From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data

EZH2 inhibition stimulates repetitive element expression and viral mimicry in resting splenic B cells

Mammalian cells repress expression of repetitive genomic sequences by forming heterochromatin. However, the consequences of ectopic repeat expression remain unclear. Here we demonstrate that inhibitors of EZH2, the catalytic subunit of the Polycomb repressive complex 2 (PRC2), stimulate repeat misexpression and cell death in resting splenic B cells. B cells are uniquely sensitive to these agents because they exhibit high levels of histone H3 lysine 27 trimethylation (H3K27me3) and correspondingly low DNA methylation at repeat elements. We generated a pattern recognition receptor loss-of-function mouse model, called RIC, with mutations in Rigi (encoding for RIG-I), Ifih1 (MDA5), and Cgas. In both wildtype and RIC mutant B cells, EZH2 inhibition caused loss of H3K27me3 at repetitive elements and upregulated their expression. However, NF-κB-dependent expression of inflammatory chemokines and subsequent cell death was suppressed by the RIC mutations. We further show that inhibition of EZH2 in cancer cells requires the same pattern recognition receptors to activate an interferon response. Together, the results reveal chemokine expression induced by EZH2 inhibitors in B cells as a novel inflammatory response to genomic repeat expression. Given the overlap of genes induced by EZH2 inhibitors and Epstein-Barr virus infection, this response can be described as a form of viral mimicry.

Profiling of the Helicobacter pylori redox switch HP1021 regulon using a multi-omics approach

The gastric human pathogen Helicobacter pylori has developed mechanisms to combat stress factors, including reactive oxygen species (ROS). Here, we present a comprehensive study on the redox switch protein HP1021 regulon combining transcriptomic, proteomic and DNA-protein interactions analyses. Our results indicate that HP1021 modulates H. pylori's response to oxidative stress. HP1021 controls the transcription of 497 genes, including 407 genes related to response to oxidative stress. 79 proteins are differently expressed in the HP1021 deletion mutant. HP1021 controls typical ROS response pathways (katA, rocF) and less canonical ones, particularly DNA uptake and central carbohydrate metabolism. HP1021 is a molecular regulator of competence in H. pylori, as HP1021-dependent repression of the comB DNA uptake genes is relieved under oxidative conditions, increasing natural competence. Furthermore, HP1021 controls glucose consumption by directly regulating the gluP transporter and has an important impact on maintaining the energetic balance in the cell.

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.

Spatial rearrangement of the Streptomyces venezuelae linear chromosome during sporogenic development

Bacteria of the genus Streptomyces have a linear chromosome, with a core region and two ‘arms’. During their complex life cycle, these bacteria develop multi-genomic hyphae that differentiate into chains of exospores that carry a single copy of the genome. Sporulation-associated cell division requires chromosome segregation and compaction. Here, we show that the arms of Streptomyces venezuelae chromosomes are spatially separated at entry to sporulation, but during sporogenic cell division they are closely aligned with the core region. Arm proximity is imposed by segregation protein ParB and condensin SMC. Moreover, the chromosomal terminal regions are organized into distinct domains by the Streptomyces-specific HU-family protein HupS. Thus, as seen in eukaryotes, there is substantial chromosomal remodelling during the Streptomyces life cycle, with the chromosome undergoing rearrangements from an ‘open’ to a ‘closed’ conformation.

Streptomyces bacteria have a linear chromosome and a complex life cycle, including development of multi-genomic hyphae that differentiate into mono-genomic exospores. Here, Szafran et al. show that the chromosome of Streptomyces venezuelae undergoes substantial remodelling during sporulation, from an ‘open’ to a ‘closed’ conformation.

Cooperation between HDAC3 and DAX1 mediates lineage restriction of embryonic stem cells

Mouse embryonic stem cells (mESCs) are biased toward producing embryonic rather than extraembryonic endoderm fates. Here, we identify the mechanism of this barrier and report that the histone deacetylase Hdac3 and the transcriptional corepressor Dax1 cooperatively limit the lineage repertoire of mESCs by silencing an enhancer of the extraembryonic endoderm-specifying transcription factor Gata6. This restriction is opposed by the pluripotency transcription factors Nr5a2 and Esrrb, which promote cell type conversion. Perturbation of the barrier extends mESC potency and allows formation of 3D spheroids that mimic the spatial segregation of embryonic epiblast and extraembryonic endoderm in early embryos. Overall, this study shows that transcriptional repressors stabilize pluripotency by biasing the equilibrium between embryonic and extraembryonic lineages that is hardwired into the mESC transcriptional network.

MAnorm2 for quantitatively comparing groups of ChIP-seq samples

Eukaryotic gene transcription is regulated by a large cohort of chromatin-associated proteins, and inferring their differential binding sites between cellular contexts requires a rigorous comparison of the corresponding ChIP-seq data. We present MAnorm2, a new computational tool for quantitatively comparing groups of ChIP-seq samples. MAnorm2 uses a hierarchical strategy for normalization of ChIP-seq data and assesses within-group variability of ChIP-seq signals based on an empirical Bayes framework. In this framework, MAnorm2 allows for abundant differential ChIP-seq signals between groups of samples as well as very different global within-group variability between groups. Using a number of real ChIP-seq data sets, we observed that MAnorm2 clearly outperformed existing tools for differential ChIP-seq analysis, especially when the groups of samples being compared had distinct global within-group variability.

A high-content RNAi screen reveals multiple roles for long noncoding RNAs in cell division

Genome stability relies on proper coordination of mitosis and cytokinesis, where dynamic microtubules capture and faithfully segregate chromosomes into daughter cells. With a high-content RNAi imaging screen targeting more than 2,000 human lncRNAs, we identify numerous lncRNAs involved in key steps of cell division such as chromosome segregation, mitotic duration and cytokinesis. Here, we provide evidence that the chromatin-associated lncRNA, linc00899, leads to robust mitotic delay upon its depletion in multiple cell types. We perform transcriptome analysis of linc00899-depleted cells and identify the neuronal microtubule-binding protein, TPPP/p25, as a target of linc00899. We further show that linc00899 binds TPPP/p25 and suppresses its transcription. In cells depleted of linc00899, upregulation of TPPP/p25 alters microtubule dynamics and delays mitosis. Overall, our comprehensive screen uncovers several lncRNAs involved in genome stability and reveals a lncRNA that controls microtubule behaviour with functional implications beyond cell division.

A step-by-step guide to analyzing CAGE data using R/Bioconductor

Cap Analysis of Gene Expression (CAGE) is one of the most popular 5'-end sequencing methods. In a single experiment, CAGE can be used to locate and quantify the expression of both Transcription Start Sites (TSSs) and enhancers. This is workflow is a case study on how to use the CAGEfightR package to orchestrate analysis of CAGE data within the Bioconductor project. This workflow starts from BigWig-files and covers both basic CAGE analyses such as identifying, quantifying and annotating TSSs and enhancers, advanced analysis such as finding interacting TSS-enhancer pairs and enhancer clusters, to differential expression analysis and alternative TSS usage. R-code, discussion and references are intertwined to help provide guidelines for future CAGE studies of the same kind.

A practical guide to methods controlling false discoveries in computational biology

BACKGROUND

In high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as informative covariates to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigate the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology.

RESULTS

Methods that incorporate informative covariates are modestly more powerful than classic approaches, and do not underperform classic approaches, even when the covariate is completely uninformative. The majority of methods are successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we find that the improvement of the modern FDR methods over the classic methods increases with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses.

CONCLUSIONS

Modern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.

Unsupervised clustering and epigenetic classification of single cells

Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.

Vitamin D Receptor Signaling and Cancer

The vitamin D receptor (VDR) binds the secosteroid hormone 1,25(OH)₂D₃ with high affinity and regulates gene programs that control a serum calcium levels, as well as cell proliferation and differentiation. A significant focus has been to exploit the VDR in cancer settings. Although preclinical studies have been strongly encouraging, to date clinical trials have delivered equivocal findings that have paused the clinical translation of these compounds. However, it is entirely possible that mining of genomic data will help to refine precisely what are the key anticancer actions of vitamin D compounds and where these can be used most effectively.

Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR

Cytosine methylation is an important DNA epigenetic modification. In vertebrates, methylation occurs at CpG sites, which are dinucleotides where a cytosine is immediately followed by a guanine in the DNA sequence from 5' to 3'. When located in the promoter region of a gene, DNA methylation is often associated with transcriptional silencing of the gene. Aberrant DNA methylation is associated with the development of various diseases such as cancer. Bisulfite sequencing (BS-seq) is the current "gold-standard" technology for high-resolution profiling of DNA methylation. Reduced representation bisulfite sequencing (RRBS) is an efficient form of BS-seq that targets CpG-rich DNA regions in order to save sequencing costs. A typical bioinformatics aim is to identify CpGs that are differentially methylated (DM) between experimental conditions. This workflow demonstrates that differential methylation analysis of RRBS data can be conducted using software and methodology originally developed for RNA-seq data. The RNA-seq pipeline is adapted to methylation by adding extra columns to the design matrix to account for read coverage at each CpG, after which the RRBS and RNA-seq pipelines are almost identical. This approach is statistically natural and gives analysts access to a rich collection of analysis tools including generalized linear models, gene set testing and pathway analysis. The article presents a complete start to finish case study analysis of RRBS profiles of different cell populations from the mouse mammary gland using the Bioconductor package edgeR. We show that lineage-committed cells are typically hyper-methylated compared to progenitor cells and this is true on all the autosomes but not the sex chromosomes. We demonstrate a strong negative correlation between methylation of promoter regions and gene expression as measured by RNA-seq for the same cell types, showing that methylation is a regulatory mechanism involved in epithelial linear commitment.

Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR

Cytosine methylation is an important DNA epigenetic modification. In vertebrates, methylation occurs at CpG sites, which are dinucleotides where a cytosine is immediately followed by a guanine in the DNA sequence from 5' to 3'. When located in the promoter region of a gene, DNA methylation is often associated with transcriptional silencing of the gene. Aberrant DNA methylation is associated with the development of various diseases such as cancer. Bisulfite sequencing (BS-seq) is the current "gold-standard" technology for high-resolution profiling of DNA methylation. Reduced representation bisulfite sequencing (RRBS) is an efficient form of BS-seq that targets CpG-rich DNA regions in order to save sequencing costs. A typical bioinformatics aim is to identify CpGs that are differentially methylated (DM) between experimental conditions. This workflow demonstrates that differential methylation analysis of RRBS data can be conducted using software and methodology originally developed for RNA-seq data. The RNA-seq pipeline is adapted to methylation by adding extra columns to the design matrix to account for read coverage at each CpG, after which the RRBS and RNA-seq pipelines are almost identical. This approach is statistically natural and gives analysts access to a rich collection of analysis tools including generalized linear models, gene set testing and pathway analysis. The article presents a complete start to finish case study analysis of RRBS profiles of different cell populations from the mouse mammary gland using the Bioconductor package edgeR. We show that lineage-committed cells are typically hyper-methylated compared to progenitor cells and this is true on all the autosomes but not the sex chromosomes. We demonstrate a strong negative correlation between methylation of promoter regions and gene expression as measured by RNA-seq for the same cell types, showing that methylation is a regulatory mechanism involved in epithelial linear commitment.

ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION

One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure. The robust procedure has the effect of decreasing the informativeness of the prior distribution for outlier genes while increasing its informativeness for other genes. This effect has the double benefit of reducing the chance that hypervariable genes will be spuriously identified as DE while increasing statistical power for the main body of genes. The robust EB algorithm is fast and numerically stable. The procedure allows exact small-sample null distributions for the test statistics and reduces exactly to the original EB procedure when no outlier genes are present. Simulations show that the robustified tests have similar performance to the original tests in the absence of outlier genes but have greater power and robustness when outliers are present. The article includes case studies for which the robust method correctly identifies and downweights genes associated with hidden covariates and detects more genes likely to be scientifically relevant to the experimental conditions. The new procedure is implemented in the limma software package freely available from the Bioconductor repository.

csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify binding sites for a target protein in the genome. An important scientific application is to identify changes in protein binding between different treatment conditions, i.e. to detect differential binding. This can reveal potential mechanisms through which changes in binding may contribute to the treatment effect. The csaw package provides a framework for the de novo detection of differentially bound genomic regions. It uses a window-based strategy to summarize read counts across the genome. It exploits existing statistical software to test for significant differences in each window. Finally, it clusters windows into regions for output and controls the false discovery rate properly over all detected regions. The csaw package can handle arbitrarily complex experimental designs involving biological replicates. It can be applied to both transcription factor and histone mark datasets, and, more generally, to any type of sequencing data measuring genomic coverage. csaw performs favorably against existing methods for de novo DB analyses on both simulated and real data. csaw is implemented as a R software package and is freely available from the open-source Bioconductor project.