Search

Transcription factors (TFs) are the main regulators of eukaryotic gene expression. The cooperative binding of at least two TFs to genomic DNA is a major mechanism of transcription regulation. Massive analysis of the co-occurrence of overrepresented pairs of motifs for different target TFs studied in ChIP-seq experiments can clarify the mechanisms of TF cooperation. We categorized the target TFs from M. musculus ChIP-seq and A. thaliana ChIP-seq/DAP-seq experiments according to the structure of their DNA-binding domains (DBDs) into classes. We studied homotypic pairs of motifs, using the same recognition model for each motif. Asymmetric and symmetric pairs consist of motifs of remote and close recognition scores. We found that asymmetric pairs of motifs predominate for all TF classes. TFs from the murine/plant 'Basic helix-loop-helix (bHLH)', 'Basic leucine zipper (bZIP)', and 'Tryptophan cluster' classes and murine 'p53 domain' and 'Rel homology region' classes showed the highest enrichment of asymmetric homotypic pairs of motifs. Pioneer TFs, despite their DBD types, have a higher significance of asymmetry within homotypic pairs of motifs compared to other TFs. Asymmetry within homotypic CEs is a promising new feature decrypting the mechanisms of gene transcription regulation.

Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. Here, we develop ChromatinHD, a pair of scale-adaptive models that uses the raw accessibility data, without peak-calling or windows, to link regions to gene expression and determine differentially accessible chromatin. We show how ChromatinHD consistently outperforms existing peak and window-based approaches and find that this is due to a large number of uniquely captured, functional accessibility changes within and outside of putative cis-regulatory regions. Furthermore, ChromatinHD can delineate collaborating regulatory regions, including their preferential genomic conformations, that drive gene expression. Finally, our models also use changes in ATAC-seq fragment lengths to identify dense binding of transcription factors, a feature not captured by footprinting methods. Altogether, ChromatinHD, available at https://chromatinhd.org , is a suite of computational tools that enables a data-driven understanding of chromatin accessibility at various scales and how it relates to gene expression., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

ChIP-Seq is used to study DNA-protein interactions, unraveling chromatin states and gene regulatory properties of transcription factors. ChIP-Seq involves immunoprecipitation followed by sequencing using Next-Generation sequencing approaches. The ENCODE consortium provides extensive guidelines for ChIP-Seq analysis. Meanwhile, appropriate QC metrics and knowledge to interpret outcomes are essential for a good ChIP-Seq experiment. This chapter outlines the various QC metrics and analytical tools for ChIP-Seq analysis to provide a better understanding of the results., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Hox genes play a pivotal role during development. Their expression is tightly controlled in a spatiotemporal manner, ensuring that specific body structures develop at the correct locations and times during development. Various genomics approaches have been used to capture temporal and dynamic regulation of Hox gene expression at the nucleosome/chromatin level. This chapter focuses on the utilization of capture MNase-seq and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), two advanced techniques that enable the exploration of chromatin accessibility and nucleosome positioning within these critical genomic regions., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Homeobox genes (HOX), the master regulators, deploy a unique set of target genes to coordinate and orchestrate the spatiotemporal development of an organism. HOX encoded transcriptional factors regulate the expression of target genes by binding to the specific sequences on the genome. Chromatin Immunoprecipitation (ChIP) and Chromatin Immunoprecipitation with Sequencing (ChIP-Seq) are widely used to map and understand specific gene locus and global regulatory regions on the genome. ChIP is a powerful technique of cross-linking the proteins bound to the DNA, fragmenting DNA to the desired size, and pulling them down using specific antibodies to enrich and analyze the protein-bound DNA. Based on the mapping information, a differential ChIP can be performed to understand cis-regulatory modulations at a defined locus by two developmental stages. This chapter describes the differential ChIP used to identify new targets by comparing two different developmental stages simultaneously using Drosophila melanogaster., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Various proteins interact with specific genome regions, playing crucial roles in gene regulation. Chromatin Immunoprecipitation (ChIP) is the most commonly used method to study protein-DNA interactions in vivo. By combining ChIP with high-throughput sequencing, ChIP-seq allows for studying the genome-wide localization of proteins. Although several ChIP protocols are available for plant tissues, they are primarily designed for histone modifications and abundant proteins with high DNA-binding affinity, which are considered as the "standard targets." Here we describe a ChIP protocol for plant tissues not only optimized for the standard targets but also adapted for proteins with low abundances or weak DNA-binding ability. Successful execution of the protocol enables reliable generation of DNA templates for quantitative PCR or libraries for next-generation sequencing, which makes it an effective tool for analyzing genomic interactions of a wide range of proteins., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is widely used to probe the chromatin landscape of transcription factors, chromatin components, and associated proteins. Conventional ChIP normalization procedures robustly allow estimating differences in local enrichment across genomic regions. Yet, inter-sample comparisons can be biased by technical variability and biological differences. This is notably the case when samples display large differences in the abundance of the target protein or its enrichment at chromatin. For example, epigenome defects are improperly detected or quantified upon large-effect genetic or chemical inhibition of chromatin modifiers. To circumvent these caveats and robustly determine biological variations while minimizing technical variability, ChIP adaptations using an external reference have flourished. Here, we describe a step-by-step protocol employing a reference exogenous chromatin (ChIP-Rx) that allows absolute comparisons of epigenome variations in Arabidopsis samples displaying drastic differences in chromatin mark abundance. In contrast to the originally published ChIP-Rx approach, which assumes that exogenous spike-in references are constant across samples, the method detailed here involves the sequencing of each input sample to account for technical variability in initial reference chromatin contents. We also report a detailed computational workflow with an accompanying Github resource to help in calculating spike-in normalization factors, applying them to normalize epigenome tracks, and performing spike-in normalized inter-sample differential analyses. We propose two ways of computing the spike-in factor: a classically used method based on raw counts and a noise-corrected method using peak detection on the exogenous genome., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Chromatin regulation provides a mechanism through which cells dynamically and rapidly regulate their gene expression profiles, playing a pivotal role in diverse biological processes and disease states. The Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method that enables genome-wide detection of accessible chromatin regions, providing information on nucleosome positioning and the epigenetic regulation of the chromatin structure. ATAC-seq has been used in various biological contexts, and several reports have demonstrated its application to studying infections with viral or bacterial pathogens. The ability to characterize changes in viral or bacterial genome accessibility during infections provides insights into both pathogen replication and host defense mechanisms. Viral genomes undergo dynamic changes in their structural landscape to facilitate replication and evade host immune responses. Additionally, host cells encode DNA sensors, which are specialized proteins that bind to viral genomes to initiate innate immune responses and sometimes, to suppress viral gene expression. ATAC-seq enables the systematic detection of key structural changes on the viral genome mediated by either viral or host proteins, offering mechanistic insights into virus-host interactions. Here, we describe an ATAC-seq method optimized for studying changes in chromatin accessibility in both host and viral genomes. We have previously applied this method to demonstrate a systematic decrease in the genome accessibility of herpes simplex virus type I (HSV-1) enabled by a host antiviral factor, the interferon-gamma inducible protein 16 (IFI16) during infection of human fibroblasts. This protocol can be adapted to various biological contexts involving the introduction of foreign DNA, making it a valuable tool for a broad range of research endeavors., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Histone modifications and transcription factor-DNA interactions regulate vital processes such as transcription, recombination, repair, and accurate chromosome segregation. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has been instrumental in studying genome-wide distribution of DNA-bound or chromatin-associated factors and histone posttranslational modifications (PTMs). Here, we describe a ChIPmentation protocol adapted for fission yeast, Schizosaccharomyces pombe. This method merges Tn5 mediated tagmentation with existing ChIP protocols, resulting in lower sample input requirements with significant reduction in hands-on time and sample preparation costs., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Eukaryotic DNA is organized in the nucleus in the form of chromatin. Nucleosomes, the fundamental unit of chromatin, are subject to many posttranslational modifications (PTMs) as well as compositional variations through incorporation of histone variants. These alterations play important roles in regulation of genome structure and activity. Genome-wide profiling of these regulatory features is essential for understanding of genome function. Chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-Seq) is a widely used method to assay genome-wide localization in fission yeast but suffers from the requirement for a large amount of input chromatin, antibodies, and a cumbersome experimental pipeline. New methods such as Cleavage Under Targets and Tagmentation (CUT&Tag), which combine the specificity of targeted cleavage and adapter insertion with the sensitivity of next-generation sequencing, enable identification and characterization of various epigenetic marks affording low input requirement as well as more streamlined protocols. However, these approaches have not been adapted for use in fission yeast, Schizosaccharomyces pombe. Here, we describe an adapted CUT&Tag protocol for epigenomic profiling in fission yeast using the heterochromatin-associated histone H3K9 methylation PTM for benchmarking., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

This chapter describes the computational pipeline for the processing and visualization of Protec-Seq data, a method for purification and genome-wide mapping of double-stranded DNA protected by a specific protein at both ends. In the published case, the protein of choice was Saccharomyces cerevisiae Spo11, a conserved topoisomerase-like enzyme that makes meiotic double-strand breaks (DSBs) to initiate homologous recombination, ensuring proper segregation of homologous chromosomes and fertility. The isolated DNA molecules were thus termed double DSB (dDSB) fragments and were found to represent 34 to several hundred base-pair long segments that are generated by Spo11 and are enriched at DSB hotspots, which are sites of topological stress. In order to allow quantitative comparisons between dDSB profiles across experiments, we implemented calibrated chromatin immunoprecipitation sequencing (ChIP-Seq) using the meiosis-competent yeast species Saccharomyces kudriavzevii as calibration strain. Here, we provide a detailed description of the computational methods for processing, analyzing, and visualizing Protec-Seq data, comprising the download of the raw data, the calibrated genome-wide alignments, and the scripted creation of either arc plots or Hi-C-style heatmaps for the illustration of chromosomal regions of interest. The workflow is based on Linux shell scripts (including wrappers for publicly available, open-source software) as well as R scripts and is highly customizable through its modular structure., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

To reveal gene regulation mechanisms, it is essential to understand the role of regulatory elements, which are possibly distant from gene promoters. Integrative analysis of epigenetic and transcriptomic data can be used to gain insights into gene-expression regulation in specific phenotypes. Here, we discuss STITCHIT, an approach to dissect epigenetic variation in a gene-specific manner across many samples for the identification of regulatory elements without relying on peak calling algorithms. The obtained genomic regions are then further refined using a regularized linear model approach, which can also be used to predict gene expression. We illustrate the use of STITCHIT using H3k27ac ChIP-seq and RNA-seq data from the International Human Epigenome Consortium (IHEC)., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline ., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The generation of quality data from a single-nucleus profiling experiment requires nuclei to be isolated from tissues in a gentle and efficient manner. Nuclei isolation must be carefully optimized across tissue types to preserve nuclear architecture, prevent nucleic acid degradation, and remove unwanted contaminants. Here, we present an optimized workflow for generating a single-nucleus suspension from ocular tissues of the embryonic chicken that is compatible with various downstream workflows. The described protocol enables the rapid isolation of a high yield of aggregate-free nuclei from the embryonic chicken eye without compromising nucleic acid integrity, and the nuclei suspension is compatible with single-nucleus RNA and ATAC sequencing. We detail several stopping points, either via cryopreservation or fixation, to enhance workflow adaptability. Further, we provide a guide through multiple QC points and demonstrate proof-of-principle using two commercially available kits. Finally, we demonstrate that existing in silico genotyping methods can be adopted to computationally derive biological replicates from a single pool of chicken nuclei, greatly reducing the cost of biological replication and allowing researchers to consider sex as a variable during analysis. Together, this tutorial represents a cost-effective, simple, and effective approach to single-nucleus profiling of embryonic chicken eye tissues and is likely to be easily modified to be compatible with similar tissue types., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Assessing the dynamics of chromatin features and transcription factor (TF) binding at scale remains a significant challenge in plants. Here, we present PHILO (Plant HIgh-throughput LOw input) ChIP-seq, a high-throughput ChIP-seq platform that enables the cost-effective and extensive capture of TF binding and genome-wide distributions of histone modifications. The PHILO ChIP-seq pipeline is adaptable to many plant species, requires very little starting material (1mg), and provides the option to use MNase (micrococcal nuclease) for chromatin fragmentation. By employing H3K9ac PHILO ChIP-seq on eight Arabidopsis thaliana jasmonic acid (JA) pathway mutants, with the simultaneous processing of over 100 samples, we not only recapitulated but also expanded the current understanding of the intricate interplay between the master TFs MYC2/3/4 and various chromatin regulators. Additionally, our analyses brought to light previously unknown histone acetylation patterns within the regulatory regions of MYC2 target genes in Arabidopsis, which is also conserved in tomato (Solanum lycopersicum). In summary, our PHILO ChIP-seq platform demonstrates its high effectiveness in investigating TF binding and chromatin dynamics on a large scale in plants, paving the way for the cost-efficient realization of complex experimental setups., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Cellular reprogramming of somatic cells towards induced pluripotency is a multistep stochastic process mediated by the transcription factors Oct4, Sox2, Klf4 and c-Myc (OSKM), which orchestrate global epigenetic and transcriptional changes. We performed a large-scale analysis of integrated ChIP-seq, ATAC-seq and RNA-seq data and revealed the spatiotemporal highly dynamic pattern of OSKM DNA binding during reprogramming. We found that OSKM show distinct temporal patterns of binding to different classes of pluripotency-related enhancers. Genes involved in reprogramming are regulated by the coordinated activity of multiple enhancers, which are sequentially bound by OSKM for strict transcriptional control. Based on these findings, we developed an unbiased approach to identify Reprogramming-Inducible Enhancers (RIEs), constructed enhancer-traps and isolated cells undergoing reprogramming in real time. We used a representative RIE taken from the Upp1 gene fused to Gfp and isolated cells at different time-points during reprogramming and found that they have unique developmental capacities as they are reprogrammed with high efficiency due to their distinct molecular signatures. In conclusion, our experiments have led to the development of an unbiased method to identify and isolate reprogrammable cells in real time by exploiting the functional dynamics of OSKM, which can be used as efficient reprogramming biomarkers.

Transcriptional regulatory networks (TRNs) in bacteria are crucial for elucidating the mechanisms that regulate gene expression and cellular responses to environmental stimuli. These networks delineate the interactions between transcription factors (TFs) and their target genes, thereby uncovering the regulatory processes that modulate gene expression under varying environmental conditions. Analyzing TRNs offers valuable insights into bacterial adaptation, stress responses, and metabolic optimization from an evolutionary standpoint. Additionally, understanding TRNs can drive the development of novel antimicrobial therapies and the engineering of microbial strains for biofuel and bioproduct production. This protocol integrates advanced data analysis pipelines, including ChEAP, DEOCSU, and DESeq2, to analyze omics datasets that encompass genome-wide TF binding sites and transcriptome profiles derived from ChIP-exo and RNA-seq experiments. This approach minimizes both the time required and the risk of bias, making it accessible to non-expert users. Key steps in the protocol include preprocessing and peak calling from ChIP-exo data, differential expression analysis of RNA-seq data, and motif and regulon analysis. This method offers a comprehensive and efficient framework for TRN reconstruction across various bacterial strains, enhancing both the accuracy and reliability of the analysis while providing valuable insights for basic and applied research., Competing Interests: Declarations. Conflict of interest: The authors have no conflict of interest to report., (© 2024. The Author(s), under exclusive licence to Microbiological Society of Korea.)

One challenge in studies using tissue collected from multiple cohorts is avoiding batch effects when preparing for large-scale multi-omic experiments, such as combined single-cell RNA sequencing and metabolomics. The method in the current study utilizes flash-frozen pancreatic islets from fetal non-human primates collected over a span of two years for input into single-nucleus RNA sequencing and ATAC sequencing assays. The cytosolic fraction generated during nuclear extraction was retained for downstream capillary electrophoresis-mass spectrometry and subsequent metabolite quantification. This method allows for bulk analysis of metabolites that contribute to the changing transcriptomic and epigenomic landscapes within experimental conditions. It is applicable to many tissue types and maximizes the amount of information that can be extracted from samples that are not readily available. As the contribution of metabolism to the establishment of cellular identity via epigenetic modifications becomes more appreciated, techniques that allow for identifying the contribution of metabolites in specific cell types are timely and necessary.

Background: Assay of Transposase Accessible Chromatin Sequencing (ATAC-seq) is a high-throughput sequencing technique that detects open chromatin regions across the genome. These regions are critical in facilitating transcription factor binding and subsequent gene expression. Herein, we utilized ATAC-seq to identify key molecular targets regulating the development and progression of hepatocellular carcinoma (HCC) and elucidate the underlying mechanisms., Methods: We first compared chromatin accessibility profiles between HCC and normal tissues. Subsequently, RNA-seq data was employed to identify differentially expressed genes (DEGs). Integrating ATAC-seq and RNA-seq data allowed the identification of transcription factors and their putative target genes associated with differentially accessible regions (DARs). Finally, functional experiments were conducted to investigate the effects of the identified regulatory factors and corresponding targets on HCC cell proliferation and migration., Results: Enrichment analysis of DARs between HCC and adjacent normal tissues revealed distinct signaling pathways and regulatory factors. Upregulated DARs in HCC were enriched in genes related to the MAPK and FoxO signaling pathways and associated with transcription factor families like ETS and AP-1. Conversely, downregulated DARs were associated with the TGF-β, cAMP, and p53 signaling pathways and the CTCF family. Integration of the datasets revealed a positive correlation between specific DARs and DEGs. Notably, PRPF3 emerged as a gene associated with DARs in HCC, and functional assays demonstrated its ability to promote HCC cell proliferation and migration. To the best of our knowledge, this is the first report highlighting the oncogenic role of PRPF3 in HCC. Furthermore, ZNF93 expression positively correlated with PRPF3, and ChIP-seq data indicated its potential role as a transcription factor regulating PRPF3 by binding to its promoter region., Conclusion: This study provides a comprehensive analysis of the epigenetic landscape in HCC, encompassing both chromatin accessibility and the transcriptome. Our findings reveal that ZNF93 promotes the proliferation and motility of HCC cells through transcriptional regulation of a novel oncogene, PRPF3., (© 2024. The Author(s).)

Identifying cell-type-specific 3D chromatin interactions between regulatory elements can help decipher gene regulation and interpret disease-associated non-coding variants. However, achieving this resolution with current 3D genomics technologies is often infeasible given limited input cell numbers. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps, including regulatory interactions, from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility across metacells, and a CTCF motif track as inputs and employs a lightweight architecture to train on standard GPUs. Trained on paired scATAC-seq and Hi-C data in human samples, ChromaFold accurately predicts the 3D contact map and peak-level interactions across diverse human and mouse test cell types. Compared to leading contact map prediction models that use ATAC-seq and CTCF ChIP-seq, ChromaFold achieves state-of-the-art performance using only scATAC-seq. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations., (© 2024. The Author(s).)

Summary: "quaqc" allows for ATAC-seq-specific quality control and read filtering of NGS data with minimal processing time and extremely low memory overhead. An efficient scaling implementation allows for a wide range of use cases, from processing individual samples processed on personal laptops to handling thousands of samples processed in parallel on compute clusters. The helper R package "quaqcr" allows for interactive program execution and exploration of results., Availability and Implementation: Source code and documentation are freely available for download from https://github.com/bjmt/quaqc and https://github.com/bjmt/quaqcr under the GPLv3 license. "quaqc" is implemented in C and has been tested on both macOS and Linux. The "quaqcr" helper package only requires the R programming language. Fixed versions of the programs and code associated with this manuscript can be found at https://zenodo.org/records/13833437., (© The Author(s) 2024. Published by Oxford University Press.)

Chromatin immunoprecipitation and sequencing (vs ChIP-seq) is an essential tool for epigenetic and molecular genetic studies. Although being routinely used, ChIP-seq is expensive, requires grams of plant materials, and is challenging for samples that enrich fatty acids such as seeds. Here, we developed an Ultrasensitive Plant ChIP-seq (UP-ChIP) method based on native ChIP-seq combined with Tn5 tagmentation-based library construction strategy. UP-ChIP is generally applicable for profiling both histone modification and Pol II in a wide range of plant samples, such as a single Arabidopsis seedling, a few Arabidopsis seeds, and sorted nuclei. Compared with conventional ChIP-seq, UP-ChIP is much less labor intensive and only consumes 1 μg of antibody and 10 μl of Protein-A/G conjugated beads for each IP and can work effectively with the amount of starting material down to a few milligrams. By performing UP-ChIP in various conditions and genotypes, we showed that UP-ChIP is highly reliable, sensitive, and quantitative for studying histone modifications. Detailed UP-ChIP protocol is provided. We recommend UP-ChIP as an alternative to traditional ChIP-seq for profiling histone modifications and Pol II, offering the advantages of reduced labor intensity, decreased costs, and low-sample input., (© 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation.)

Histone modifications can regulate transcription epigenetically by marking specific genomic loci, which can be mapped using chromatin immunoprecipitation sequencing (ChIP-seq). Here we present QHistone, a predictive database of 1534 ChIP-seqs from 27 histone modifications in Arabidopsis, offering three key functionalities. Firstly, QHistone employs machine learning to predict the epigenomic profile of a query protein, characterized by its most associated histone modifications, and uses these modifications to infer the protein's role in transcriptional regulation. Secondly, it predicts synergistic regulatory activities between two proteins by comparing their profiles. Lastly, it detects previously unexplored co-regulating protein pairs by screening all known proteins. QHistone accurately identifies histone modifications associated with specific known proteins, and allows users to computationally validate their results using gene expression data from various plant tissues. These functions demonstrate an useful approach to utilizing epigenome data for gene regulation analysis, making QHistone a valuable resource for the scientific community ( https://qhistone.paoyang.ipmb.sinica.edu.tw )., (© 2024. The Author(s).)

ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (a chromVAR-limma workflow with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs-in our case NFkB-at the protein level., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gerbaldo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Goat milk is abundant in nutrients, particularly in milk fats, which confer health benefits to humans. Exploring the regulatory mechanism of fatty acid synthesis is highly important to understand milk composition manipulation. In this study, we used chromatin immunoprecipitation sequencing (ChIP-seq) on goat mammary glands at different lactation stages which revealed a novel lactation regulatory factor: cell death-inducing DFFA-like effector B (CIDEB). RT-qPCR results revealed that CIDEB was significantly upregulated during lactation in dairy goats. CIDEB overexpression significantly increased the expression levels of genes involved in fatty acid synthesis ( ACACA , SCD1 , p < 0.05; ELOVL6 , p < 0.01), lipid droplet formation ( XDH , p < 0.05), and triacylglycerol (TAG) synthesis ( DGAT1 , p < 0.05; GPAM , p < 0.01) in goat mammary epithelial cells (GMECs). The contents of lipid droplets, TAG, and cholesterol were increased ( p < 0.05) in CIDEB-overexpressing GMECs, and knockdown of CIDEB led to the opposite results. In addition, CIDEB knockdown significantly decreased the proportion of C16:0 and total C18:2. Luciferase reporter assays indicated that X-box binding protein 1 (XBP1) promoted CIDEB transcription via XBP1 binding sites located in the CIDEB promoter. Furthermore, CIDEB knockdown attenuated the stimulatory effect of XBP1 on lipid droplet accumulation. Collectively, these findings elucidate the critical regulatory roles of CIDEB in milk fat synthesis, thus providing new insights into improving the quality of goat milk.

Differential accessibility (DA) analysis of single-cell epigenomics data enables the discovery of regulatory programs that establish cell type identity and steer responses to physiological and pathophysiological perturbations. While many statistical methods to identify DA regions have been developed, the principles that determine the performance of these methods remain unclear. As a result, there is no consensus on the most appropriate statistical methods for DA analysis of single-cell epigenomics data. Here, we present a systematic evaluation of statistical methods that have been applied to identify DA regions in single-cell ATAC-seq (scATAC-seq) data. We leverage a compendium of scATAC-seq experiments with matching bulk ATAC-seq or scRNA-seq in order to assess the accuracy, bias, robustness, and scalability of each statistical method. The structure of our experiments also provides the opportunity to define best practices for the analysis of scATAC-seq data beyond DA itself. We leverage this understanding to develop an R package implementing these best practices., (© 2024. The Author(s).)

Genome-wide nucleosome profiles are predominantly characterized using MNase-seq, which involves extensive MNase digestion and size selection to enrich for mononucleosome-sized fragments. Most available MNase-seq analysis packages assume that nucleosomes uniformly protect 147 bp DNA fragments. However, some nucleosomes with atypical histone or chemical compositions protect shorter lengths of DNA. The rigid assumptions imposed by current nucleosome analysis packages potentially prevent investigators from understanding the regulatory roles played by atypical nucleosomes. To enable the characterization of different nucleosome types from MNase-seq data, we introduce the size-based expectation maximization (SEM) nucleosome-calling package. SEM employs a hierarchical Gaussian mixture model to estimate nucleosome positions and subtypes. Nucleosome subtypes are automatically identified based on the distribution of protected DNA fragments. Benchmark analysis indicates that SEM is on par with existing packages in terms of standard nucleosome-calling accuracy metrics, while uniquely providing the ability to characterize nucleosome subtype identities. Applying SEM to a low-dose MNase-H2B-ChIP-seq data set from mouse embryonic stem cells, we identified three nucleosome types: short-fragment nucleosomes, canonical nucleosomes, and di-nucleosomes. Short-fragment nucleosomes can be divided further into two subtypes based on their chromatin accessibility. Short-fragment nucleosomes in accessible regions exhibit high MNase sensitivity and are enriched at transcription start sites (TSSs) and CTCF peaks, similar to previously reported "fragile nucleosomes." These SEM-defined accessible short-fragment nucleosomes are found not just in promoters but also in distal regulatory regions. Additional analyses reveal their colocalization with the chromatin remodelers CHD6, CHD8, and EP400. In summary, SEM provides an effective platform for exploration of nonstandard nucleosome subtypes., (© 2024 Yang et al.; Published by Cold Spring Harbor Laboratory Press.)

Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the microenvironment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes., Competing Interests: AG, JR, MF, CJ No competing interests declared, DG has recieved consulting fees from CeCaVa and Gnubiotics, (© 2024, Gabriel et al.)

Mapping genome-wide DNA-protein interactions (DPIs) provides insights into the epigenetic landscape of complex biological systems and elucidates the mechanisms of epigenetic regulation in biological progress. However, current technologies in DPI profiling still suffer from high cell demands, low detection sensitivity, and large reagent consumption. To address these problems, we developed DMF-ChIP-seq that builds on digital microfluidic (DMF) technology to profile genome-wide DPIs in a highly efficient, cost-effective, and user-friendly way. The entire workflow including cell pretreatment, antibody recognition, pA-Tn5 tagmentation, fragment enrichment, and PCR amplification is programmatically manipulated on a single chip. Leveraging closed submicroliter reaction volumes and a superhydrophobic interface, DMF-ChIP-seq presented higher sensitivity in peak enrichment than other current methods, with high accuracy (Pearson Correlation Coefficient (PCC) > 0.86) and high repeatability (PCC > 0.92). Furthermore, DMF-ChIP-seq was capable of processing the samples with as few as 8 cells while maintaining a high signal-to-noise ratio. By applying DMF-ChIP-seq, H3K27ac histone modification of early embryonic cells during differentiation was profiled for the investigation of epigenomic landscape dynamics. With the benefits of high efficiency and sensitivity in DPI analysis, the system provides great promise in studying epigenetic regulation during various biological processes.

Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be enhanced. In this study, we propose MLSNet, a novel deep learning architecture designed specifically to predict TFBSs. MLSNet innovatively integrates multisize convolutional fusion with long short-term memory (LSTM) networks to effectively capture DNA-sparse higher-order sequence features. Further, MLSNet incorporates super token attention and Bi-LSTM to systematically extract and integrate higher-order DNA shape features. Experimental results on 165 ChIP-seq (chromatin immunoprecipitation followed by sequencing) datasets indicate that MLSNet consistently outperforms several state-of-the-art algorithms in the prediction of TFBSs. Specifically, MLSNet reports average metrics: 0.8306 for ACC, 0.8992 for AUROC, and 0.9035 for AUPRC, surpassing the second-best methods by 1.82%, 1.68%, and 1.54%, respectively. This research delineates the effectiveness of combining multi-size convolutional layers with LSTM and DNA shape-based features in enhancing predictive accuracy. Moreover, this study comprehensively assesses the variability in model performance across different cell lines and transcription factors. The source code of MLSNet is available at https://github.com/minghaidea/MLSNet., (© The Author(s) 2024. Published by Oxford University Press.)

Simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq) profiles transcriptomics and chromatin accessibility in the same cells at high throughput. Here, we present a protocol for multimodal profiling of human kidneys with SHARE-seq. We describe steps for processing fixed nuclei for SHARE-seq split-pool barcoding and library preparation. We also detail how to determine the optimal working concentration of Tn5 transposase for transposition and tagmentation. This protocol allows researchers to generate large-scale single-cell multiomics data at low reagent cost. For complete details on the use and execution of this protocol, please refer to Li et al. 1 ., Competing Interests: Declaration of interests B.D.H. is a consultant for Janssen Research & Development, LLC, Pfizer, and Chinook Therapeutics, holds equity in Chinook Therapeutics, and grants funding from Chinook Therapeutics, Pfizer, and Janssen Research & Development, LLC; all interests are unrelated to the current work., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Efficient and reliable profiling methods are essential to study epigenetics. Tn5, one of the first identified prokaryotic transposases with high DNA-binding and tagmentation efficiency, is widely adopted in different genomic and epigenomic protocols for high-throughputly exploring the genome and epigenome. Based on Tn5, the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and the Cleavage Under Targets and Tagmentation (CUT&Tag) were developed to measure chromatin accessibility and detect DNA-protein interactions. These methodologies can be applied to large amounts of biological samples with low-input levels, such as rare tissues, embryos, and sorted single cells. However, fast and proper processing of these epigenomic data has become a bottleneck because massive data production continues to increase quickly. Furthermore, inappropriate data analysis can generate biased or misleading conclusions. Therefore, it is essential to evaluate the performance of Tn5-based ATAC-seq and CUT&Tag data processing bioinformatics tools, many of which were developed mostly for analyzing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here, we conducted a comprehensive benchmarking analysis to evaluate the performance of eight popular software for processing ATAC-seq and CUT&Tag data. We compared the sensitivity, specificity, and peak width distribution for both narrow-type and broad-type peak calling. We also tested the influence of the availability of control IgG input in CUT&Tag data analysis. Finally, we evaluated the differential analysis strategies commonly used for analyzing the CUT&Tag data. Our study provided comprehensive guidance for selecting bioinformatics tools and recommended analysis strategies, which were implemented into Docker/Singularity images for streamlined data analysis., (© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.)

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is crucial for profiling histone modifications and transcription factor binding throughout the genome. However, its application in economically important plant organs (EIPOs) such as seeds, fruits and flowers is challenging due to their sturdy cell walls and complex constituents. Here we present advanced ChIP (aChIP), an optimized method that efficiently isolates chromatin from plant tissues while simultaneously removing cell walls and cellular constituents. aChIP precisely profiles histone modifications in all 14 tested EIPOs and identifies transcription factor and chromatin-modifying enzyme binding sites. In addition, aChIP enhances ChIP efficiency, revealing numerous novel modified sites compared with previous methods in vegetative tissues. aChIP reveals the histone modification landscape for rapeseed dry seeds, highlighting the intricate roles of chromatin dynamics during seed dormancy and germination. Altogether, aChIP is a powerful, efficient and sensitive approach for comprehensive chromatin profiling in virtually all plant tissues, especially in EIPOs., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

A pervasive question in biological research studying gene regulation, chromatin structure, or genomics is where, and to what extent, does a signal of interest arise genome-wide? This question is addressed using a variety of methods relying on high-throughput sequencing data as their final output, including ChIP-seq for protein-DNA interactions, 1 GapR-seq for measuring supercoiling, 2 and HBD-seq or DRIP-seq for R-loop positioning. 3,4 Current computational methods to calculate genome-wide enrichment of the signal of interest usually do not properly handle the count-based nature of sequencing data, they often do not make use of the local correlation structure of sequencing data, and they do not apply any regularization of enrichment estimates. This can result in unrealistic estimates of the true underlying biological enrichment of interest, unrealistically low estimates of confidence in point estimates of enrichment (or no estimates of confidence at all), unrealistic gyrations in enrichment estimates at very close (<10 bp) genomic loci due to noise inherent in sequencing data, and in a multiple-hypothesis testing problem during interpretation of genome-wide enrichment estimates. We developed a tool called Enricherator to infer genome-wide enrichments from sequencing count data. Enricherator uses the variational Bayes algorithm to fit a generalized linear model to sequencing count data and to sample from the approximate posterior distribution of enrichment estimates (https://github.com/jwschroeder3/enricherator). Enrichments inferred by Enricherator more precisely identify known binding sites in cases where low coverage between binding sites leads to false-positive peak calls in these noisy regions of the genome; these benefits extend to published datasets., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: P.L. Freddolino is an Editorial Board Member/Editor-in-Chief/Associate Editor/Guest Editor for Scientific Reports and EcoSal Plus; neither organization was involved in the editorial review or the decision to publish this article. P.L. Freddolino is on the Scientific Advisory Board and is a Consultant for CircNova, Inc; CircNova provided no financial support for this work, and was not involved in any way in the performance of the research, manuscript preparation, editorial review, or decision to publish this article. J.W. Schroeder declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Cis-regulatory elements (CREs) are pivotal in orchestrating gene expression throughout diverse biological systems. Accurate identification and in-depth characterization of functional CREs are crucial for decoding gene regulation networks during cellular processes. In this study, we develop Kethoxal-Assisted Single-stranded DNA Assay for Transposase-Accessible Chromatin with Sequencing (KAS-ATAC-seq) to quantitatively analyze the transcriptional activity of CREs. A main advantage of KAS-ATAC-seq lies in its precise measurement of ssDNA levels within both proximal and distal ATAC-seq peaks, enabling the identification of transcriptional regulatory sequences. This feature is particularly adept at defining Single-Stranded Transcribing Enhancers (SSTEs). SSTEs are highly enriched with nascent RNAs and specific transcription factors (TFs) binding sites that define cellular identity. Moreover, KAS-ATAC-seq provides a detailed characterization and functional implications of various SSTE subtypes. Our analysis of CREs during mouse neural differentiation demonstrates that KAS-ATAC-seq can effectively identify immediate-early activated CREs in response to retinoic acid (RA) treatment. Our findings indicate that KAS-ATAC-seq provides more precise annotation of functional CREs in transcription. Future applications of KAS-ATAC-seq would help elucidate the intricate dynamics of gene regulation in diverse biological processes., (© 2024. The Author(s).)

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data., (© 2024. The Author(s).)

Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility (DA) analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study that are most enriched in GO terms related to neurogenesis and the clinical phenotype of AD, and AD-associated GWAS SNPs., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Spatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations. spaVAE introduces a hybrid embedding combining a Gaussian process prior with a Gaussian prior to explicitly capture spatial correlations among spots. It then optimizes the parameters of deep neural networks to approximate the distributions underlying the SRT data. With the approximated distributions, spaVAE can contribute to several analytical tasks that are essential for SRT data analysis, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement and identification of spatially variable genes. Moreover, we have extended spaVAE to spaPeakVAE and spaMultiVAE to characterize spatial ATAC-seq (assay for transposase-accessible chromatin using sequencing) data and spatial multi-omics data, respectively., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Transposable elements (TEs) and other repetitive regions have been shown to contain gene regulatory elements, including transcription factor binding sites. However, regulatory elements harbored by repeats have proven difficult to characterize using short-read sequencing assays such as ChIP-seq or ATAC-seq. Most regulatory genomics analysis pipelines discard "multimapped" reads that align equally well to multiple genomic locations. Because multimapped reads arise predominantly from repeats, current analysis pipelines fail to detect a substantial portion of regulatory events that occur in repetitive regions. To address this shortcoming, we developed Allo, a new approach to allocate multimapped reads in an efficient, accurate, and user-friendly manner. Allo combines probabilistic mapping of multimapped reads with a convolutional neural network that recognizes the read distribution features of potential peaks, offering enhanced accuracy in multimapping read assignment. Allo also provides read-level output in the form of a corrected alignment file, making it compatible with existing regulatory genomics analysis pipelines and downstream peak-finders. In a demonstration application on CTCF ChIP-seq data, we show that Allo results in the discovery of thousands of new CTCF peaks. Many of these peaks contain the expected cognate motif and/or serve as TAD boundaries. We additionally apply Allo to a diverse collection of ENCODE ChIP-seq data sets, resulting in multiple previously unidentified interactions between transcription factors and repetitive element families. Finally, we show that Allo may be particularly beneficial in identifying ChIP-seq peaks at centromeres, near segmentally duplicated genes, and in younger TEs, enabling new regulatory analyses in these regions., (© 2024 Morrissey et al.; Published by Cold Spring Harbor Laboratory Press.)

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore, we present Cloud ATAC, an open-source, cloud-based interactive framework with a scalable, flexible, and streamlined analysis framework based on the best practices approach for pooled-cell and single-cell ATAC-seq data. These frameworks use on-demand computational power and memory, scalability, and a secure and compliant environment provided by the Google Cloud. Additionally, we leverage Jupyter Notebook's interactive computing platform that combines live code, tutorials, narrative text, flashcards, quizzes, and custom visualizations to enhance learning and analysis. Further, leveraging GPU instances has significantly improved the run-time of the single-cell framework. The source codes and data are publicly available through NIH Cloud lab https://github.com/NIGMS/ATAC-Seq-and-Single-Cell-ATAC-Seq-Analysis. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses., (© The Author(s) 2024. Published by Oxford University Press.)

ChIP-Atlas (https://chip-atlas.org/) presents a suite of data-mining tools for analyzing epigenomic landscapes, powered by the comprehensive integration of over 376 000 public ChIP-seq, ATAC-seq, DNase-seq and Bisulfite-seq experiments from six representative model organisms. To unravel the intricacies of chromatin architecture that mediates the regulome-initiated generation of transcriptional and phenotypic diversity within cells, we report ChIP-Atlas 3.0 that enhances clarity by incorporating additional tracks for genomic and epigenomic features within a newly consolidated 'annotation track' section. The tracks include chromosomal conformation (Hi-C and eQTL datasets), transcriptional regulatory elements (ChromHMM and FANTOM5 enhancers), and genomic variants associated with diseases and phenotypes (GWAS SNPs and ClinVar variants). These annotation tracks are easily accessible alongside other experimental tracks, facilitating better elucidation of chromatin architecture underlying the diversification of transcriptional and phenotypic traits. Furthermore, 'Diff Analysis,' a new online tool, compares the query epigenome data to identify differentially bound, accessible, and methylated regions using ChIP-seq, ATAC-seq and DNase-seq, and Bisulfite-seq datasets, respectively. The integration of annotation tracks and the Diff Analysis tool, coupled with continuous data expansion, renders ChIP-Atlas 3.0 a robust resource for mining the landscape of transcriptional regulatory mechanisms, thereby offering valuable perspectives, particularly for genetic disease research and drug discovery., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.

Zn 2+ is an essential metal required by approximately 850 human transcription factors. How these proteins acquire their essential Zn 2+ cofactor and whether they are sensitive to changes in the labile Zn 2+ pool in cells remain open questions. Using ATAC-seq to profile regions of accessible chromatin coupled with transcription factor enrichment analysis, we examined how increases and decreases in the labile zinc pool affect chromatin accessibility and transcription factor enrichment. We found 685 transcription factor motifs were differentially enriched, corresponding to 507 unique transcription factors. The pattern of perturbation and the types of transcription factors were notably different at promoters versus intergenic regions, with zinc-finger transcription factors strongly enriched in intergenic regions in elevated Zn 2+ To test whether ATAC-seq and transcription factor enrichment analysis predictions correlate with changes in transcription factor binding, we used ChIP-qPCR to profile six p53 binding sites. We found that for five of the six targets, p53 binding correlates with the local accessibility determined by ATAC-seq. These results demonstrate that changes in labile zinc alter chromatin accessibility and transcription factor binding to DNA., (© 2024 Ocampo et al.)

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios., (© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.)

The ever decreasing cost of Next-Generation Sequencing coupled with the emergence of efficient and reproducible analysis pipelines has rendered genomic methods more accessible. However, downstream analyses are basic or missing in most workflows, creating a significant barrier for non-bioinformaticians. To help close this gap, we developed Cactus, an end-to-end pipeline for analyzing ATAC-Seq and mRNA-Seq data, either separately or jointly. Its Nextflow-, container-, and virtual environment-based architecture ensures efficient and reproducible analyses. Cactus preprocesses raw reads, conducts differential analyses between conditions, and performs enrichment analyses in various databases, including DNA-binding motifs, ChIP-Seq binding sites, chromatin states, and ontologies. We demonstrate the utility of Cactus in a multi-modal and multi-species case study as well as by showcasing its unique capabilities as compared to other ATAC-Seq pipelines. In conclusion, Cactus can assist researchers in gaining comprehensive insights from chromatin accessibility and gene expression data in a quick, user-friendly, and reproducible manner., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Genome sequence variants affecting complex traits (quantitative trait loci, QTL) are enriched in functional regions of the genome, such as those marked by certain histone modifications. These variants are believed to influence gene expression. However, due to the linkage disequilibrium among nearby variants, pinpointing the precise location of QTL is challenging. We aimed to identify allele-specific binding (ASB) QTL (asbQTL) that cause variation in the level of histone modification, as measured by the height of peaks assayed by ChIP-seq (chromatin immunoprecipitation sequencing). We identified DNA sequences that predict the difference between alleles in ChIP-seq peak height in H3K4me3 and H3K27ac histone modifications in the mammary glands of cows., Results: We used a gapped k-mer support vector machine, a novel best linear unbiased prediction model, and a multiple linear regression model that combines the other two approaches to predict variant impacts on peak height. For each method, a subset of 1000 sites with the highest magnitude of predicted ASB was considered as candidate asbQTL. The accuracy of this prediction was measured by the proportion where the predicted direction matched the observed direction. Prediction accuracy ranged between 0.59 and 0.74, suggesting that these 1000 sites are enriched for asbQTL. Using independent data, we investigated functional enrichment in the candidate asbQTL set and three control groups, including non-causal ASB sites, non-ASB variants under a peak, and SNPs (single nucleotide polymorphisms) not under a peak. For H3K4me3, a higher proportion of the candidate asbQTL were confirmed as ASB when compared to the non-causal ASB sites (P < 0.01). However, these candidate asbQTL did not enrich for the other annotations, including expression QTL (eQTL), allele-specific expression QTL (aseQTL) and sites conserved across mammals (P > 0.05)., Conclusions: We identified putatively causal sites for asbQTL using the DNA sequence surrounding these sites. Our results suggest that many sites influencing histone modifications may not directly affect gene expression. However, it is important to acknowledge that distinguishing between putative causal ASB sites and other non-causal ASB sites in high linkage disequilibrium with the causal sites regarding their impact on gene expression may be challenging due to limitations in statistical power., (© 2024. The Author(s).)

Motivation: Understanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to learn to predict enhancer-promoter (EP) relationships in a data-driven manner., Results: We applied machine learning to one of the largest enhancer perturbation studies integrated with transcription factor (TF) and histone modification ChIP-seq. The results uncovered a discrepancy in the prediction of genome-wide data compared to data from targeted experiments. Relative strength of contact was important for prediction, confirming the basic principle of EP regulation. Novel features such as the density of the enhancers/promoters in the genomic region was found to be important, highlighting our lack of understanding on how other elements in the region contribute to the regulation. Several TF peaks were identified that improved the prediction by identifying the negatives and reducing False Positives. In summary, integrating genomic assays with enhancer perturbation studies increased the accuracy of the model, and provided novel insights into the understanding of enhancer-driven transcription., Availability and Implementation: The trained models, data, and the source code are available at http://doi.org/10.5281/zenodo.11290386 and https://github.com/HanLabUNLV/sleps., (© The Author(s) 2024. Published by Oxford University Press.)

Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Standard scATAC sequencing (scATAC-seq) analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. Here we present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space, to address this limitation. We show that CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and can score transcription factor activities in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia., (© 2024. The Author(s).)