Development of an antibody control system using phage display.

While chromatin immunoprecipitation has become a widely-used method in the field of transcription regulation studies, serious limitations connected to the complexity and relatively little standardization of the method serve as obstacles for its use in clinical research. In this paper we introduce a method for developing bacteriophage-based controls for the better standardization of the chromatin immunoprecipitation reactions. Random phage display libraries were selected with ChIP-grade antibodies for several rounds and individual monoclonal phages were isolated. These monoclonal phages can be propagated, characterized, capillary sequenced and if needed later cloned from in-silico data. Using such control tools allows for a better characterization of the immunoprecipitation stage needed for further clinical research in the field of chromatin-immunoprecipitation-based studies.

Parallel factor ChIP provides essential internal control for quantitative differential ChIP-seq.

A key challenge in quantitative ChIP combined with high-throughput sequencing (ChIP-seq) is the normalization of data in the presence of genome-wide changes in occupancy. Analysis-based normalization methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor (TF) binding, these assumptions do not hold true. The challenges in normalization are confounded by experimental variability during sample preparation, processing and recovery. We present a novel normalization strategy utilizing an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalization. We compare our approach to normalization by total read depth and two alternative methods that utilize external experimental controls to study TF binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in patient-derived xenographs. This is supported by an adaptable pipeline to normalize and quantify differential TF binding genome-wide and generate metrics for differential binding at individual sites.

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.

Statistical Analysis and Quality Assessment of ChIP-seq Data with DROMPA.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis can detect protein/DNA-binding and histone-modification sites across an entire genome. As there are various factors during sample preparation that affect the obtained results, multilateral quality assessments are essential. Here, we describe a step-by-step protocol using DROMPA, a program for user-friendly ChIP-seq pipelining. DROMPA can be used for quality assessment, data normalization, visualization, peak calling, and multiple statistical analyses.

Data exploration, quality control and statistical analysis of ChIP-exo/nexus experiments.

ChIP-exo/nexus experiments rely on innovative modifications of the commonly used ChIP-seq protocol for high resolution mapping of transcription factor binding sites. Although many aspects of the ChIP-exo data analysis are similar to those of ChIP-seq, these high throughput experiments pose a number of unique quality control and analysis challenges. We develop a novel statistical quality control pipeline and accompanying R/Bioconductor package, ChIPexoQual, to enable exploration and analysis of ChIP-exo and related experiments. ChIPexoQual evaluates a number of key issues including strand imbalance, library complexity, and signal enrichment of data. Assessment of these features are facilitated through diagnostic plots and summary statistics computed over regions of the genome with varying levels of coverage. We evaluated our QC pipeline with both large collections of public ChIP-exo/nexus data and multiple, new ChIP-exo datasets from Escherichia coli. ChIPexoQual analysis of these datasets resulted in guidelines for using these QC metrics across a wide range of sequencing depths and provided further insights for modelling ChIP-exo data.

Comparative analysis of osteoblast gene expression profiles and Runx2 genomic occupancy of mouse and human osteoblasts in vitro.

Fast progress of the next generation sequencing (NGS) technology has allowed global transcriptional profiling and genome-wide mapping of transcription factor binding sites in various cellular contexts. However, limited number of replicates and high amount of data processing may weaken the significance of the findings. Comparative analyses of independent data sets acquired in the different laboratories would greatly increase the validity of the data. Runx2 is the key transcription factor regulating osteoblast differentiation and bone formation. We performed a comparative analysis of three published Runx2 data sets of chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) analysis in osteoblasts from mouse and human origin. Moreover, we assessed the similarity of the corresponding transcription data of these studies available online. The ChIP-seq data analysis confirmed general features of Runx2 binding, including location at genic vs intergenic regions and abundant Runx2 binding on promoters of the highly expressed genes. We also found high frequency of Runx2 DNA binding without a consensus Runx2 motif at the binding site. Importantly, mouse and human Runx2 showed moderately similar binding patterns in terms of peak-associated closest genes and their associated genomic ontology (GO) pathways. Accordingly, the gene expression profiles were highly similar and osteoblastic phenotype was prominent in the differentiated stage in both species. In conclusion, ChIP-seq method shows good reproducibility in the context of mature osteoblasts, and mouse and human osteoblast models resemble each other closely in Runx2 binding and in gene expression profiles, supporting the use of these models as adequate tools in studying osteoblast differentiation.

Genome-wide analysis of chromatin structures in Trypanosoma brucei using high-resolution MNase-ChIP-seq.

Specific DNA-protein interactions are the basis for many important cellular mechanisms like the regulation of gene expression or replication. Knowledge about the precise genomic locations of DNA-protein interactions is important because it provides insight into the regulation of these processes. Recently, we have adapted an approach that combines micrococcal nuclease (MNase) digestion of chromatin with chromatin immunoprecipitation in Trypanosoma brucei. Here, we describe in detail how this method can be used to map the genome-wide distribution of nucleosomes or other DNA-binding proteins at high resolution in T. brucei.

Sierra platinum: a fast and robust peak-caller for replicated ChIP-seq experiments with visual quality-control and -steering.

BACKGROUND: Histone modifications play an important role in gene regulation. Their genomic locations are of great interest. Usually, the location is measured by ChIP-seq and analyzed with a peak-caller. Replicated ChIP-seq experiments become more and more available. However, their analysis is based on single-experiment peak-calling or on tools like PePr which allows peak-calling of replicates but whose underlying model might not be suitable for the conditions under which the experiments are performed. RESULTS: We propose a new peak-caller called 'Sierra Platinum' that allows peak-calling of replicated ChIP-seq experiments. Moreover, it provides a variety of quality measures together with integrated visualizations supporting the assessment of the replicates and the resulting peaks, as well as steering the peak-calling process. CONCLUSION: We show that Sierra Platinum outperforms currently available methods using a newly generated benchmark data set and using real data from the NIH Roadmap Epigenomics Project. It is robust against noisy replicates.

The Commercial Antibodies Widely Used to Measure H3 K56 Acetylation Are Non-Specific in Human and Drosophila Cells.

Much of our understanding of the function of histone post-translational modifications in metazoans is inferred from their genomic localization and / or extrapolated from yeast studies. For example, acetylation of histone H3 lysine 56 (H3 K56Ac) is assumed to be important for transcriptional regulation in metazoan cells based on its occurrence at promoters and its function in yeast. Here we directly assess the function of H3 K56Ac during chromatin disassembly from gene regulatory regions during transcriptional induction in human cells by using mutations that either mimic or prevent H3 K56Ac. Although there is rapid histone H3 disassembly during induction of some estrogen receptor responsive genes, depletion of the histone chaperone ASF1A/B, which is required for H3 K56 acetylation, has no effect on chromatin disassembly at these regions. During the course of this work, we found that all the commercially available antibodies to H3 K56Ac are non-specific in human cells and in Drosophila. We used H3-YFP fusions to show that the H3 K56Q mutation can promote chromatin disassembly from regulatory regions of some estrogen responsive genes in the context of transcriptional induction. However, neither the H3 K56R nor K56Q mutation significantly altered chromatin disassembly dynamics by FRAP analysis. These results indicate that unlike the situation in yeast, human cells do not use H3 K56Ac to promote chromatin disassembly from regulatory regions or from the genome in general. Furthermore, our work highlights the need for rigorous characterization of the specificity of antibodies to histone post-translational modifications in vivo.

Biological chromodynamics: a general method for measuring protein occupancy across the genome by calibrating ChIP-seq.

Sequencing DNA fragments associated with proteins following in vivo cross-linking with formaldehyde (known as ChIP-seq) has been used extensively to describe the distribution of proteins across genomes. It is not widely appreciated that this method merely estimates a protein's distribution and cannot reveal changes in occupancy between samples. To do this, we tagged with the same epitope orthologous proteins in Saccharomyces cerevisiae and Candida glabrata, whose sequences have diverged to a degree that most DNA fragments longer than 50 bp are unique to just one species. By mixing defined numbers of C. glabrata cells (the calibration genome) with S. cerevisiae samples (the experimental genomes) prior to chromatin fragmentation and immunoprecipitation, it is possible to derive a quantitative measure of occupancy (the occupancy ratio - OR) that enables a comparison of occupancies not only within but also between genomes. We demonstrate for the first time that this 'internal standard' calibration method satisfies the sine qua non for quantifying ChIP-seq profiles, namely linearity over a wide range. Crucially, by employing functional tagged proteins, our calibration process describes a method that distinguishes genuine association within ChIP-seq profiles from background noise. Our method is applicable to any protein, not merely highly conserved ones, and obviates the need for the time consuming, expensive, and technically demanding quantification of ChIP using qPCR, which can only be performed on individual loci. As we demonstrate for the first time in this paper, calibrated ChIP-seq represents a major step towards documenting the quantitative distributions of proteins along chromosomes in different cell states, which we term biological chromodynamics.

Calibrating ChIP-Seq with Nucleosomal Internal Standards to Measure Histone Modification Density Genome Wide.

Chromatin immunoprecipitation (ChIP) serves as a central experimental technique in epigenetics research, yet there are serious drawbacks: it is a relative measurement, which untethered to any external scale obscures fair comparison among experiments; it employs antibody reagents that have differing affinities and specificities for target epitopes that vary in abundance; and it is frequently not reproducible. To address these problems, we developed Internal Standard Calibrated ChIP (ICeChIP), wherein a native chromatin sample is spiked with nucleosomes reconstituted from recombinant and semisynthetic histones on barcoded DNA prior to immunoprecipitation. ICeChIP measures local histone modification densities on a biologically meaningful scale, enabling unbiased trans-experimental comparisons, and reveals unique insight into the nature of bivalent domains. This technology provides in situ assessment of the immunoprecipitation step, accommodating for many experimental pitfalls as well as providing a critical examination of untested assumptions inherent to conventional ChIP.

Quantitative ChIP-Seq normalization reveals global modulation of the epigenome.

Epigenomic profiling by chromatin immunoprecipitation coupled with massively parallel DNA sequencing (ChIP-seq) is a prevailing methodology used to investigate chromatin-based regulation in biological systems such as human disease, but the lack of an empirical methodology to enable normalization among experiments has limited the precision and usefulness of this technique. Here, we describe a method called ChIP with reference exogenous genome (ChIP-Rx) that allows one to perform genome-wide quantitative comparisons of histone modification status across cell populations using defined quantities of a reference epigenome. ChIP-Rx enables the discovery and quantification of dynamic epigenomic profiles across mammalian cells that would otherwise remain hidden using traditional normalization methods. We demonstrate the utility of this method for measuring epigenomic changes following chemical perturbations and show how reference normalization of ChIP-seq experiments enables the discovery of disease-relevant changes in histone modification occupancy.

Quantifying ChIP-seq data: a spiking method providing an internal reference for sample-to-sample normalization.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to determine, within entire genomes, the occupancy sites of any protein of interest, including, for example, transcription factors, RNA polymerases, or histones with or without various modifications. In addition to allowing the determination of occupancy sites within one cell type and under one condition, this method allows, in principle, the establishment and comparison of occupancy maps in various cell types, tissues, and conditions. Such comparisons require, however, that samples be normalized. Widely used normalization methods that include a quantile normalization step perform well when factor occupancy varies at a subset of sites, but may miss uniform genome-wide increases or decreases in site occupancy. We describe a spike adjustment procedure (SAP) that, unlike commonly used normalization methods intervening at the analysis stage, entails an experimental step prior to immunoprecipitation. A constant, low amount from a single batch of chromatin of a foreign genome is added to the experimental chromatin. This "spike" chromatin then serves as an internal control to which the experimental signals can be adjusted. We show that the method improves similarity between replicates and reveals biological differences including global and largely uniform changes.

Large-scale quality analysis of published ChIP-seq data.

ChIP-seq has become the primary method for identifying in vivo protein-DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique appearing in PubMed as of December 2012. Individually and in aggregate, these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia of DNA Elements (ENCODE) project developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here, we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1, 2012. The majority (55%) of datasets scored as being highly successful, but a substantial minority (20%) were of apparently poor quality, and another ∼25% were of intermediate quality. We discuss how different uses of ChIP-seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e., no immunoprecipitation and mock immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high-quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.

Joint modeling of ChIP-seq data via a Markov random field model.

Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first-order Markov dependence and, for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modeling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared with existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies.

Hitchhiker antigens: inconsistent ChIP results, questionable immunohistology data, and poor antibody performance may have a common factor.

Questionable data and poor antibody performance may have a common factor: antigens "hitchhiking" on the very antibodies designed to target them. Here I focus on histone hitchhikers and their antibodies, given the impact of chromatin immunoprecipitation on our understanding of DNA regulation. Caused by a lack of stringency during antibody purification, hitchhikers will impede important advances in chromatin research and therapeutics derived from that research, if similar circumstances in the study of lupus decades ago are any guide. Evidence of this phenomenon is reviewed, purification modifications for antibody manufacturing are suggested, and a histone hitchhiker detection procedure is provided.

A quality control system for profiles obtained by ChIP sequencing.

The absence of a quality control (QC) system is a major weakness for the comparative analysis of genome-wide profiles generated by next-generation sequencing (NGS). This concerns particularly genome binding/occupancy profiling assays like chromatin immunoprecipitation (ChIP-seq) but also related enrichment-based studies like methylated DNA immunoprecipitation/methylated DNA binding domain sequencing, global run on sequencing or RNA-seq. Importantly, QC assessment may significantly improve multidimensional comparisons that have great promise for extracting information from combinatorial analyses of the global profiles established for chromatin modifications, the bindings of epigenetic and chromatin-modifying enzymes/machineries, RNA polymerases and transcription factors and total, nascent or ribosome-bound RNAs. Here we present an approach that associates global and local QC indicators to ChIP-seq data sets as well as to a variety of enrichment-based studies by NGS. This QC system was used to certify >5600 publicly available data sets, hosted in a database for data mining and comparative QC analyses.

DROMPA: easy-to-handle peak calling and visualization software for the computational analysis and validation of ChIP-seq data.

Chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) can identify genomic regions that bind proteins involved in various chromosomal functions. Although the development of next-generation sequencers offers the technology needed to identify these protein-binding sites, the analysis can be computationally challenging because sequencing data sometimes consist of >100 million reads/sample. Herein, we describe a cost-effective and time-efficient protocol that is generally applicable to ChIP-seq analysis; this protocol uses a novel peak-calling program termed DROMPA to identify peaks and an additional program, parse2wig, to preprocess read-map files. This two-step procedure drastically reduces computational time and memory requirements compared with other programs. DROMPA enables the identification of protein localization sites in repetitive sequences and efficiently identifies both broad and sharp protein localization peaks. Specifically, DROMPA outputs a protein-binding profile map in pdf or png format, which can be easily manipulated by users who have a limited background in bioinformatics.

Acquisition of high quality DNA for massive parallel sequencing by in vivo chromatin immunoprecipitation.

ChIP-seq is rapidly becoming a routine technique for the determination of the genome wide association of DNA binding proteins and histone modifications. Here we provide a protocol for the isolation, purification, and immunoprecipitation of DNA fragments associated with a target transcription factor of interest. Although the method makes use of adult mouse hearts, it can, with relative ease, be adapted for the in vivo ChIP isolation of DNA from other cell and tissue sources with the intention of massive parallel sequencing.

Peak identification for ChIP-seq data with no controls.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is increasingly being used for genome-wide profiling of transcriptional regulation, as this technique enables dissection of the gene regulatory networks. With input as control, a variety of statistical methods have been proposed for identifying the enriched regions in the genome, i.e., the transcriptional factor binding sites and chromatin modifications. However, when there are no controls, whether peak calling is still reliable awaits systematic evaluations. To address this question, we used a Bayesian framework approach to show the effectiveness of peak calling without controls (PCWC). Using several different types of ChIP-seq data, we demonstrated the relatively high accuracy of PCWC with less than a 5% false discovery rate (FDR). Compared with previously published methods, e.g., the model-based analysis of ChIP-seq (MACS), PCWC is reliable with lower FDR. Furthermore, to interpret the biological significance of the called peaks, in combination with microarray gene expression data, gene ontology annotation and subsequent motif discovery, our results indicate PCWC possesses a high efficiency. Additionally, using in silico data, only a small number of peaks were identified, suggesting the significantly low FDR for PCWC.