a novel enhancer regulating RA-induced Hoxa genes expression and early ESCs differentiation.

A comprehensive resource for retrieving, visualizing, and integrating functional genomics data

This study presents qcGenomics, a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multi-dimensional data integration.

The enormous amount of freely accessible functional genomics data is an invaluable resource for interrogating the biological function of multiple DNA-interacting players and chromatin modifications by large-scale comparative analyses. However, in practice, interrogating large collections of public data requires major efforts for (i) reprocessing available raw reads, (ii) incorporating quality assessments to exclude artefactual and low-quality data, and (iii) processing data by using high-performance computation. Here, we present qcGenomics, a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multidimensional data integration.

AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification

ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'target' dataset. We introduce the AIControl framework, which eliminates the need to obtain a control dataset and instead identifies binding peaks by estimating the distributions of background signals from many publicly available control ChIP-seq datasets. We thereby avoid the cost of running control experiments while simultaneously increasing the accuracy of binding location identification. Specifically, AIControl can (i) estimate background signals at fine resolution, (ii) systematically weigh the most appropriate control datasets in a data-driven way, (iii) capture sources of potential biases that may be missed by one control dataset and (iv) remove the need for costly and time-consuming control experiments. We applied AIControl to 410 IP datasets in the ENCODE ChIP-seq database, using 440 control datasets from 107 cell types to impute background signal. Without using matched control datasets, AIControl identified peaks that were more enriched for putative binding sites than those identified by other popular peak callers that used a matched control dataset. We also demonstrated that our framework identifies binding sites that recover documented protein interactions more accurately.

Genome-wide histone modification profiling of inner cell mass and trophectoderm of bovine blastocysts by RAT-ChIP

Chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq) has revolutionized our understanding of chromatin-related biological processes. The method, however, requires thousands of cells and has therefore limited applications in situations where cell numbers are limited. Here we describe a novel method called Restriction Assisted Tagmentation Chromatin Immunoprecipitation (RAT-ChIP) that enables global histone modification profiling from as few as 100 cells. The method is simple, cost-effective and takes a single day to complete. We demonstrate the sensitivity of the method by deriving the first genome-wide maps of histone H3K4me3 and H3K27me3 modifications of inner cell mass and trophectoderm of bovine blastocyst stage embryos.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

EpiSAFARI: sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

MOTIVATION

Functional genomics experiments generate genomewide signal profiles that are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinctive patterns as they fluctuate along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in assays such as ChIP Sequencing and bisulfite sequencing. The genomic positions of valleys pinpoint locations of cis-regulatory elements such as enhancers and insulators. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements. Nevertheless, the valleys are not reported by majority of the analysis pipelines.

RESULTS

We describe EpiSAFARI, a computational method for sensitive detection of valleys from diverse types of epigenetic profiles. EpiSAFARI employs a novel smoothing method for decreasing noise in signal profiles and accounts for technical factors such as sparse signals, mappability and nucleotide content. In performance comparisons, EpiSAFARI performs favorably in terms of accuracy. The histone modification valleys detected by EpiSAFARI exhibit high conservation, transcription factor binding and they are enriched in nascent transcription. In addition, the large clusters of histone valleys are found to be enriched at the promoters of the developmentally associated genes. Differential histone valleys exhibit concordance with differential DNase signal at cell line specific valleys. DNA methylation valleys exhibit elevated conservation and high transcription factor binding. Specifically, we observed enriched binding of transcription factors associated with chromatin structure around methyl-valleys.

AVAILABILITY AND IMPLEMENTATION

EpiSAFARI is publicly available at https://github.com/harmancilab/EpiSAFARI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Population size estimation for quality control of ChIP-Seq datasets

Chromatin immunoprecipitation followed by sequencing, i.e. ChIP-Seq, is a widely used experimental technology for the identification of functional protein-DNA interactions. Nowadays, such databases as ENCODE, GTRD, ChIP-Atlas and ReMap systematically collect and annotate a large number of ChIP-Seq datasets. Comprehensive control of dataset quality is currently indispensable to select the most reliable data for further analysis. In addition to existing quality control metrics, we have developed two novel metrics that allow to control false positives and false negatives in ChIP-Seq datasets. For this purpose, we have adapted well-known population size estimate for determination of unknown number of genuine transcription factor binding regions. Determination of the proposed metrics was based on overlapping distinct binding sites derived from processing one ChIP-Seq experiment by different peak callers. Moreover, the metrics also can be useful for assessing quality of datasets obtained from processing distinct ChIP-Seq experiments by a given peak caller. We also have shown that these metrics appear to be useful not only for dataset selection but also for comparison of peak callers and identification of site motifs based on ChIP-Seq datasets. The developed algorithm for determination of the false positive control metric and false negative control metric for ChIP-Seq datasets was implemented as a plugin for a BioUML platform: https://ict.biouml.org/bioumlweb/chipseq_analysis.html.

Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling

BACKGROUND

CUT&RUN is an efficient epigenome profiling method that identifies sites of DNA binding protein enrichment genome-wide with high signal to noise and low sequencing requirements. Currently, the analysis of CUT&RUN data is complicated by its exceptionally low background, which renders programs designed for analysis of ChIP-seq data vulnerable to oversensitivity in identifying sites of protein binding.

RESULTS

Here we introduce Sparse Enrichment Analysis for CUT&RUN (SEACR), an analysis strategy that uses the global distribution of background signal to calibrate a simple threshold for peak calling. SEACR discriminates between true and false-positive peaks with near-perfect specificity from "gold standard" CUT&RUN datasets and efficiently identifies enriched regions for several different protein targets. We also introduce a web server ( http://seacr.fredhutch.org ) for plug-and-play analysis with SEACR that facilitates maximum accessibility across users of all skill levels.

CONCLUSIONS

SEACR is a highly selective peak caller that definitively validates the accuracy of CUT&RUN for datasets with known true negatives. Its ease of use and performance in comparison with existing peak calling strategies make it an ideal choice for analyzing CUT&RUN data.

Dynamic BAF chromatin remodeling complex subunit inclusion promotes temporally distinct gene expression programs in cardiogenesis

Chromatin remodeling complexes instruct cellular differentiation and lineage specific transcription. The BRG1/BRM-associated factor (BAF) complexes are important for several aspects of differentiation. We show that the catalytic subunit gene Brg1 has a specific role in cardiac precursors (CPs) to initiate cardiac gene expression programs and repress non-cardiac expression. Using immunopurification with mass spectrometry, we have determined the dynamic composition of BAF complexes during mammalian cardiac differentiation, identifying several cell-type specific subunits. We focused on the CP- and cardiomyocyte (CM)-enriched subunits BAF60c (SMARCD3) and BAF170 (SMARCC2). Baf60c and Baf170 co-regulate gene expression with Brg1 in CPs, and in CMs their loss results in broadly deregulated cardiac gene expression. BRG1, BAF60c and BAF170 modulate chromatin accessibility, to promote accessibility at activated genes while closing chromatin at repressed genes. BAF60c and BAF170 are required for proper BAF complex composition, and BAF170 loss leads to retention of BRG1 at CP-specific sites. Thus, dynamic interdependent BAF complex subunit assembly modulates chromatin states and thereby participates in directing temporal gene expression programs in cardiogenesis.

Recursive Motif Analyses Identify Brain Epigenetic Transcription Regulatory Modules

DNA methylation is an epigenetic modification modulating the structure of DNA molecule and the interactions with its binding proteins. Accumulating large-scale methylation data motivates the development of analytic tools to facilitate methylome data mining. One critical phenomenon associated with dynamic DNA methylation is the altered DNA binding affinity of transcription factors, which plays key roles in gene expression regulation. In this study, we conceived an algorithm to predict epigenetic regulatory modules through recursive motif analyses on differentially methylated loci. A two-step procedure was implemented to first group differentially methylated loci into clusters according to their correlations in methylation profiles and then to repeatedly identify the transcription factor binding motifs significantly enriched in each cluster. We applied this tool on methylome datasets generated for mouse brains which have a lack of DNA demethylation enzymes TET1 or TET2. Compared with wild type control, the differentially methylated CpG sites identified in TET1 knockout mouse brains differed significantly from those determined for TET2 knockout. Transcription factors with zinc finger DNA binding domains including Egr1, Zic3, and Zeb1 were predicted to be associated with TET1 mediated brain methylome programming, while Lhx family members with Homeobox domains were predicted to be associated with TET2 function. Interestingly, genomic loci from a co-methylated cluster often host motifs for transcription factors sharing the same DNA binding domains. Altogether, our study provided a systematic approach for epigenetic regulatory module identification and will help throw light on the interplay of DNA methylation and transcription factors.

Differential analysis of chromatin accessibility and histone modifications for predicting mouse developmental enhancers

Enhancers are distal cis-regulatory elements that modulate gene expression. They are depleted of nucleosomes and enriched in specific histone modifications; thus, calling DNase-seq and histone mark ChIP-seq peaks can predict enhancers. We evaluated nine peak-calling algorithms for predicting enhancers validated by transgenic mouse assays. DNase and H3K27ac peaks were consistently more predictive than H3K4me1/2/3 and H3K9ac peaks. DFilter and Hotspot2 were the best DNase peak callers, while HOMER, MUSIC, MACS2, DFilter and F-seq were the best H3K27ac peak callers. We observed that the differential DNase or H3K27ac signals between two distant tissues increased the area under the precision-recall curve (PR-AUC) of DNase peaks by 17.5-166.7% and that of H3K27ac peaks by 7.1-22.2%. We further improved this differential signal method using multiple contrast tissues. Evaluated using a blind test, the differential H3K27ac signal method substantially improved PR-AUC from 0.48 to 0.75 for predicting heart enhancers. We further validated our approach using postnatal retina and cerebral cortex enhancers identified by massively parallel reporter assays, and observed improvements for both tissues. In summary, we compared nine peak callers and devised a superior method for predicting tissue-specific mouse developmental enhancers by reranking the called peaks.

TRCMGene: A two-step referential compression method for the efficient storage of genetic data

BACKGROUND

The massive quantities of genetic data generated by high-throughput sequencing pose challenges to data storage, transmission and analyses. These problems are effectively solved through data compression, in which the size of data storage is reduced and the speed of data transmission is improved. Several options are available for compressing and storing genetic data. However, most of these options either do not provide sufficient compression rates or require a considerable length of time for decompression and loading.

RESULTS

Here, we propose TRCMGene, a lossless genetic data compression method that uses a referential compression scheme. The novel concept of two-step compression method, which builds an index structure using K-means and k-nearest neighbours, is introduced to TRCMGene. Evaluation with several real datasets revealed that the compression factor of TRCMGene ranges from 9 to 21. TRCMGene presents a good balance between compression factor and reading time. On average, the reading time of compressed data is 60% of that of uncompressed data. Thus, TRCMGene not only saves disc space but also saves file access time and speeds up data loading. These effects collectively improve genetic data storage and transmission in the current hardware environment and render system upgrades unnecessary. TRCMGene, user manual and demos could be accessed freely from https://github.com/tangyou79/TRCM. The data mentioned in this manuscript could be downloaded from: https://github.com/tangyou79/TRCM/wiki.

HiGlass: web-based visual exploration and analysis of genome interaction maps

We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at http://higlass.io and is also available as a containerized application that can be run on any platform.

Computational Methods for Assessing Chromatin Hierarchy

The hierarchical organization of chromatin is known to associate with diverse cellular functions; however, the precise mechanisms and the 3D structure remain to be determined. With recent advances in high-throughput next generation sequencing (NGS) techniques, genome-wide profiling of chromatin structures is made possible. Here, we provide a comprehensive overview of NGS-based methods for profiling "higher-order" and "primary-order" chromatin structures from both experimental and computational aspects. Experimental requirements and considerations specific for each method were highlighted. For computational analysis, we summarized a common analysis strategy for both levels of chromatin assessment, focusing on the characteristic computing steps and the tools. The recently developed single-cell level techniques based on Hi-C and ATAC-seq present great potential to reveal cell-to-cell variability in chromosome architecture. A brief discussion on these methods in terms of experimental and data analysis features is included. We also touch upon the biological relevance of chromatin organization and how the combination with other techniques uncovers the underlying mechanisms. We conclude with a summary and our prospects on necessary improvements of currently available methods in order to advance understanding of chromatin hierarchy. Our review brings together the analyses of both higher- and primary-order chromatin structures, and serves as a roadmap when choosing appropriate experimental and computational methods for assessing chromatin hierarchy.

Characteristics of functional enrichment and gene expression level of human putative transcriptional target genes

BACKGROUND

Transcriptional target genes show functional enrichment of genes. However, how many and how significantly transcriptional target genes include functional enrichments are still unclear. To address these issues, I predicted human transcriptional target genes using open chromatin regions, ChIP-seq data and DNA binding sequences of transcription factors in databases, and examined functional enrichment and gene expression level of putative transcriptional target genes.

RESULTS

Gene Ontology annotations showed four times larger numbers of functional enrichments in putative transcriptional target genes than gene expression information alone, independent of transcriptional target genes. To compare the number of functional enrichments of putative transcriptional target genes between cells or search conditions, I normalized the number of functional enrichment by calculating its ratios in the total number of transcriptional target genes. With this analysis, native putative transcriptional target genes showed the largest normalized number of functional enrichments, compared with target genes including 5-60% of randomly selected genes. The normalized number of functional enrichments was changed according to the criteria of enhancer-promoter interactions such as distance from transcriptional start sites and orientation of CTCF-binding sites. Forward-reverse orientation of CTCF-binding sites showed significantly higher normalized number of functional enrichments than the other orientations. Journal papers showed that the top five frequent functional enrichments were related to the cellular functions in the three cell types. The median expression level of transcriptional target genes changed according to the criteria of enhancer-promoter assignments (i.e. interactions) and was correlated with the changes of the normalized number of functional enrichments of transcriptional target genes.

CONCLUSIONS

Human putative transcriptional target genes showed significant functional enrichments. Functional enrichments were related to the cellular functions. The normalized number of functional enrichments of human putative transcriptional target genes changed according to the criteria of enhancer-promoter assignments and correlated with the median expression level of the target genes. These analyses and characters of human putative transcriptional target genes would be useful to examine the criteria of enhancer-promoter assignments and to predict the novel mechanisms and factors such as DNA binding proteins and DNA sequences of enhancer-promoter interactions.

Considerations on Experimental Design and Data Analysis of Chromatin Immunoprecipitation Experiments

Arguably one of the most valuable techniques to study chromatin organization, ChIP is the method of choice to map the contacts established between proteins and genomic DNA. Ever since its inception, more than 30 years ago, ChIP has been constantly evolving, improving, and expanding its capabilities and reach. Despite its widespread use by many laboratories across a wide variety of disciplines, ChIP assays can be sometimes challenging to design, and are often sensitive to variations in practical implementation.In this chapter, we provide a general overview of the ChIP method and its most common variations, with a special focus on ChIP-seq. We try to address some of the most important aspects that need to be taken into account in order to design and perform experiments that generate the most reproducible, high-quality data. Some of the main topics covered include the use of properly characterized antibodies, alternatives to chromatin preparation, the need for proper controls, and some recommendations about ChIP-seq data analysis.

RNA polymerase II primes Polycomb-repressed developmental genes throughout terminal neuronal differentiation

Polycomb repression in mouse embryonic stem cells (ESCs) is tightly associated with promoter co‐occupancy of RNA polymerase II (RNAPII) which is thought to prime genes for activation during early development. However, it is unknown whether RNAPII poising is a general feature of Polycomb repression, or is lost during differentiation. Here, we map the genome‐wide occupancy of RNAPII and Polycomb from pluripotent ESCs to non‐dividing functional dopaminergic neurons. We find that poised RNAPII complexes are ubiquitously present at Polycomb‐repressed genes at all stages of neuronal differentiation. We observe both loss and acquisition of RNAPII and Polycomb at specific groups of genes reflecting their silencing or activation. Strikingly, RNAPII remains poised at transcription factor genes which are silenced in neurons through Polycomb repression, and have major roles in specifying other, non‐neuronal lineages. We conclude that RNAPII poising is intrinsically associated with Polycomb repression throughout differentiation. Our work suggests that the tight interplay between RNAPII poising and Polycomb repression not only instructs promoter state transitions, but also may enable promoter plasticity in differentiated cells.

Genome-wide open chromatin regions and their effects on the regulation of silk protein genes in Bombyx mori

Nucleosome-depleted open chromatin regions (OCRs) often harbor transcription factor (TF) binding sites that are associated with active DNA regulatory elements. To investigate the regulation of silk-protein genes, DNA molecules isolated from the silk glands of third-day fifth-instar silkworm larvae and embryo-derived (BmE) cells were subjected to formal dehyde-assisted isolation of regulatory elements (FAIRE) and high-throughput sequencing. In total, 68,000 OCRs were identified, and a number of TF-binding motifs were predicted. In particular, OCRs located near silk-protein genes contained potential binding sites for functional TFs. Moreover, many TFs were found to bind to clusters of OCRs upstream of silk-protein genes, and to regulate the expression of these genes. The expression of silk protein genes may be related not only to regulating TFs (such as fkh, Bmdimm, and Bmsage), but also to developmental and hormone-induced TFs (such as zen, eve, Br, and eip74ef). Elucidation of genome-wide OCRs and their regulatory motifs in silk protein genes will provide valuable data and clues for characterizing the mechanisms of transcriptional control of silk protein genes.

Targeted Degradation of CTCF Decouples Local Insulation of Chromosome Domains from Genomic Compartmentalization

The molecular mechanisms underlying folding of mammalian chromosomes remain poorly understood. The transcription factor CTCF is a candidate regulator of chromosomal structure. Using the auxin-inducible degron system in mouse embryonic stem cells, we show that CTCF is absolutely and dose-dependently required for looping between CTCF target sites and insulation of topologically associating domains (TADs). Restoring CTCF reinstates proper architecture on altered chromosomes, indicating a powerful instructive function for CTCF in chromatin folding. CTCF remains essential for TAD organization in non-dividing cells. Surprisingly, active and inactive genome compartments remain properly segregated upon CTCF depletion, revealing that compartmentalization of mammalian chromosomes emerges independently of proper insulation of TADs. Furthermore, our data support that CTCF mediates transcriptional insulator function through enhancer blocking but not as a direct barrier to heterochromatin spreading. Beyond defining the functions of CTCF in chromosome folding, these results provide new fundamental insights into the rules governing mammalian genome organization.

Assay for Transposase-Accessible Chromatin Using Sequencing (ATAC-seq) Data Analysis

The study of epigenetic properties of the human genome, including structural modifications of DNA and chromatin, has increased tremendously as mounting evidence has demonstrated how much epigenetics affects human gene expression. Buenrostro et al. have developed a rapid method, requiring low numbers of living cells as input, for examining chromatin accessibility across the epigenome, known as the assay for transposase-accessible chromatin using sequencing (ATAC-seq). The overall goal of this unit is to provide a thorough ATAC-seq data analysis plan, as well as describe how primary human blood samples can be processed for use in ATAC-seq. In addition, a number of quality control parameters are discussed to ensure the integrity and confidence in the ATAC-seq data. © 2017 by John Wiley & Sons, Inc.

Genome-wide recruitment profiling of transcription factor Crz1 in response to high pH stress

Background

Exposure of the budding Saccharomyces cerevisiae to an alkaline environment produces a robust transcriptional response involving hundreds of genes. Part of this response is triggered by an almost immediate burst of calcium that activates the Ser/Thr protein phosphatase calcineurin. Activated calcineurin dephosphorylates the transcription factor (TF) Crz1, which moves to the nucleus and binds to calcineurin/Crz1 responsive gene promoters. In this work we present a genome-wide study of the binding of Crz1 to gene promoters in response to high pH stress.

Results

Environmental alkalinization promoted a time-dependent recruitment of Crz1 to 152 intergenic regions, the vast majority between 1 and 5 min upon stress onset. Positional evaluation of the genomic coordinates combined with existing transcriptional studies allowed identifying 140 genes likely responsive to Crz1 regulation. Gene Ontology analysis confirmed the relevant impact of calcineurin/Crz1 on a set of genes involved in glucose utilization, and uncovered novel targets, such as genes responsible for trehalose metabolism. We also identified over a dozen of genes encoding TFs that are likely under the control of Crz1, suggesting a possible mechanism for amplification of the signal at the transcription level. Further analysis of the binding sites allowed refining the consensus sequence for Crz1 binding to gene promoters and the effect of chromatin accessibility in the timing of Crz1 recruitment to promoters.

Conclusions

The present work defines at the genomic-wide level the kinetics of binding of Crz1 to gene promoters in response to alkaline stress, confirms diverse previously known Crz1 targets and identifies many putative novel ones. Because of the relevance of calcineurin/Crz1 in signaling diverse stress conditions, our data will contribute to understand the transcriptional response in other circumstances that also involve calcium signaling, such as exposition to sexual pheromones or saline stress.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3006-6) contains supplementary material, which is available to authorized users.