Transient chemical-mediated epigenetic modulation confers unrestricted lineage potential on human primed pluripotent stem cells

Human primed pluripotent stem cells are capable of generating all the embryonic lineages. However, their extraembryonic trophectoderm potentials are limited. It remains unclear how to expand their developmental potential to trophectoderm lineages. Here we show that transient treatment with a cocktail of small molecule epigenetic modulators imparts trophectoderm lineage potentials to human primed pluripotent stem cells while preserving their embryonic potential. These chemically treated cells can generate trophectoderm-like cells and downstream trophoblast stem cells, diverging into syncytiotrophoblast and extravillous trophoblast lineages. Transcriptomic and CUT&Tag analyses reveal that these induced cells share transcriptional profiles with in vivo trophectoderm and cytotrophoblast, and exhibit reduced H3K27me3 modification at gene loci specific to trophoblast lineages compared with primed pluripotent cells. Mechanistic exploration highlighted the critical roles of epigenetic modulators HDAC2, EZH1/2, and KDM5s in the activation of trophoblast lineage potential. Our findings demonstrate that transient epigenetic resetting activates unrestricted lineage potential in human primed pluripotent stem cells, and offer new mechanistic insights into human trophoblast lineage specification and in vitro models for studying placental development and related disorders.

The chromatin accessibility landscape during early maize seed development

Cis-regulatory elements (CREs) are enriched in accessible chromatin regions (ACRs) of eukaryotes. Despite extensive research on genome-wide ACRs in various plant tissues, the global impact of these changes on developmental processes in maize seeds remains poorly understood. In this study, we employed the assay for transposase-accessible chromatin sequencing (ATAC-seq) to reveal the chromatin accessibility profile throughout the genome during the early stages of maize seed development. We identified a total of 37 952 to 59 887 high-quality ACRs in maize seeds at 0 to 8 days after pollination (DAP). Furthermore, we examined the correlation between the identified ACRs and gene expression. We observed a positive correlation between the open degree of promoter-ACRs and the expression of most genes. Moreover, we identified binding footprints of numerous transcription factors (TFs) within chromatin accessibility regions and revealed key TF families involved in different stages. Through the footprints of accessible chromatin regions, we predicted transcription factor regulatory networks during early maize embryo development. Additionally, we discovered that DNA sequence diversity was notably reduced at ACRs, yet trait-associated SNPs were more likely to be located within ACRs. We edited the ACR containing the trait-associated SNP of NKD1. Both NKD1pro-1 and NKD1pro-2 showed phenotypes corresponding to the trait-associated SNP. Our results suggest that alterations in chromatin accessibility play a crucial role in maize seed development and highlight the potential contribution of open chromatin regions to advancements in maize breeding.

Deciphering the Transcriptional Regulatory Network Governing Starch and Storage Protein Biosynthesis in Wheat for Breeding Improvement

Starch and seed storage protein (SSP) composition profoundly impact wheat grain yield and quality. To unveil regulatory mechanisms governing their biosynthesis, transcriptome, and epigenome profiling is conducted across key endosperm developmental stages, revealing that chromatin accessibility, H3K27ac, and H3K27me3 collectively regulate SSP and starch genes with varying impact. Population transcriptome and phenotype analyses highlight accessible promoter regions' crucial role as a genetic variation resource, influencing grain yield and quality in a core collection of wheat accessions. Integration of time-serial RNA-seq and ATAC-seq enables the construction of a hierarchical transcriptional regulatory network governing starch and SSP biosynthesis, identifying 42 high-confidence novel candidates. These candidates exhibit overlap with genetic regions associated with grain size and quality traits, and their functional significance is validated through expression-phenotype association analysis among wheat accessions and loss-of-function mutants. Functional analysis of wheat abscisic acid insensitive 3-A1 (TaABI3-A1) with genome editing knock-out lines demonstrates its role in promoting SSP accumulation while repressing starch biosynthesis through transcriptional regulation. Excellent TaABI3-A1Hap1 with enhanced grain weight is selected during the breeding process in China, linked to altered expression levels. This study unveils key regulators, advancing understanding of SSP and starch biosynthesis regulation and contributing to breeding enhancement.

A genome scale transcriptional regulatory model of the human placenta

Gene regulation is essential to placental function and fetal development. We built a genome-scale transcriptional regulatory network (TRN) of the human placenta using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out-of-sample R2 greater than 0.25 for 73% of target genes. We performed siRNA knockdowns of four TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for three of the four TFs. Our final model contained 113,158 interactions across 391 TFs and 7712 target genes and is publicly available. We identified 29 TFs which were significantly enriched as regulators for genes previously associated with preterm birth, and eight of these TFs were decreased in preterm placentas.

Systematic identification of wheat spike developmental regulators by integrated multi-omics, transcriptional network, GWAS, and genetic analyses

The spike architecture of wheat plays a crucial role in determining grain number, making it a key trait for optimization in wheat breeding programs. In this study, we used a multi-omic approach to analyze the transcriptome and epigenome profiles of the young spike at eight developmental stages, revealing coordinated changes in chromatin accessibility and H3K27me3 abundance during the flowering transition. We constructed a core transcriptional regulatory network (TRN) that drives wheat spike formation and experimentally validated a multi-layer regulatory module involving TaSPL15, TaAGLG1, and TaFUL2. By integrating the TRN with genome-wide association studies, we identified 227 transcription factors, including 42 with known functions and 185 with unknown functions. Further investigation of 61 novel transcription factors using multiple homozygous mutant lines revealed 36 transcription factors that regulate spike architecture or flowering time, such as TaMYC2-A1, TaMYB30-A1, and TaWRKY37-A1. Of particular interest, TaMYB30-A1, downstream of and repressed by WFZP, was found to regulate fertile spikelet number. Notably, the excellent haplotype of TaMYB30-A1, which contains a C allele at the WFZP binding site, was enriched during wheat breeding improvement in China, leading to improved agronomic traits. Finally, we constructed a free and open access Wheat Spike Multi-Omic Database (http://39.98.48.156:8800/#/). Our study identifies novel and high-confidence regulators and offers an effective strategy for dissecting the genetic basis of wheat spike development, with practical value for wheat breeding.

The contributions of DNA accessibility and transcription factor occupancy to enhancer activity during cellular differentiation

During gene regulation, DNA accessibility is thought to limit the availability of transcription factor (TF) binding sites, while TFs can increase DNA accessibility to recruit additional factors that upregulate gene expression. Given this interplay, the causative regulatory events in the modulation of gene expression remain unknown for the vast majority of genes. We utilized deeply sequenced ATAC-Seq data and site-specific knock-in reporter genes to investigate the relationship between the binding-site resolution dynamics of DNA accessibility and the expression dynamics of the enhancers of Cebpa during macrophage-neutrophil differentiation. While the enhancers upregulate reporter expression during the earliest stages of differentiation, there is little corresponding increase in their total accessibility. Conversely, total accessibility peaks during the last stages of differentiation without any increase in enhancer activity. The accessibility of positions neighboring C/EBP-family TF binding sites, which indicates TF occupancy, does increase significantly during early differentiation, showing that the early upregulation of enhancer activity is driven by TF binding. These results imply that a generalized increase in DNA accessibility is not sufficient, and binding by enhancer-specific TFs is necessary, for the upregulation of gene expression. Additionally, high-coverage ATAC-Seq combined with time-series expression data can infer the sequence of regulatory events at binding-site resolution.

The contributions of DNA accessibility and transcription factor occupancy to enhancer activity during cellular differentiation

The upregulation of gene expression by enhancers depends upon the interplay between the binding of sequence-specific transcription factors (TFs) and DNA accessibility. DNA accessibility is thought to limit the ability of TFs to bind to their sites, while TFs can increase accessibility to recruit additional factors that upregulate gene expression. Given this interplay, the causative regulatory events underlying the modulation of gene expression during cellular differentiation remain unknown for the vast majority of genes. We investigated the binding-site resolution dynamics of DNA accessibility and the expression dynamics of the enhancers of an important neutrophil gene, Cebpa, during macrophage-neutrophil differentiation. Reporter genes were integrated in a site-specific manner in PUER cells, which are progenitors that can be differentiated into neutrophils or macrophages in vitro by activating the pan-leukocyte TF PU.1. Time series data show that two enhancers upregulate reporter expression during the first 48 hours of neutrophil differentiation. Surprisingly, there is little or no increase in the total accessibility, measured by ATAC-Seq, of the enhancers during the same time period. Conversely, total accessibility peaks 96 hrs after PU.1 activation-consistent with its role as a pioneer-but the enhancers do not upregulate gene expression. Combining deeply sequenced ATAC-Seq data with a new bias-correction method allowed the profiling of accessibility at single-nucleotide resolution and revealed protected regions in the enhancers that match all previously characterized TF binding sites and ChIP-Seq data. Although the accessibility of most positions does not change during early differentiation, that of positions neighboring TF binding sites, an indicator of TF occupancy, did increase significantly. The localized accessibility changes are limited to nucleotides neighboring C/EBP-family TF binding sites, showing that the upregulation of enhancer activity during early differentiation is driven by C/EBP-family TF binding. These results show that increasing the total accessibility of enhancers is not sufficient for upregulating their activity and other events such as TF binding are necessary for upregulation. Also, TF binding can cause upregulation without a perceptible increase in total accessibility. Finally, this study demonstrates the feasibility of comprehensively mapping individual TF binding sites as footprints using high coverage ATAC-Seq and inferring the sequence of events in gene regulation by combining with time-series gene expression data.

Transcriptional activation of Jun and Fos members of the AP-1 complex is a conserved signature of immune aging that contributes to inflammaging

Diverse mouse strains have different health and life spans, mimicking the diversity among humans. To capture conserved aging signatures, we studied long-lived C57BL/6J and short-lived NZO/HILtJ mouse strains by profiling transcriptomes and epigenomes of immune cells from peripheral blood and the spleen from young and old mice. Transcriptional activation of the AP-1 transcription factor complex, particularly Fos, Junb, and Jun genes, was the most significant and conserved aging signature across tissues and strains. ATAC-seq data analyses showed that the chromatin around these genes was more accessible with age and there were significantly more binding sites for these TFs with age across all studied tissues, targeting pro-inflammatory molecules including Il6. Age-related increases in binding sites of JUN and FOS factors were also conserved in human peripheral blood ATAC-seq data. Single-cell RNA-seq data from the mouse aging cell atlas Tabula Muris Senis showed that the expression of these genes increased with age in B, T, NK cells, and macrophages, with macrophages from old mice expressing these molecules more abundantly than other cells. Functional data showed that upon myeloid cell activation via poly(I:C), the levels of JUN protein and its binding activity increased more significantly in spleen cells from old compared to young mice. In addition, upon activation, old cells produced more IL6 compared to young cells. In sum, we showed that the aging-related transcriptional activation of Jun and Fos family members in AP-1 complex is conserved across immune tissues and long- and short-living mouse strains, possibly contributing to increased inflammation with age.

PlantCADB: A Comprehensive Plant Chromatin Accessibility Database

Chromatin accessibility landscapes are essential for detecting regulatory elements, illustrating the corresponding regulatory networks, and, ultimately, understanding the molecular basis underlying key biological processes. With the advancement of sequencing technologies, a large volume of chromatin accessibility data has been accumulated and integrated for humans and other mammals. These data have greatly advanced the study of disease pathogenesis, cancer survival prognosis, and tissue development. To advance the understanding of molecular mechanisms regulating plant key traits and biological processes, we developed a comprehensive plant chromatin accessibility database (PlantCADB) from 649 samples of 37 species. These samples are abiotic stress-related (such as heat, cold, drought, and salt; 159 samples), development-related (232 samples), and/or tissue-specific (376 samples). Overall, 18,339,426 accessible chromatin regions (ACRs) were compiled. These ACRs were annotated with genomic information, associated genes, transcription factor footprint, motif, and single-nucleotide polymorphisms (SNPs). Additionally, PlantCADB provides various tools to visualize ACRs and corresponding annotations. It thus forms an integrated, annotated, and analyzed plant-related chromatin accessibility resource, which can aid in better understanding genetic regulatory networks underlying development, important traits, stress adaptations, and evolution.PlantCADB is freely available at https://bioinfor.nefu.edu.cn/PlantCADB/.

Machine learning dissection of human accelerated regions in primate neurodevelopment

Using machine learning (ML), we interrogated the function of all human-chimpanzee variants in 2,645 human accelerated regions (HARs), finding 43% of HARs have variants with large opposing effects on chromatin state and 14% on neurodevelopmental enhancer activity. This pattern, consistent with compensatory evolution, was confirmed using massively parallel reporter assays in chimpanzee and human neural progenitor cells. The species-specific enhancer activity of HARs was accurately predicted from the presence and absence of transcription factor footprints in each species. Despite these striking cis effects, activity of a given HAR sequence was nearly identical in human and chimpanzee cells. This suggests that HARs did not evolve to compensate for changes in the trans environment but instead altered their ability to bind factors present in both species. Thus, ML prioritized variants with functional effects on human neurodevelopment and revealed an unexpected reason why HARs may have evolved so rapidly.

RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data

BACKGROUND

Massive amounts of data are produced by combining next-generation sequencing with complex biochemistry techniques to characterize regulatory genomics profiles, such as protein-DNA interaction and chromatin accessibility. Interpretation of such high-throughput data typically requires different computation methods. However, existing tools are usually developed for a specific task, which makes it challenging to analyze the data in an integrative manner.

RESULTS

We here describe the Regulatory Genomics Toolbox (RGT), a computational library for the integrative analysis of regulatory genomics data. RGT provides different functionalities to handle genomic signals and regions. Based on that, we developed several tools to perform distinct downstream analyses, including the prediction of transcription factor binding sites using ATAC-seq data, identification of differential peaks from ChIP-seq data, and detection of triple helix mediated RNA and DNA interactions, visualization, and finding an association between distinct regulatory factors.

CONCLUSION

We present here RGT; a framework to facilitate the customization of computational methods to analyze genomic data for specific regulatory genomics problems. RGT is a comprehensive and flexible Python package for analyzing high throughput regulatory genomics data and is available at: https://github.com/CostaLab/reg-gen . The documentation is available at: https://reg-gen.readthedocs.io.

Dynamic chromatin regulatory programs during embryogenesis of hexaploid wheat

BACKGROUND

Plant and animal embryogenesis have conserved and distinct features. Cell fate transitions occur during embryogenesis in both plants and animals. The epigenomic processes regulating plant embryogenesis remain largely elusive.

RESULTS

Here, we elucidate chromatin and transcriptomic dynamics during embryogenesis of the most cultivated crop, hexaploid wheat. Time-series analysis reveals stage-specific and proximal-distal distinct chromatin accessibility and dynamics concordant with transcriptome changes. Following fertilization, the remodeling kinetics of H3K4me3, H3K27ac, and H3K27me3 differ from that in mammals, highlighting considerable species-specific epigenomic dynamics during zygotic genome activation. Polycomb repressive complex 2 (PRC2)-mediated H3K27me3 deposition is important for embryo establishment. Later H3K27ac, H3K27me3, and chromatin accessibility undergo dramatic remodeling to establish a permissive chromatin environment facilitating the access of transcription factors to cis-elements for fate patterning. Embryonic maturation is characterized by increasing H3K27me3 and decreasing chromatin accessibility, which likely participates in restricting totipotency while preventing extensive organogenesis. Finally, epigenomic signatures are correlated with biased expression among homeolog triads and divergent expression after polyploidization, revealing an epigenomic contributor to subgenome diversification in an allohexaploid genome.

CONCLUSIONS

Collectively, we present an invaluable resource for comparative and mechanistic analysis of the epigenomic regulation of crop embryogenesis.

TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors

BACKGROUND

Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results.

RESULTS

We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences.

CONCLUSION

TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

Multi-omics profiling visualizes dynamics of cardiac development and functions

Cardiogenesis is a tightly regulated dynamic process through a continuum of differentiation and proliferation events. Key factors and pathways governing this process remain incompletely understood. Here, we investigate mice hearts from embryonic day 10.5 to postnatal week 8 and dissect developmental changes in phosphoproteome-, proteome-, metabolome-, and transcriptome-encompassing cardiogenesis and cardiac maturation. We identify mitogen-activated protein kinases as core kinases involved in transcriptional regulation by mediating the phosphorylation of chromatin remodeling proteins during early cardiogenesis. We construct the reciprocal regulatory network of transcription factors (TFs) and identify a series of TFs controlling early cardiogenesis involved in cycling-dependent proliferation. After birth, we identify cardiac resident macrophages with high arachidonic acid metabolism activities likely involved in the clearance of injured apoptotic cardiomyocytes. Together, our comprehensive multi-omics data offer a panoramic view of cardiac development and maturation that provides a resource for further in-depth functional exploration.

Exploiting Genomic Features to Improve the Prediction of Transcription Factor-Binding Sites in Plants

The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generation sequencing data have been developed to improve the performance of pattern matching. However, such tools have not yet been comprehensively evaluated in plants. Hence, we developed a new streamlined method aiming at predicting CREs and target genes of plant TFs in specific organs or conditions. Our approach implements a supervised machine learning strategy, which allows decision rule models to be learnt using TF ChIP-chip/seq experimental data. Different layers of genomic features were integrated in predictive models: the position on the gene, the DNA sequence conservation, the chromatin state and various CRE footprints. Among the tested features, the chromatin features were crucial for improving the accuracy of the method. Furthermore, we evaluated the transferability of predictive models across TFs, organs and species. Finally, we validated our method by correctly inferring the target genes of key TFs controlling metabolite biosynthesis at the organ level in Arabidopsis. We developed a tool-Wimtrap-to reproduce our approach in plant species and conditions/organs for which ChIP-chip/seq data are available. Wimtrap is a user-friendly R package that supports an R Shiny web interface and is provided with pre-built models that can be used to quickly get predictions of CREs and TF gene targets in different organs or conditions in Arabidopsis thaliana, Solanum lycopersicum, Oryza sativa and Zea mays.

GoPeaks: histone modification peak calling for CUT&Tag

Genome-wide mapping of histone modifications is critical to understanding transcriptional regulation. CUT&Tag is a new method for profiling histone modifications, offering improved sensitivity and decreased cost compared with ChIP-seq. Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. We compare the performance of GoPeaks against commonly used peak calling algorithms to detect histone modifications that display a range of peak profiles and are frequently used in epigenetic studies. We find that GoPeaks robustly detects genome-wide histone modifications and, notably, identifies a substantial number of H3K27ac peaks with improved sensitivity compared to other standard algorithms.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

TSSFinder-fast and accurate ab initio prediction of the core promoter in eukaryotic genomes

Promoter annotation is an important task in the analysis of a genome. One of the main challenges for this task is locating the border between the promoter region and the transcribing region of the gene, the transcription start site (TSS). The TSS is the reference point to delimit the DNA sequence responsible for the assembly of the transcribing complex. As the same gene can have more than one TSS, so to delimit the promoter region, it is important to locate the closest TSS to the site of the beginning of the translation. This paper presents TSSFinder, a new software for the prediction of the TSS signal of eukaryotic genes that is significantly more accurate than other available software. We currently are the only application to offer pre-trained models for six different eukaryotic organisms: Arabidopsis thaliana, Drosophila melanogaster, Gallus gallus, Homo sapiens, Oryza sativa and Saccharomyces cerevisiae. Additionally, our software can be easily customized for specific organisms using only 125 DNA sequences with a validated TSS signal and corresponding genomic locations as a training set. TSSFinder is a valuable new tool for the annotation of genomes. TSSFinder source code and docker container can be downloaded from http://tssfinder.github.io. Alternatively, TSSFinder is also available as a web service at http://sucest-fun.org/wsapp/tssfinder/.

AI applications in functional genomics

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

HMGB1 coordinates SASP-related chromatin folding and RNA homeostasis on the path to senescence

Spatial organization and gene expression of mammalian chromosomes are maintained and regulated in conjunction with cell cycle progression. This is perturbed once cells enter senescence and the highly abundant HMGB1 protein is depleted from nuclei to act as an extracellular proinflammatory stimulus. Despite its physiological importance, we know little about the positioning of HMGB1 on chromatin and its nuclear roles. To address this, we mapped HMGB1 binding genome-wide in two primary cell lines. We integrated ChIP-seq and Hi-C with graph theory to uncover clustering of HMGB1-marked topological domains that harbor genes involved in paracrine senescence. Using simplified Cross-Linking and Immuno-Precipitation and functional tests, we show that HMGB1 is also a bona fide RNA-binding protein (RBP) binding hundreds of mRNAs. It presents an interactome rich in RBPs implicated in senescence regulation. The mRNAs of many of these RBPs are directly bound by HMGB1 and regulate availability of SASP-relevant transcripts. Our findings reveal a broader than hitherto assumed role for HMGB1 in coordinating chromatin folding and RNA homeostasis as part of a regulatory loop controlling cell-autonomous and paracrine senescence.

Chromatin accessibility profiling methods

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

ATACdb: a comprehensive human chromatin accessibility database

Accessible chromatin is a highly informative structural feature for identifying regulatory elements, which provides a large amount of information about transcriptional activity and gene regulatory mechanisms. Human ATAC-seq datasets are accumulating rapidly, prompting an urgent need to comprehensively collect and effectively process these data. We developed a comprehensive human chromatin accessibility database (ATACdb, http://www.licpathway.net/ATACdb), with the aim of providing a large amount of publicly available resources on human chromatin accessibility data, and to annotate and illustrate potential roles in a tissue/cell type-specific manner. The current version of ATACdb documented a total of 52 078 883 regions from over 1400 ATAC-seq samples. These samples have been manually curated from over 2200 chromatin accessibility samples from NCBI GEO/SRA. To make these datasets more accessible to the research community, ATACdb provides a quality assurance process including four quality control (QC) metrics. ATACdb provides detailed (epi)genetic annotations in chromatin accessibility regions, including super-enhancers, typical enhancers, transcription factors (TFs), common single-nucleotide polymorphisms (SNPs), risk SNPs, eQTLs, LD SNPs, methylations, chromatin interactions and TADs. Especially, ATACdb provides accurate inference of TF footprints within chromatin accessibility regions. ATACdb is a powerful platform that provides the most comprehensive accessible chromatin data, QC, TF footprint and various other annotations.

An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning

Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary DNA sequence. In addition to DNA sequences, histone modifications and chromatin accessibility are also important factors influencing their activity. They have been explored to predict TFBSs recently. However, current methods rarely take into account histone modifications and chromatin accessibility using CNN in an integrative framework. To this end, we developed a general CNN model to integrate these data for predicting TFBSs. We systematically benchmarked a series of architecture variants by changing network structure in terms of width and depth, and explored the effects of sample length at flanking regions. We evaluated the performance of the three types of data and their combinations using 256 ChIP-seq experiments and also compared it with competing machine learning methods. We find that contributions from these three types of data are complementary to each other. Moreover, the integrative CNN framework is superior to traditional machine learning methods with significant improvements.

Interrogating the Accessible Chromatin Landscape of Eukaryote Genomes Using ATAC-seq

The ATAC-seq assay has emerged as the most useful, versatile, and widely adaptable method for profiling accessible chromatin regions and tracking the activity of cis-regulatory elements (cREs) in eukaryotes. Thanks to its great utility, it is now being applied to map active chromatin in the context of a very wide diversity of biological systems and questions. In the course of these studies, considerable experience working with ATAC-seq data has accumulated and a standard set of computational tasks that need to be carried for most ATAC-seq analyses has emerged. Here, we review and provide examples of common such analytical procedures (including data processing, quality control, peak calling, identifying differentially accessible open chromatin regions, and variable transcription factor (TF) motif accessibility) and discuss recommended optimal practices.

Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer's Disease

Increasing evidence suggests Alzheimer's disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline.

The Meningioma Enhancer Landscape Delineates Novel Subgroups and Drives Druggable Dependencies

Meningiomas are the most common primary intracranial tumor with current classification offering limited therapeutic guidance. Here, we interrogated meningioma enhancer landscapes from 33 tumors to stratify patients based upon prognosis and identify novel meningioma-specific dependencies. Enhancers robustly stratified meningiomas into three biologically distinct groups (adipogenesis/cholesterol, mesodermal, and neural crest) distinguished by distinct hormonal lineage transcriptional regulators. Meningioma landscapes clustered with intrinsic brain tumors and hormonally responsive systemic cancers with meningioma subgroups, reflecting progesterone or androgen hormonal signaling. Enhancer classification identified a subset of tumors with poor prognosis, irrespective of histologic grading. Superenhancer signatures predicted drug dependencies with superior in vitro efficacy to treatment based upon the NF2 genomic profile. Inhibition of DUSP1, a novel and druggable meningioma target, impaired tumor growth in vivo. Collectively, epigenetic landscapes empower meningioma classification and identification of novel therapies. SIGNIFICANCE: Enhancer landscapes inform prognostic classification of aggressive meningiomas, identifying tumors at high risk of recurrence, and reveal previously unknown therapeutic targets. Druggable dependencies discovered through epigenetic profiling potentially guide treatment of intractable meningiomas.This article is highlighted in the In This Issue feature, p. 1611.

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types

Characterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits.

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

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

Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation

Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.

Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability

Regulatory variation plays a major role in complex disease and that cell type-specific binding of transcription factors (TF) is critical to gene regulation. However, assessing the contribution of genetic variation in TF-binding sites to disease heritability is challenging, as binding is often cell type-specific and annotations from directly measured TF binding are not currently available for most cell type-TF pairs. We investigate approaches to annotate TF binding, including directly measured chromatin data and sequence-based predictions. We find that TF-binding annotations constructed by intersecting sequence-based TF-binding predictions with cell type-specific chromatin data explain a large fraction of heritability across a broad set of diseases and corresponding cell types; this strategy of constructing annotations addresses both the limitation that identical sequences may be bound or unbound depending on surrounding chromatin context and the limitation that sequence-based predictions are generally not cell type-specific. We partitioned the heritability of 49 diseases and complex traits using stratified linkage disequilibrium (LD) score regression with the baseline-LD model (which is not cell type-specific) plus the new annotations. We determined that 100 bp windows around MotifMap sequenced-based TF-binding predictions intersected with a union of six cell type-specific chromatin marks (imputed using ChromImpute) performed best, with an 58% increase in heritability enrichment compared to the chromatin marks alone (11.6× vs. 7.3×, P = 9 × 10-14 for difference) and a 20% increase in cell type-specific signal conditional on annotations from the baseline-LD model (P = 8 × 10-11 for difference). Our results show that TF-binding annotations explain substantial disease heritability and can help refine genome-wide association signals.

Integrative Omics Analysis Reveals a Limited Transcriptional Shock After Yeast Interspecies Hybridization

The formation of interspecific hybrids results in the coexistence of two diverged genomes within the same nucleus. It has been hypothesized that negative epistatic interactions and regulatory interferences between the two sub-genomes may elicit a so-called genomic shock involving, among other alterations, broad transcriptional changes. To assess the magnitude of this shock in hybrid yeasts, we investigated the transcriptomic differences between a newly formed Saccharomyces cerevisiae × Saccharomyces uvarum diploid hybrid and its diploid parentals, which diverged ∼20 mya. RNA sequencing (RNA-Seq) based allele-specific expression (ASE) analysis indicated that gene expression changes in the hybrid genome are limited, with only ∼1-2% of genes significantly altering their expression with respect to a non-hybrid context. In comparison, a thermal shock altered six times more genes. Furthermore, differences in the expression between orthologous genes in the two parental species tended to be diminished for the corresponding homeologous genes in the hybrid. Finally, and consistent with the RNA-Seq results, we show a limited impact of hybridization on chromatin accessibility patterns, as assessed with assay for transposase-accessible chromatin using sequencing (ATAC-Seq). Overall, our results suggest a limited genomic shock in a newly formed yeast hybrid, which may explain the high frequency of successful hybridization in these organisms.

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.

A comprehensive review of computational prediction of genome-wide features

There are significant correlations among different types of genetic, genomic and epigenomic features within the genome. These correlations make the in silico feature prediction possible through statistical or machine learning models. With the accumulation of a vast amount of high-throughput data, feature prediction has gained significant interest lately, and a plethora of papers have been published in the past few years. Here we provide a comprehensive review on these published works, categorized by the prediction targets, including protein binding site, enhancer, DNA methylation, chromatin structure and gene expression. We also provide discussions on some important points and possible future directions.

Predicting transcription factor binding using ensemble random forest models

Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs).

Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups.

Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier built based upon data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal.

Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

BiFET: sequencing Bias-free transcription factor Footprint Enrichment Test

Transcription factor (TF) footprinting uncovers putative protein–DNA binding via combined analyses of chromatin accessibility patterns and their underlying TF sequence motifs. TF footprints are frequently used to identify TFs that regulate activities of cell/condition-specific genomic regions (target loci) in comparison to control regions (background loci) using standard enrichment tests. However, there is a strong association between the chromatin accessibility level and the GC content of a locus and the number and types of TF footprints that can be detected at this site. Traditional enrichment tests (e.g. hypergeometric) do not account for this bias and inflate false positive associations. Therefore, we developed a novel post-processing method, Bias-free Footprint Enrichment Test (BiFET), that corrects for the biases arising from the differences in chromatin accessibility levels and GC contents between target and background loci in footprint enrichment analyses. We applied BiFET on TF footprint calls obtained from EndoC-βH1 ATAC-seq samples using three different algorithms (CENTIPEDE, HINT-BC and PIQ) and showed BiFET’s ability to increase power and reduce false positive rate when compared to hypergeometric test. Furthermore, we used BiFET to study TF footprints from human PBMC and pancreatic islet ATAC-seq samples to show its utility to identify putative TFs associated with cell-type-specific loci.

Reproducible big data science: A case study in continuous FAIRness

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility-thus ensuring that big data are not hard-to-(re)use data. We evaluate our approach via a user study, and show that 91% of participants were able to replicate a complex analysis involving considerable data volumes.

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.

Accurate prediction of cell type-specific transcription factor binding

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

ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference

Background

Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data.

Results

We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads.

Conclusions

We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4943-z) contains supplementary material, which is available to authorized users.

Genome-scale identification of transcription factors that mediate an inflammatory network during breast cellular transformation

Transient activation of Src oncoprotein in non-transformed, breast epithelial cells can initiate an epigenetic switch to the stably transformed state via a positive feedback loop that involves the inflammatory transcription factors STAT3 and NF-κB. Here, we develop an experimental and computational pipeline that includes 1) a Bayesian network model (AccessTF) that accurately predicts protein-bound DNA sequence motifs based on chromatin accessibility, and 2) a scoring system (TFScore) that rank-orders transcription factors as candidates for being important for a biological process. Genetic experiments validate TFScore and suggest that more than 40 transcription factors contribute to the oncogenic state in this model. Interestingly, individual depletion of several of these factors results in similar transcriptional profiles, indicating that a complex and interconnected transcriptional network promotes a stable oncogenic state. The combined experimental and computational pipeline represents a general approach to comprehensively identify transcriptional regulators important for a biological process.

HMGB2 Loss upon Senescence Entry Disrupts Genomic Organization and Induces CTCF Clustering across Cell Types

Processes like cellular senescence are characterized by complex events giving rise to heterogeneous cell populations. However, the early molecular events driving this cascade remain elusive. We hypothesized that senescence entry is triggered by an early disruption of the cells' three-dimensional (3D) genome organization. To test this, we combined Hi-C, single-cell and population transcriptomics, imaging, and in silico modeling of three distinct cells types entering senescence. Genes involved in DNA conformation maintenance are suppressed upon senescence entry across all cell types. We show that nuclear depletion of the abundant HMGB2 protein occurs early on the path to senescence and coincides with the dramatic spatial clustering of CTCF. Knocking down HMGB2 suffices for senescence-induced CTCF clustering and for loop reshuffling, while ectopically expressing HMGB2 rescues these effects. Our data suggest that HMGB2-mediated genomic reorganization constitutes a primer for the ensuing senescent program.

Characteristics of functional enrichment and gene expression level of human putative transcriptional target genes

BACKGROUND

Transcriptional target genes show functional enrichment of genes. However, how many and how significantly transcriptional target genes include functional enrichments are still unclear. To address these issues, I predicted human transcriptional target genes using open chromatin regions, ChIP-seq data and DNA binding sequences of transcription factors in databases, and examined functional enrichment and gene expression level of putative transcriptional target genes.

RESULTS

Gene Ontology annotations showed four times larger numbers of functional enrichments in putative transcriptional target genes than gene expression information alone, independent of transcriptional target genes. To compare the number of functional enrichments of putative transcriptional target genes between cells or search conditions, I normalized the number of functional enrichment by calculating its ratios in the total number of transcriptional target genes. With this analysis, native putative transcriptional target genes showed the largest normalized number of functional enrichments, compared with target genes including 5-60% of randomly selected genes. The normalized number of functional enrichments was changed according to the criteria of enhancer-promoter interactions such as distance from transcriptional start sites and orientation of CTCF-binding sites. Forward-reverse orientation of CTCF-binding sites showed significantly higher normalized number of functional enrichments than the other orientations. Journal papers showed that the top five frequent functional enrichments were related to the cellular functions in the three cell types. The median expression level of transcriptional target genes changed according to the criteria of enhancer-promoter assignments (i.e. interactions) and was correlated with the changes of the normalized number of functional enrichments of transcriptional target genes.

CONCLUSIONS

Human putative transcriptional target genes showed significant functional enrichments. Functional enrichments were related to the cellular functions. The normalized number of functional enrichments of human putative transcriptional target genes changed according to the criteria of enhancer-promoter assignments and correlated with the median expression level of the target genes. These analyses and characters of human putative transcriptional target genes would be useful to examine the criteria of enhancer-promoter assignments and to predict the novel mechanisms and factors such as DNA binding proteins and DNA sequences of enhancer-promoter interactions.

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

BACKGROUNDS

With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings.

RESULTS

We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence.

CONCLUSION

Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility

Background

Computational prediction of transcription factor (TF) binding sites in different cell types is challenging. Recent technology development allows us to determine the genome-wide chromatin accessibility in various cellular and developmental contexts. The chromatin accessibility profiles provide useful information in prediction of TF binding events in various physiological conditions. Furthermore, ChIP-Seq analysis was used to determine genome-wide binding sites for a range of different TFs in multiple cell types. Integration of these two types of genomic information can improve the prediction of TF binding events.

Results

We assessed to what extent a model built upon on other TFs and/or other cell types could be used to predict the binding sites of TFs of interest. A random forest model was built using a set of cell type-independent features such as specific sequences recognized by the TFs and evolutionary conservation, as well as cell type-specific features derived from chromatin accessibility data. Our analysis suggested that the models learned from other TFs and/or cell lines performed almost as well as the model learned from the target TF in the cell type of interest. Interestingly, models based on multiple TFs performed better than single-TF models. Finally, we proposed a universal model, BPAC, which was generated using ChIP-Seq data from multiple TFs in various cell types.

Conclusion

Integrating chromatin accessibility information with sequence information improves prediction of TF binding.The prediction of TF binding is transferable across TFs and/or cell lines suggesting there are a set of universal “rules”. A computational tool was developed to predict TF binding sites based on the universal “rules”.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1769-7) contains supplementary material, which is available to authorized users.

RegulatorTrail: a web service for the identification of key transcriptional regulators

Transcriptional regulators such as transcription factors and chromatin modifiers play a central role in most biological processes. Alterations in their activities have been observed in many diseases, e.g. cancer. Hence, it is of utmost importance to evaluate and assess the effects of transcriptional regulators on natural and pathogenic processes. Here, we present RegulatorTrail, a web service that provides rich functionality for the identification and prioritization of key transcriptional regulators that have a strong impact on, e.g. pathological processes. RegulatorTrail offers eight methods that use regulator binding information in combination with transcriptomic or epigenomic data to infer the most influential regulators. Our web service not only provides an intuitive web interface, but also a well-documented RESTful API that allows for a straightforward integration into third-party workflows. The presented case studies highlight the capabilities of our web service and demonstrate its potential for the identification of influential regulators: we successfully identified regulators that might explain the increased malignancy in metastatic melanoma compared to primary tumors, as well as important regulators in macrophages. RegulatorTrail is freely accessible at: https://regulatortrail.bioinf.uni-sb.de/.

Mocap: large-scale inference of transcription factor binding sites from chromatin accessibility

Differential binding of transcription factors (TFs) at cis-regulatory loci drives the differentiation and function of diverse cellular lineages. Understanding the regulatory interactions that underlie cell fate decisions requires characterizing TF binding sites (TFBS) across multiple cell types and conditions. Techniques, e.g. ChIP-Seq can reveal genome-wide patterns of TF binding, but typically requires laborious and costly experiments for each TF-cell-type (TFCT) condition of interest. Chromosomal accessibility assays can connect accessible chromatin in one cell type to many TFs through sequence motif mapping. Such methods, however, rarely take into account that the genomic context preferred by each factor differs from TF to TF, and from cell type to cell type. To address the differences in TF behaviors, we developed Mocap, a method that integrates chromatin accessibility, motif scores, TF footprints, CpG/GC content, evolutionary conservation and other factors in an ensemble of TFCT-specific classifiers. We show that integration of genomic features, such as CpG islands improves TFBS prediction in some TFCT. Further, we describe a method for mapping new TFCT, for which no ChIP-seq data exists, onto our ensemble of classifiers and show that our cross-sample TFBS prediction method outperforms several previously described methods.