A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data

Inferring differential protein binding from time-series chromatin accessibility data

Motivation

Due to internal and external factors, the epigenomic landscape is constantly changing in ways that are linked to changes in gene expression. Chromatin accessibility data, such as MNase-seq, provide valuable insights into this landscape and have been used to compute chromatin occupancy profiles. Multiple datasets generated over time or under different conditions can thus be used to study dynamic changes in chromatin occupancy across the genome.

Results

Our existing model, RoboCOP, computes a genome-wide chromatin occupancy profile for nucleosomes and hundreds of transcription factors. Here, we present a new method called DynaCOP that takes multiple chromatin occupancy profiles and uses them to generate a series of nucleosome-guided difference profiles. These profiles identify differentially binding transcription factors and reveal changes in nucleosome occupancy and positioning. We apply DynaCOP to chromatin occupancy profiles derived from deeply sequenced time-series MNase-seq data to study differential chromatin occupancy in the yeast genome under cadmium stress. We find strong correlations between the observed chromatin changes and changes in transcription.

Availability and implementation

https://github.com/HarteminkLab/RoboCOP.

AI applications in functional genomics

We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.

Chromatin accessibility profiling methods

Chromatin accessibility, or the physical access to chromatinized DNA, is a widely studied characteristic of the eukaryotic genome. As active regulatory DNA elements are generally 'accessible', the genome-wide profiling of chromatin accessibility can be used to identify candidate regulatory genomic regions in a tissue or cell type. Multiple biochemical methods have been developed to profile chromatin accessibility, both in bulk and at the single-cell level. Depending on the method, enzymatic cleavage, transposition or DNA methyltransferases are used, followed by high-throughput sequencing, providing a view of genome-wide chromatin accessibility. In this Primer, we discuss these biochemical methods, as well as bioinformatics tools for analysing and interpreting the generated data, and insights into the key regulators underlying developmental, evolutionary and disease processes. We outline standards for data quality, reproducibility and deposition used by the genomics community. Although chromatin accessibility profiling is invaluable to study gene regulation, alone it provides only a partial view of this complex process. Orthogonal assays facilitate the interpretation of accessible regions with respect to enhancer-promoter proximity, functional transcription factor binding and regulatory function. We envision that technological improvements including single-molecule, multi-omics and spatial methods will bring further insight into the secrets of genome regulation.

A practical guide for DNase-seq data analysis: from data management to common applications

Deoxyribonuclease I (DNase I)-hypersensitive site sequencing (DNase-seq) has been widely used to determine chromatin accessibility and its underlying regulatory lexicon. However, exploring DNase-seq data requires sophisticated downstream bioinformatics analyses. In this study, we first review computational methods for all of the major steps in DNase-seq data analysis, including experimental design, quality control, read alignment, peak calling, annotation of cis-regulatory elements, genomic footprinting and visualization. The challenges associated with each step are highlighted. Next, we provide a practical guideline and a computational pipeline for DNase-seq data analysis by integrating some of these tools. We also discuss the competing techniques and the potential applications of this pipeline for the analysis of analogous experimental data. Finally, we discuss the integration of DNase-seq with other functional genomics techniques.

Accurate prediction of cell type-specific transcription factor binding

Prediction of cell type-specific, in vivo transcription factor binding sites is one of the central challenges in regulatory genomics. Here, we present our approach that earned a shared first rank in the "ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge" in 2017. In post-challenge analyses, we benchmark the influence of different feature sets and find that chromatin accessibility and binding motifs are sufficient to yield state-of-the-art performance. Finally, we provide 682 lists of predicted peaks for a total of 31 transcription factors in 22 primary cell types and tissues and a user-friendly version of our approach, Catchitt, for download.

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding

Motivation

Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of adjacent base pairs, etc. Several methods have been developed to jointly account for DNA sequence and shape properties in predicting TF binding affinity. However, a limitation of these methods is that they typically require a training set of aligned TF binding sites.

Results

We describe a sequence + shape kernel that leverages DNA sequence and shape information to better understand protein-DNA binding preference and affinity. This kernel extends an existing class of k-mer based sequence kernels, based on the recently described di-mismatch kernel. Using three in vitro benchmark datasets, derived from universal protein binding microarrays (uPBMs), genomic context PBMs (gcPBMs) and SELEX-seq data, we demonstrate that incorporating DNA shape information improves our ability to predict protein-DNA binding affinity. In particular, we observe that (i) the k-spectrum + shape model performs better than the classical k-spectrum kernel, particularly for small k values; (ii) the di-mismatch kernel performs better than the k-mer kernel, for larger k; and (iii) the di-mismatch + shape kernel performs better than the di-mismatch kernel for intermediate k values.

Availability and implementation

The software is available at https://bitbucket.org/wenxiu/sequence-shape.git.

Supplementary information

Supplementary data are available at Bioinformatics online.

Detecting Differential Transcription Factor Activity from ATAC-Seq Data

Transcription factors are managers of the cellular factory, and key components to many diseases. Many non-coding single nucleotide polymorphisms affect transcription factors, either by directly altering the protein or its functional activity at individual binding sites. Here we first briefly summarize high-throughput approaches to studying transcription factor activity. We then demonstrate, using published chromatin accessibility data (specifically ATAC-seq), that the genome-wide profile of TF recognition motifs relative to regions of open chromatin can determine the key transcription factor altered by a perturbation. Our method of determining which TFs are altered by a perturbation is simple, is quick to implement, and can be used when biological samples are limited. In the future, we envision that this method could be applied to determine which TFs show altered activity in response to a wide variety of drugs and diseases.

LRPPRC-mediated folding of the mitochondrial transcriptome

The expression of the compact mammalian mitochondrial genome requires transcription, RNA processing, translation and RNA decay, much like the more complex chromosomal systems, and here we use it as a model system to understand the fundamental aspects of gene expression. Here we combine RNase footprinting with PAR-CLIP at unprecedented depth to reveal the importance of RNA-protein interactions in dictating RNA folding within the mitochondrial transcriptome. We show that LRPPRC, in complex with its protein partner SLIRP, binds throughout the mitochondrial transcriptome, with a preference for mRNAs, and its loss affects the entire secondary structure and stability of the transcriptome. We demonstrate that the LRPPRC-SLIRP complex is a global RNA chaperone that stabilizes RNA structures to expose the required sites for translation, stabilization, and polyadenylation. Our findings reveal a general mechanism where extensive RNA-protein interactions ensure that RNA is accessible for its biological functions.

DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter

Motivation

Identifying the locations of transcription factor binding sites is critical for understanding how gene transcription is regulated across different cell types and conditions. Chromatin accessibility experiments such as DNaseI sequencing (DNase-seq) and Assay for Transposase Accessible Chromatin sequencing (ATAC-seq) produce genome-wide data that include distinct 'footprint' patterns at binding sites. Nearly all existing computational methods to detect footprints from these data assume that footprint signals are highly homogeneous across footprint sites. Additionally, a comprehensive and systematic comparison of footprinting methods for specifically identifying which motif sites for a specific factor are bound has not been performed.

Results

Using DNase-seq data from the ENCODE project, we show that a large degree of previously uncharacterized site-to-site variability exists in footprint signal across motif sites for a transcription factor. To model this heterogeneity in the data, we introduce a novel, supervised learning footprinter called Detecting Footprints Containing Motifs (DeFCoM). We compare DeFCoM to nine existing methods using evaluation sets from four human cell-lines and eighteen transcription factors and show that DeFCoM outperforms current methods in determining bound and unbound motif sites. We also analyze the impact of several biological and technical factors on the quality of footprint predictions to highlight important considerations when conducting footprint analyses and assessing the performance of footprint prediction methods. Finally, we show that DeFCoM can detect footprints using ATAC-seq data with similar accuracy as when using DNase-seq data.

Availability and Implementation

Python code available at https://bitbucket.org/bryancquach/defcom.

Contact

bquach@email.unc.edu or tsfurey@email.unc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling

BACKGROUND

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data.

AIM

The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data.

MATERIALS AND METHODS

This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years.

RESULTS

The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation.

CONCLUSION

Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Mining precise cause and effect rules in large time series data of socio-economic indicators

Discovery of cause–effect relationships, particularly in large databases of time-series is challenging because of continuous data of different characteristics and complex lagged relationships. In this paper, we have proposed a novel approach, to extract cause–effect relationships in large time series data set of socioeconomic indicators. The method enhances the scope of relationship discovery to cause–effect relationships by identifying multiple causal structures such as binary, transitive, many to one and cyclic. We use temporal association and temporal odds ratio to exclude noncausal association and to ensure the high reliability of discovered causal rules. We assess the method with both synthetic and real-world datasets. Our proposed method will help to build quantitative models to analyze socioeconomic processes by generating a precise cause–effect relationship between different economic indicators. The outcome shows that the proposed method can effectively discover existing causality structure in large time series databases.

c-Myb Binding Sites in Haematopoietic Chromatin Landscapes

Strict control of tissue-specific gene expression plays a pivotal role during lineage commitment. The transcription factor c-Myb has an essential role in adult haematopoiesis and functions as an oncogene when rearranged in human cancers. Here we have exploited digital genomic footprinting analysis to obtain a global picture of c-Myb occupancy in the genome of six different haematopoietic cell-types. We have biologically validated several c-Myb footprints using c-Myb knockdown data, reporter assays and DamID analysis. We show that our predicted conserved c-Myb footprints are highly dependent on the haematopoietic cell type, but that there is a group of gene targets common to all cell-types analysed. Furthermore, we find that c-Myb footprints co-localise with active histone mark H3K4me3 and are significantly enriched at exons. We analysed co-localisation of c-Myb footprints with 104 chromatin regulatory factors in K562 cells, and identified nine proteins that are enriched together with c-Myb footprints on genes positively regulated by c-Myb and one protein enriched on negatively regulated genes. Our data suggest that c-Myb is a transcription factor with multifaceted target regulation depending on cell type.

Protein-DNA binding in high-resolution

Recent advances in experimental and computational methodologies are enabling ultra-high resolution genome-wide profiles of protein-DNA binding events. For example, the ChIP-exo protocol precisely characterizes protein-DNA cross-linking patterns by combining chromatin immunoprecipitation (ChIP) with 5' → 3' exonuclease digestion. Similarly, deeply sequenced chromatin accessibility assays (e.g. DNase-seq and ATAC-seq) enable the detection of protected footprints at protein-DNA binding sites. With these techniques and others, we have the potential to characterize the individual nucleotides that interact with transcription factors, nucleosomes, RNA polymerases and other regulatory proteins in a particular cellular context. In this review, we explain the experimental assays and computational analysis methods that enable high-resolution profiling of protein-DNA binding events. We discuss the challenges and opportunities associated with such approaches.

Survey of protein-DNA interactions in Aspergillus oryzae on a genomic scale

The genome-scale delineation of in vivo protein–DNA interactions is key to understanding genome function. Only ∼5% of transcription factors (TFs) in the Aspergillus genus have been identified using traditional methods. Although the Aspergillus oryzae genome contains >600 TFs, knowledge of the in vivo genome-wide TF-binding sites (TFBSs) in aspergilli remains limited because of the lack of high-quality antibodies. We investigated the landscape of in vivo protein–DNA interactions across the A. oryzae genome through coupling the DNase I digestion of intact nuclei with massively parallel sequencing and the analysis of cleavage patterns in protein–DNA interactions at single-nucleotide resolution. The resulting map identified overrepresented de novo TF-binding motifs from genomic footprints, and provided the detailed chromatin remodeling patterns and the distribution of digital footprints near transcription start sites. The TFBSs of 19 known Aspergillus TFs were also identified based on DNase I digestion data surrounding potential binding sites in conjunction with TF binding specificity information. We observed that the cleavage patterns of TFBSs were dependent on the orientation of TF motifs and independent of strand orientation, consistent with the DNA shape features of binding motifs with flanking sequences.

Chromatin accessibility: a window into the genome

Transcriptional activation throughout the eukaryotic lineage has been tightly linked with disruption of nucleosome organization at promoters, enhancers, silencers, insulators and locus control regions due to transcription factor binding. Regulatory DNA thus coincides with open or accessible genomic sites of remodeled chromatin. Current chromatin accessibility assays are used to separate the genome by enzymatic or chemical means and isolate either the accessible or protected locations. The isolated DNA is then quantified using a next-generation sequencing platform. Wide application of these assays has recently focused on the identification of the instrumental epigenetic changes responsible for differential gene expression, cell proliferation, functional diversification and disease development. Here we discuss the limitations and advantages of current genome-wide chromatin accessibility assays with especial attention on experimental precautions and sequence data analysis. We conclude with our perspective on future improvements necessary for moving the field of chromatin profiling forward.

DNase footprint signatures are dictated by factor dynamics and DNA sequence

Genomic footprinting has emerged as an unbiased discovery method for transcription factor (TF) occupancy at cognate DNA in vivo. A basic premise of footprinting is that sequence-specific TF-DNA interactions are associated with localized resistance to nucleases, leaving observable signatures of cleavage within accessible chromatin. This phenomenon is interpreted to imply protection of the critical nucleotides by the stably bound protein factor. However, this model conflicts with previous reports of many TFs exchanging with specific binding sites in living cells on a timescale of seconds. We show that TFs with short DNA residence times have no footprints at bound motif elements. Moreover, the nuclease cleavage profile within a footprint originates from the DNA sequence in the factor-binding site, rather than from the protein occupying specific nucleotides. These findings suggest a revised understanding of TF footprinting and reveal limitations in comprehensive reconstruction of the TF regulatory network using this approach.

Identifying and mitigating bias in next-generation sequencing methods for chromatin biology

Next-generation sequencing (NGS) technologies have been used in diverse ways to investigate various aspects of chromatin biology by identifying genomic loci that are bound by transcription factors, occupied by nucleosomes or accessible to nuclease cleavage, or loci that physically interact with remote genomic loci. However, reaching sound biological conclusions from such NGS enrichment profiles requires many potential biases to be taken into account. In this Review, we discuss common ways in which biases may be introduced into NGS chromatin profiling data, approaches to diagnose these biases and analytical techniques to mitigate their effect.

Learning protein-DNA interaction landscapes by integrating experimental data through computational models

MOTIVATION

Transcriptional regulation is directly enacted by the interactions between DNA and many proteins, including transcription factors (TFs), nucleosomes and polymerases. A critical step in deciphering transcriptional regulation is to infer, and eventually predict, the precise locations of these interactions, along with their strength and frequency. While recent datasets yield great insight into these interactions, individual data sources often provide only partial information regarding one aspect of the complete interaction landscape. For example, chromatin immunoprecipitation (ChIP) reveals the binding positions of a protein, but only for one protein at a time. In contrast, nucleases like MNase and DNase can be used to reveal binding positions for many different proteins at once, but cannot easily determine the identities of those proteins. Currently, few statistical frameworks jointly model these different data sources to reveal an accurate, holistic view of the in vivo protein-DNA interaction landscape.

RESULTS

Here, we develop a novel statistical framework that integrates different sources of experimental information within a thermodynamic model of competitive binding to jointly learn a holistic view of the in vivo protein-DNA interaction landscape. We show that our framework learns an interaction landscape with increased accuracy, explaining multiple sets of data in accordance with thermodynamic principles of competitive DNA binding. The resulting model of genomic occupancy provides a precise mechanistic vantage point from which to explore the role of protein-DNA interactions in transcriptional regulation.

AVAILABILITY AND IMPLEMENTATION

The C source code for compete and Python source code for MCMC-based inference are available at http://www.cs.duke.edu/∼amink.

CONTACT

amink@cs.duke.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape

We describe protein interaction quantitation (PIQ), a computational method for modeling the magnitude and shape of genome-wide DNase I hypersensitivity profiles to identify transcription factor (TF) binding sites. Through the use of machine-learning techniques, PIQ identified binding sites for >700 TFs from one DNase I hypersensitivity analysis followed by sequencing (DNase-seq) experiment with accuracy comparable to that of chromatin immunoprecipitation followed by sequencing (ChIP-seq). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into prepancreatic and intestinal endoderm. We identified 120 and experimentally validated eight 'pioneer' TF families that dynamically open chromatin. Four pioneer TF families only opened chromatin in one direction from their motifs. Furthermore, we identified 'settler' TFs whose genomic binding is principally governed by proximity to open chromatin. Our results support a model of hierarchical TF binding in which directional and nondirectional pioneer activity shapes the chromatin landscape for population by settler TFs.

Mapping of mitochondrial RNA-protein interactions by digital RNase footprinting

Human mitochondrial DNA is transcribed as long polycistronic transcripts that encompass each strand of the genome and are processed subsequently into mature mRNAs, tRNAs, and rRNAs, necessitating widespread posttranscriptional regulation. Here, we establish methods for massively parallel sequencing and analyses of RNase-accessible regions of human mitochondrial RNA and thereby identify specific regions within mitochondrial transcripts that are bound by proteins. This approach provides a range of insights into the contribution of RNA-binding proteins to the regulation of mitochondrial gene expression.

Using DNase digestion data to accurately identify transcription factor binding sites

Identifying binding sites of transcription factors (TFs) is a key task in deciphering transcriptional regulation. ChIP-based methods are used to survey the genomic locations of a single TF in each experiment. But methods combining DNase digestion data with TF binding specificity information could potentially be used to survey the locations of many TFs in the same experiment, provided such methods permit reasonable levels of sensitivity and specificity. Here, we present a simple such method that outperforms a leading recent method, centipede, marginally in human but dramatically in yeast (average auROC across 20 TFs increases from 74% to 94%). Our method is based on logistic regression and thus benefits from supervision, but we show that partially and completely unsupervised variants perform nearly as well. Because the number of parameters in our method is at least an order of magnitude smaller than CENTIPEDE, we dub it MILLIPEDE.

Pharmacogene regulatory elements: from discovery to applications

Regulatory elements play an important role in the variability of individual responses to drug treatment. This has been established through studies on three classes of elements that regulate RNA and protein abundance: promoters, enhancers and microRNAs. Each of these elements, and genetic variants within them, are being characterized at an exponential pace by next-generation sequencing (NGS) technologies. In this review, we outline examples of how each class of element affects drug response via regulation of drug targets, transporters and enzymes. We also discuss the impact of NGS technologies such as chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq), and the ramifications of new techniques such as high-throughput chromosome capture (Hi-C), chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and massively parallel reporter assays (MPRA). NGS approaches are generating data faster than they can be analyzed, and new methods will be required to prioritize laboratory results before they are ready for the clinic. However, there is no doubt that these approaches will bring about a systems-level understanding of the interplay between genetic variants and drug response. An understanding of the importance of regulatory variants in pharmacogenomics will facilitate the identification of responders versus non-responders, the prevention of adverse effects and the optimization of therapies for individual patients.

Identification of key mechanisms controlling gene expression in Leishmania infected macrophages using genome-wide promoter analysis

The present study describes the in silico prediction of the regulatory network of Leishmania infected human macrophages. The construction of the gene regulatory network requires the identification of Transcription Factor Binding Sites (TFBSs) in the regulatory regions (promoters, enhancers) of genes that are regulated upon Leishmania infection. The promoters of human, mouse, rat, dog and chimpanzee genes were first identified in the whole genomes using available experimental data on full length cDNA sequences or deep CAGE tag data (DBTSS, FANTOM3, FANTOM4), mRNA models (ENSEMBL), or using hand annotated data (EPD, TRANSFAC). A phylogenetic footprinting analysis and a MATCH analysis of the promoter sequences were then performed to predict TFBS. Then, an SQL database that integrates all results of promoter analysis as well as other genome annotation information obtained from ENSEMBL, TRANSFAC, TRED (Transcription Regulatory Element Database), ORegAnno and the ENCODE project, was established. Finally publicly available expression data from human Leishmania infected macrophages were analyzed using the genome-wide information on predicted TFBS with the computer system ExPlain™. The gene regulatory network was constructed and activated signal transduction pathways were revealed. The Irak1 pathway was identified as a key pathway regulating gene expression changes in Leishmania infected macrophages.

Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data

Accurate functional annotation of regulatory elements is essential for understanding global gene regulation. Here, we report a genome-wide map of 827,000 transcription factor binding sites in human lymphoblastoid cell lines, which is comprised of sites corresponding to 239 position weight matrices of known transcription factor binding motifs, and 49 novel sequence motifs. To generate this map, we developed a probabilistic framework that integrates cell- or tissue-specific experimental data such as histone modifications and DNase I cleavage patterns with genomic information such as gene annotation and evolutionary conservation. Comparison to empirical ChIP-seq data suggests that our method is highly accurate yet has the advantage of targeting many factors in a single assay. We anticipate that this approach will be a valuable tool for genome-wide studies of gene regulation in a wide variety of cell types or tissues under diverse conditions.