Universal correction of enzymatic sequence bias reveals molecular signatures of protein/DNA interactions

The chromatin landscape of pathogenic transcriptional cell states in rheumatoid arthritis

Synovial tissue inflammation is a hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. Here, we examine genome-wide open chromatin at single-cell resolution in 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identify 24 chromatin classes and predict their associated transcription factors, including a CD8 + GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating with an RA tissue transcriptional atlas, we propose that these chromatin classes represent 'superstates' corresponding to multiple transcriptional cell states. Finally, we demonstrate the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance, as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.

ZNF143 binds DNA and stimulates transcripstion initiation to activate and repress direct target genes

Transcription factors bind to sequence motifs and act as activators or repressors. Transcription factors interface with a constellation of accessory cofactors to regulate distinct mechanistic steps to regulate transcription. We rapidly degraded the essential and ubiquitously expressed transcription factor ZNF143 to determine its function in the transcription cycle. ZNF143 facilitates RNA Polymerase initiation and activates gene expression. ZNF143 binds the promoter of nearly all its activated target genes. ZNF143 also binds near the site of genic transcription initiation to directly repress a subset of genes. Although ZNF143 stimulates initiation at ZNF143-repressed genes (i.e. those that increase expression upon ZNF143 depletion), the molecular context of binding leads to cis repression. ZNF143 competes with other more efficient activators for promoter access, physically occludes transcription initiation sites and promoter-proximal sequence elements, and acts as a molecular roadblock to RNA Polymerases during early elongation. The term context specific is often invoked to describe transcription factors that have both activation and repression functions. We define the context and molecular mechanisms of ZNF143-mediated cis activation and repression.

Emerging Approaches to Profile Accessible Chromatin from Formalin-Fixed Paraffin-Embedded Sections

Nucleosomes are non-uniformly distributed across eukaryotic genomes, with stretches of 'open' chromatin strongly associated with transcriptionally active promoters and enhancers. Understanding chromatin accessibility patterns in normal tissue and how they are altered in pathologies can provide critical insights to development and disease. With the advent of high-throughput sequencing, a variety of strategies have been devised to identify open regions across the genome, including DNase-seq, MNase-seq, FAIRE-seq, ATAC-seq, and NicE-seq. However, the broad application of such methods to FFPE (formalin-fixed paraffin-embedded) tissues has been curtailed by the major technical challenges imposed by highly fixed and often damaged genomic material. Here, we review the most common approaches for mapping open chromatin regions, recent optimizations to overcome the challenges of working with FFPE tissue, and a brief overview of a typical data pipeline with analysis considerations.

TRPS1 modulates chromatin accessibility to regulate estrogen receptor alpha (ER) binding and ER target gene expression in luminal breast cancer cells

Common genetic variants in the repressive GATA-family transcription factor (TF) TRPS1 locus are associated with breast cancer risk, and luminal breast cancer cell lines are particularly sensitive to TRPS1 knockout. We introduced an inducible degron tag into the native TRPS1 locus within a luminal breast cancer cell line to identify the direct targets of TRPS1 and determine how TRPS1 mechanistically regulates gene expression. We acutely deplete over 80 percent of TRPS1 from chromatin within 30 minutes of inducing degradation. We find that TRPS1 regulates transcription of hundreds of genes, including those related to estrogen signaling. TRPS1 directly regulates chromatin structure, which causes estrogen receptor alpha (ER) to redistribute in the genome. ER redistribution leads to both repression and activation of dozens of ER target genes. Downstream from these primary effects, TRPS1 depletion represses cell cycle-related gene sets and reduces cell doubling rate. Finally, we show that high TRPS1 activity, calculated using a gene expression signature defined by primary TRPS1-regulated genes, is associated with worse breast cancer patient prognosis. Taken together, these data suggest a model in which TRPS1 modulates the genomic distribution of ER, both activating and repressing transcription of genes related to cancer cell fitness.

TRPS1 modulates chromatin accessibility to regulate estrogen receptor (ER) binding and ER target gene expression in luminal breast cancer cells

Breast cancer is the most frequently diagnosed cancer in women. The most common subtype is luminal breast cancer, which is typically driven by the estrogen receptor α (ER), a transcription factor (TF) that activates many genes required for proliferation. Multiple effective therapies target this pathway, but individuals often develop resistance. Thus, there is a need to identify additional targets that regulate ER activity and contribute to breast tumor progression. TRPS1 is a repressive GATA-family TF that is overexpressed in breast tumors. Common genetic variants in the TRPS1 locus are associated with breast cancer risk, and luminal breast cancer cell lines are particularly sensitive to TRPS1 knockout. However, we do not know how TRPS1 regulates target genes to mediate these breast cancer patient and cellular outcomes. We introduced an inducible degron tag into the native TRPS1 locus within a luminal breast cancer cell line to identify the direct targets of TRPS1 and determine how TRPS1 mechanistically regulates gene expression. We acutely deplete over eighty percent of TRPS1 from chromatin within 30 minutes of inducing degradation. We find that TRPS1 regulates transcription of hundreds of genes, including those related to estrogen signaling. TRPS1 directly regulates chromatin structure, which causes ER to redistribute in the genome. ER redistribution leads to both repression and activation of dozens of ER target genes. Downstream from these primary effects, TRPS1 depletion represses cell cycle-related gene sets and reduces cell doubling rate. Finally, we show that high TRPS1 activity, calculated using a gene expression signature defined by primary TRPS1-regulated genes, is associated with worse breast cancer patient prognosis. Taken together, these data suggest a model in which TRPS1 modulates the activity of other TFs, both activating and repressing transcription of genes related to cancer cell fitness.

Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling

Chromatin accessibility assays have revolutionized the field of transcription regulation by providing single-nucleotide resolution measurements of regulatory features such as promoters and transcription factor binding sites. ATAC-seq directly measures how well the Tn5 transposase accesses chromatinized DNA. Tn5 has a complex sequence bias that is not effectively scaled with traditional bias-correction methods. We model this complex bias using a rule ensemble machine learning approach that integrates information from many input k-mers proximal to the ATAC sequence reads. We effectively characterize and correct single-nucleotide sequence biases and regional sequence biases of the Tn5 enzyme. Correction of enzymatic sequence bias is an important step in interpreting chromatin accessibility assays that aim to infer transcription factor binding and regulatory activity of elements in the genome.

The Chromatin Landscape of Pathogenic Transcriptional Cell States in Rheumatoid Arthritis

Synovial tissue inflammation is the hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. We measured genome-wide open chromatin at single cell resolution from 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identified 24 chromatin classes and predicted their associated transcription factors, including a CD8+ GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating an RA tissue transcriptional atlas, we found that the chromatin classes represented 'superstates' corresponding to multiple transcriptional cell states. Finally, we demonstrated the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.

ATAC-seq exposes differences in chromatin accessibility leading to distinct leaf shapes in mulberry

Mulberry leaf shape is an important agronomic trait indicating yield, growth, development, and habitat variation. China was the earliest country in the world to grow mulberry for sericulture, and it is also one of the great contributions of the Chinese nation to human civilization. ATAC-seq (Assay for Transposase Accessible Chromatin using sequencing) is a recently developed technique for genome-wide analysis of chromatin accessibility. The samples used for ATAC sequencing in this study were divided into two groups of whole leaves (CK-1 and CK-2) and lobed leaves (HL-1 and HL-2), with two replicates in each group. The related motif analysis, differential expression motif screening, and functional annotation of mulberry leaf shape differences were performed by raw letter analysis to finally obtain the transcription factors (TFs) that lead to the production of heteromorphic leaves. These transcription factors are common in plants, especially the TCP family, shown to be associated with leaf development and growth in other woody plants and are a potential transcription factor responsible for leaf shape differences in mulberry. Dissecting the regulatory mechanisms of leaf shape of different forms of mulberry leaves by ATAC-seq is an important way to protect mulberry germplasm resources and improve mulberry yield. It is conducive to cultivating mulberry varieties with high resistance to adversity, promoting the sustainable development of sericulture, and protecting and improving the ecological environment.

Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA

Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data.

Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. Here the authors develop a computational model, SELMA, to estimate and correct enzymatic cleavage biases in chromatin accessibility profiling data.

Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data

Over a thousand different transcription factors (TFs) bind with varying occupancy across the human genome. Chromatin immunoprecipitation (ChIP) can assay occupancy genome-wide, but only one TF at a time, limiting our ability to comprehensively observe the TF occupancy landscape, let alone quantify how it changes across conditions. We developed TF occupancy profiler (TOP), a Bayesian hierarchical regression framework, to profile genome-wide quantitative occupancy of numerous TFs using data from a single chromatin accessibility experiment (DNase- or ATAC-seq). TOP is supervised, and its hierarchical structure allows it to predict the occupancy of any sequence-specific TF, even those never assayed with ChIP. We used TOP to profile the quantitative occupancy of hundreds of sequence-specific TFs at sites throughout the genome and examined how their occupancies changed in multiple contexts: in approximately 200 human cell types, through 12 h of exposure to different hormones, and across the genetic backgrounds of 70 individuals. TOP enables cost-effective exploration of quantitative changes in the landscape of TF binding.

Chromatin accessibility profiling by ATAC-seq

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) provides a simple and scalable way to detect the unique chromatin landscape associated with a cell type and how it may be altered by perturbation or disease. ATAC-seq requires a relatively small number of input cells and does not require a priori knowledge of the epigenetic marks or transcription factors governing the dynamics of the system. Here we describe an updated and optimized protocol for ATAC-seq, called Omni-ATAC, that is applicable across a broad range of cell and tissue types. The ATAC-seq workflow has five main steps: sample preparation, transposition, library preparation, sequencing and data analysis. This protocol details the steps to generate and sequence ATAC-seq libraries, with recommendations for sample preparation and downstream bioinformatic analysis. ATAC-seq libraries for roughly 12 samples can be generated in 10 h by someone familiar with basic molecular biology, and downstream sequencing analysis can be implemented using benchmarked pipelines by someone with basic bioinformatics skills and with access to a high-performance computing environment.

Comprehensive understanding of Tn5 insertion preference improves transcription regulatory element identification

Tn5 transposase, which can efficiently tagment the genome, has been widely adopted as a molecular tool in next-generation sequencing, from short-read sequencing to more complex methods such as assay for transposase-accessible chromatin using sequencing (ATAC-seq). Here, we systematically map Tn5 insertion characteristics across several model organisms, finding critical parameters that affect its insertion. On naked genomic DNA, we found that Tn5 insertion is not uniformly distributed or random. To uncover drivers of these biases, we used a machine learning framework, which revealed that DNA shape cooperatively works with DNA motif to affect Tn5 insertion preference. These intrinsic insertion preferences can be modeled using nucleotide dependence information from DNA sequences, and we developed a computational pipeline to correct for these biases in ATAC-seq data. Using our pipeline, we show that bias correction improves the overall performance of ATAC-seq peak detection, recovering many potential false-negative peaks. Furthermore, we found that these peaks are bound by transcription factors, underscoring the biological relevance of capturing this additional information. These findings highlight the benefits of an improved understanding and precise correction of Tn5 insertion preference.

The Thioesterase ACOT1 as a Regulator of Lipid Metabolism in Type 2 Diabetes Detected in a Multi-Omics Study of Human Liver

Type 2 diabetes (T2D) is characterized by pathophysiological alterations in lipid metabolism. One strategy to understand the molecular mechanisms behind these abnormalities is to identify cis-regulatory elements (CREs) located in chromatin-accessible regions of the genome that regulate key genes. In this study we integrated assay for transposase-accessible chromatin followed by sequencing (ATAC-seq) data, widely used to decode chromatin accessibility, with multi-omics data and publicly available CRE databases to identify candidate CREs associated with T2D for further experimental validations. We performed high-sensitive ATAC-seq in nine human liver samples from normal and T2D donors, and identified a set of differentially accessible regions (DARs). We identified seven DARs including a candidate enhancer for the ACOT1 gene that regulates the balance of acyl-CoA and free fatty acids (FFAs) in the cytoplasm. The relevance of ACOT1 regulation in T2D was supported by the analysis of transcriptomics and proteomics data in liver tissue. Long-chain acyl-CoA thioesterases (ACOTs) are a group of enzymes that hydrolyze acyl-CoA esters to FFAs and coenzyme A. ACOTs have been associated with regulation of triglyceride levels, fatty acid oxidation, mitochondrial function, and insulin signaling, linking their regulation to the pathogenesis of T2D. Our strategy integrating chromatin accessibility with DNA binding and other types of omics provides novel insights on the role of genetic regulation in T2D and is extendable to other complex multifactorial diseases.

Annotating the Insect Regulatory Genome

An ever-growing number of insect genomes is being sequenced across the evolutionary spectrum. Comprehensive annotation of not only genes but also regulatory regions is critical for reaping the full benefits of this sequencing. Driven by developments in sequencing technologies and in both empirical and computational discovery strategies, the past few decades have witnessed dramatic progress in our ability to identify cis-regulatory modules (CRMs), sequences such as enhancers that play a major role in regulating transcription. Nevertheless, providing a timely and comprehensive regulatory annotation of newly sequenced insect genomes is an ongoing challenge. We review here the methods being used to identify CRMs in both model and non-model insect species, and focus on two tools that we have developed, REDfly and SCRMshaw. These resources can be paired together in a powerful combination to facilitate insect regulatory annotation over a broad range of species, with an accuracy equal to or better than that of other state-of-the-art methods.

Fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution

Decoding the cell type-specific transcription factor (TF) binding landscape at single-nucleotide resolution is crucial for understanding the regulatory mechanisms underlying many fundamental biological processes and human diseases. However, limits on time and resources restrict the high-resolution experimental measurements of TF binding profiles of all possible TF-cell type combinations. Previous computational approaches either cannot distinguish the cell context-dependent TF binding profiles across diverse cell types or can only provide a relatively low-resolution prediction. Here we present a novel deep learning approach, Leopard, for predicting TF binding sites at single-nucleotide resolution, achieving the average area under receiver operating characteristic curve (AUROC) of 0.982 and the average area under precision recall curve (AUPRC) of 0.208. Our method substantially outperformed the state-of-the-art methods Anchor and FactorNet, improving the predictive AUPRC by 19% and 27%, respectively, when evaluated at 200-bp resolution. Meanwhile, by leveraging a many-to-many neural network architecture, Leopard features a hundredfold to thousandfold speedup compared with current many-to-one machine learning methods.

ATAC-Seq Identifies Chromatin Landscapes Linked to the Regulation of Oxidative Stress in the Human Fungal Pathogen Candida albicans

Human fungal pathogens often encounter fungicidal stress upon host invasion, but they can swiftly adapt by transcriptional reprogramming that enables pathogen survival. Fungal immune evasion is tightly connected to chromatin regulation. Hence, fungal chromatin modifiers pose alternative treatment options to combat fungal infections. Here, we present an assay for transposase-accessible chromatin using sequencing (ATAC-seq) protocol adapted for the opportunistic pathogen Candida albicans to gain further insight into the interplay of chromatin accessibility and gene expression mounted during fungal adaptation to oxidative stress. The ATAC-seq workflow not only facilitates the robust detection of genomic regions with accessible chromatin but also allows for the precise modeling of nucleosome positions in C. albicans. Importantly, the data reveal genes with altered chromatin accessibility in upstream regulatory regions, which correlate with transcriptional regulation during oxidative stress. Interestingly, many genes show increased chromatin accessibility without change in gene expression upon stress exposure. Such chromatin signatures could predict yet unknown regulatory factors under highly dynamic transcriptional control. Additionally, de novo motif analysis in genomic regions with increased chromatin accessibility upon H₂O₂ treatment shows significant enrichment for Cap1 binding sites, a major factor of oxidative stress responses in C. albicans. Taken together, the ATAC-seq workflow enables the identification of chromatin signatures and highlights the dynamics of regulatory mechanisms mediating environmental adaptation of C. albicans.

Self-Reporting Transposons Enable Simultaneous Readout of Gene Expression and Transcription Factor Binding in Single Cells

Cellular heterogeneity confounds in situ assays of transcription factor (TF) binding. Single-cell RNA sequencing (scRNA-seq) deconvolves cell types from gene expression, but no technology links cell identity to TF binding sites (TFBS) in those cell types. We present self-reporting transposons (SRTs) and use them in single-cell calling cards (scCC), a novel assay for simultaneously measuring gene expression and mapping TFBS in single cells. The genomic locations of SRTs are recovered from mRNA, and SRTs deposited by exogenous, TF-transposase fusions can be used to map TFBS. We then present scCC, which map SRTs from scRNA-seq libraries, simultaneously identifying cell types and TFBS in those same cells. We benchmark multiple TFs with this technique. Next, we use scCC to discover BRD4-mediated cell-state transitions in K562 cells. Finally, we map BRD4 binding sites in the mouse cortex at single-cell resolution, establishing a new method for studying TF biology in situ.

Analytical Approaches for ATAC-seq Data Analysis

ATAC-seq, the assay for transposase-accessible chromatin using sequencing, is a quick and efficient approach to investigating the chromatin accessibility landscape. Investigating chromatin accessibility has broad utility for answering many biological questions, such as mapping nucleosomes, identifying transcription factor binding sites, and measuring differential activity of DNA regulatory elements. Because the ATAC-seq protocol is both simple and relatively inexpensive, there has been a rapid increase in the availability of chromatin accessibility data. Furthermore, advances in ATAC-seq protocols are rapidly extending its breadth to additional experimental conditions, cell types, and species. Accompanying the increase in data, there has also been an explosion of new tools and analytical approaches for analyzing it. Here, we explain the fundamentals of ATAC-seq data processing, summarize common analysis approaches, and review computational tools to provide recommendations for different research questions. This primer provides a starting point and a reference for analysis of ATAC-seq data. © 2020 Wiley Periodicals LLC.

Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data

Chromatin accessibility maps are important for the functional interpretation of the genome. Here, we systematically analysed assay specific differences between DNase I-seq, ATAC-seq and NOMe-seq in a side by side experimental and bioinformatic setup. We observe that most prominent nucleosome depleted regions (NDRs, e.g. in promoters) are roboustly called by all three or at least two assays. However, we also find a high proportion of assay specific NDRs that are often ‘called’ by only one of the assays. We show evidence that these assay specific NDRs are indeed genuine open chromatin sites and contribute important information for accurate gene expression prediction. While technically ATAC-seq and DNase I-seq provide a superb high NDR calling rate for relatively low sequencing costs in comparison to NOMe-seq, NOMe-seq singles out for its genome-wide coverage allowing to not only detect NDRs but also endogenous DNA methylation and as we show here genome wide segmentation into heterochromatic B domains and local phasing of nucleosomes outside of NDRs. In summary, our comparisons strongly suggest to consider assay specific differences for the experimental design and for generalized and comparative functional interpretations.

ATAC-seq normalization method can significantly affect differential accessibility analysis and interpretation

BACKGROUND

Chromatin dysregulation is associated with developmental disorders and cancer. Numerous methods for measuring genome-wide chromatin accessibility have been developed in the genomic era to interrogate the function of chromatin regulators. A recent technique which has gained widespread use due to speed and low input requirements with native chromatin is the Assay for Transposase-Accessible Chromatin, or ATAC-seq. Biologists have since used this method to compare chromatin accessibility between two cellular conditions. However, approaches for calculating differential accessibility can yield conflicting results, and little emphasis is placed on choice of normalization method during differential ATAC-seq analysis, especially when global chromatin alterations might be expected.

RESULTS

Using an in vivo ATAC-seq data set generated in our recent report, we observed differences in chromatin accessibility patterns depending on the data normalization method used to calculate differential accessibility. This observation was further verified on published ATAC-seq data from yeast. We propose a generalized workflow for differential accessibility analysis using ATAC-seq data. We further show this workflow identifies sites of differential chromatin accessibility that correlate with gene expression and is sensitive to differential analysis using negative controls.

CONCLUSIONS

We argue that researchers should systematically compare multiple normalization methods before continuing with differential accessibility analysis. ATAC-seq users should be aware of the interpretations of potential bias within experimental data and the assumptions of the normalization method implemented.

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.

HMMRATAC: a Hidden Markov ModeleR for ATAC-seq

ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragments that contain additional nucleosome positioning information. We present the first dedicated ATAC-seq analysis tool, a semi-supervised machine learning approach named HMMRATAC. HMMRATAC splits a single ATAC-seq dataset into nucleosome-free and nucleosome-enriched signals, learns the unique chromatin structure around accessible regions, and then predicts accessible regions across the entire genome. We show that HMMRATAC outperforms the popular peak-calling algorithms on published human ATAC-seq datasets. We find that single-end sequenced or size-selected ATAC-seq datasets result in a loss of sensitivity compared to paired-end datasets without size-selection.

CTCF confers local nucleosome resiliency after DNA replication and during mitosis

The access of Transcription Factors (TFs) to their cognate DNA binding motifs requires a precise control over nucleosome positioning. This is especially important following DNA replication and during mitosis, both resulting in profound changes in nucleosome organization over TF binding regions. Using mouse Embryonic Stem (ES) cells, we show that the TF CTCF displaces nucleosomes from its binding site and locally organizes large and phased nucleosomal arrays, not only in interphase steady-state but also immediately after replication and during mitosis. Correlative analyses suggest this is associated with fast gene reactivation following replication and mitosis. While regions bound by other TFs (Oct4/Sox2), display major rearrangement, the post-replication and mitotic nucleosome positioning activity of CTCF is not unique: Esrrb binding regions are also characterized by persistent nucleosome positioning. Therefore, selected TFs such as CTCF and Esrrb act as resilient TFs governing the inheritance of nucleosome positioning at regulatory regions throughout the cell-cycle.

An improved auxin-inducible degron system preserves native protein levels and enables rapid and specific protein depletion

Rapid perturbation of protein function permits the ability to define primary molecular responses while avoiding downstream cumulative effects of protein dysregulation. The auxin-inducible degron (AID) system was developed as a tool to achieve rapid and inducible protein degradation in nonplant systems. However, tagging proteins at their endogenous loci results in chronic auxin-independent degradation by the proteasome. To correct this deficiency, we expressed the auxin response transcription factor (ARF) in an improved inducible degron system. ARF is absent from previously engineered AID systems but is a critical component of native auxin signaling. In plants, ARF directly interacts with AID in the absence of auxin, and we found that expression of the ARF PB1 (Phox and Bem1) domain suppresses constitutive degradation of AID-tagged proteins. Moreover, the rate of auxin-induced AID degradation is substantially faster in the ARF-AID system. To test the ARF-AID system in a quantitative and sensitive manner, we measured genome-wide changes in nascent transcription after rapidly depleting the ZNF143 transcription factor. Transcriptional profiling indicates that ZNF143 activates transcription in cis and regulates promoter-proximal paused RNA polymerase density. Rapidly inducible degradation systems that preserve the target protein's native expression levels and patterns will revolutionize the study of biological systems by enabling specific and temporally defined protein dysregulation.

Identification of transcription factor binding sites using ATAC-seq

Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) is a simple protocol for detection of open chromatin. Computational footprinting, the search for regions with depletion of cleavage events due to transcription factor binding, is poorly understood for ATAC-seq. We propose the first footprinting method considering ATAC-seq protocol artifacts. HINT-ATAC uses a position dependency model to learn the cleavage preferences of the transposase. We observe strand-specific cleavage patterns around transcription factor binding sites, which are determined by local nucleosome architecture. By incorporating all these biases, HINT-ATAC is able to significantly outperform competing methods in the prediction of transcription factor binding sites with footprints.

Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modeling

BACKGROUND

DNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBSs) in regulatory regions through footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq.

RESULTS

Here, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite the differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impact the discrimination of footprint from the background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints.

CONCLUSIONS

We demonstrate that the impact of bias correction on footprinting performance is greater for DNase-seq than for ATAC-seq and that DNase-seq footprinting leads to better performance. It is possible to infer concordant footprints by using replicates, highlighting the importance of reproducibility assessment. The results presented here provide an overview of the advantages and limitations of footprinting analyses using ATAC-seq and DNase-seq.

Anchor: trans-cell type prediction of transcription factor binding sites

The ENCyclopedia of DNA Elements (ENCODE) consortium has generated transcription factor (TF) binding ChIP-seq data covering hundreds of TF proteins and cell types; however, due to limits on time and resources, only a small fraction of all possible TF-cell type pairs have been profiled. One solution is to build machine learning models trained on currently available epigenomic data sets that can be applied to the remaining missing pairs. A major challenge is that TF binding sites are cell-type-specific, which can be attributed to cellular contexts such as chromatin accessibility. Meanwhile, indirect TF-DNA binding and interactions between TFs complicate this regulatory process. Technical issues such as sequencing biases and batch effects render the prediction task even more challenging. Many pioneering efforts have been made to predict TF binding profiles based on DNA sequence and DNase-seq footprints, but to what extent a model can be generalized to completely untested cell conditions remains unknown. In this study, we describe our first place solution to the 2017 ENCODE-DREAM in vivo TF binding site prediction challenge. By carefully addressing multisource biases and information imbalance across cell types, we created a pipeline that significantly outperforms the current state-of-the-art methods. The proposed method is sufficiently complex enough to model nonlinear interactions between TF binding motifs and chromatin accessibility information up to 1500 bp from the genomic region of interest.

mtDNA Chromatin-like Organization Is Gradually Established during Mammalian Embryogenesis

Unlike the nuclear genome, the mammalian mitochondrial genome (mtDNA) is thought to be coated solely by mitochondrial transcription factor A (TFAM), whose binding sequence preferences are debated. Therefore, higher-order mtDNA organization is considered much less regulated than both the bacterial nucleoid and the nuclear chromatin. However, our recently identified conserved DNase footprinting pattern in human mtDNA, which co-localizes with regulatory elements and responds to physiological conditions, likely reflects a structured higher-order mtDNA organization. We hypothesized that this pattern emerges during embryogenesis. To test this hypothesis, we analyzed assay for transposase-accessible chromatin sequencing (ATAC-seq) results collected during the course of mouse and human early embryogenesis. Our results reveal, for the first time, a gradual and dynamic emergence of the adult mtDNA footprinting pattern during embryogenesis of both mammals. Taken together, our findings suggest that the structured adult chromatin-like mtDNA organization is gradually formed during mammalian embryogenesis.

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Mouse and human mtDNA ATAC-seq footprinting patterns are formed during embryogenesis

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mtDNA footprinting sites were either occupied in preimplantation or appeared later

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mtDNA footprinting associates with regulatory elements and protein-binding sites

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The mtDNA footprinting sites tend to harbor secondary structures

Simplified ChIP-exo assays

ChIP-seq and ChIP-exo identify where proteins bind along any genome in vivo. Although ChIP-seq is widely adopted in academic research, it has inherently high noise. In contrast, ChIP-exo has relatively low noise and achieves near-base pair resolution. Consequently, and unlike other genomic assays, ChIP-exo provides structural information on genome-wide binding proteins. Construction of ChIP-exo libraries is technically difficult. Here we describe greatly simplified ChIP-exo methods, each with use-specific advantages. This is achieved through assay optimization and use of Tn5 tagmentation and/or single-stranded DNA ligation. Greater library yields, lower processing time, and lower costs are achieved. In comparing assays, we reveal substantial limitations in other ChIP-based assays. Importantly, the new ChIP-exo assays allow high-resolution detection of some protein-DNA interactions in organs and in as few as 27,000 cells. It is suitable for high-throughput parallelization. The simplicity of ChIP-exo now makes it a highly appropriate substitute for ChIP-seq, and for broader adoption.

While ChIP-exo is low noise and highly informative regarding genome-wide binding proteins, libraries are difficult to construct. Here the authors present a simplified ChIP-exo method for high-resolution detection of interactions.

Identification of breast cancer associated variants that modulate transcription factor binding

Genome-wide association studies (GWAS) have discovered thousands loci associated with disease risk and quantitative traits, yet most of the variants responsible for risk remain uncharacterized. The majority of GWAS-identified loci are enriched for non-coding single-nucleotide polymorphisms (SNPs) and defining the molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter transcription factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. We identified candidate causal SNPs that are predicted to alter TF binding within breast cancer-relevant regulatory regions that are in strong linkage disequilibrium with significantly associated GWAS SNPs. We confirm that the TFs bind with predicted allele-specific preferences using CTCF ChIP-seq data. We used The Cancer Genome Atlas breast cancer patient data to identify ANKLE1 and ZNF404 as the target genes of candidate TF binding site SNPs in the 19p13.11 and 19q13.31 GWAS-identified loci. These SNPs are associated with the expression of ZNF404 and ANKLE1 in breast tissue. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.