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

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

OneSC: A computational platform for recapitulating cell state transitions

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulation

Epigenetic mechanisms contribute to gene regulation by altering chromatin accessibility through changes in transcription factor (TF) and nucleosome occupancy throughout the genome. Despite numerous studies focusing on changes in gene expression, the intricate chromatin-mediated regulatory code remains largely unexplored on a comprehensive scale. We address this by employing a factor-agnostic, reverse-genetics approach that uses MNase-seq to capture genome-wide TF and nucleosome occupancies in response to the individual deletion of 201 transcriptional regulators in Saccharomyces cerevisiae, thereby assaying nearly one million mutant-gene interactions. We develop a principled approach to identify and quantify chromatin changes genome-wide, observing differences in TF and nucleosome occupancy that recapitulate well-established pathways identified by gene expression data. We also discover distinct chromatin signatures associated with the up- and downregulation of genes, and use these signatures to reveal regulatory mechanisms previously unexplored in expression-based studies. Finally, we demonstrate that chromatin features are predictive of transcriptional activity and leverage these features to reconstruct chromatin-based transcriptional regulatory networks. Overall, these results illustrate the power of an approach combining genetic perturbation with high-resolution epigenomic profiling; the latter enables a close examination of the interplay between TFs and nucleosomes genome-wide, providing a deeper, more mechanistic understanding of the complex relationship between chromatin organization and transcription.

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

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

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

A data-driven optimization method for coarse-graining gene regulatory networks

One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction, and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.

Epigenetic Regulation of Endothelial Cell Lineages During Zebrafish Development-New Insights From Technical Advances

Epigenetic regulation is integral in orchestrating the spatiotemporal regulation of gene expression which underlies tissue development. The emergence of new tools to assess genome-wide epigenetic modifications has enabled significant advances in the field of vascular biology in zebrafish. Zebrafish represents a powerful model to investigate the activity of cis-regulatory elements in vivo by combining technologies such as ATAC-seq, ChIP-seq and CUT&Tag with the generation of transgenic lines and live imaging to validate the activity of these regulatory elements. Recently, this approach led to the identification and characterization of key enhancers of important vascular genes, such as gata2a, notch1b and dll4. In this review we will discuss how the latest technologies in epigenetics are being used in the zebrafish to determine chromatin states and assess the function of the cis-regulatory sequences that shape the zebrafish vascular network.

PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments

As chromatin accessibility data from ATAC-seq experiments continues to expand, there is continuing need for standardized analysis pipelines. Here, we present PEPATAC, an ATAC-seq pipeline that is easily applied to ATAC-seq projects of any size, from one-off experiments to large-scale sequencing projects. PEPATAC leverages unique features of ATAC-seq data to optimize for speed and accuracy, and it provides several unique analytical approaches. Output includes convenient quality control plots, summary statistics, and a variety of generally useful data formats to set the groundwork for subsequent project-specific data analysis. Downstream analysis is simplified by a standard definition format, modularity of components, and metadata APIs in R and Python. It is restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also demonstrate the advantage of aligning to the mitochondrial genome serially, which improves the accuracy of alignment statistics and quality control metrics. PEPATAC is a robust and portable first step for any ATAC-seq project. BSD2-licensed code and documentation are available at https://pepatac.databio.org.

Towards improved genetic diagnosis of human differences of sex development

Despite being collectively among the most frequent congenital developmental conditions worldwide, differences of sex development (DSD) lack recognition and research funding. As a result, what constitutes optimal management remains uncertain. Identification of the individual conditions under the DSD umbrella is challenging and molecular genetic diagnosis is frequently not achieved, which has psychosocial and health-related repercussions for patients and their families. New genomic approaches have the potential to resolve this impasse through better detection of protein-coding variants and ascertainment of under-recognized aetiology, such as mosaic, structural, non-coding or epigenetic variants. Ultimately, it is hoped that better outcomes data, improved understanding of the molecular causes and greater public awareness will bring an end to the stigma often associated with DSD.

AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) is a technique widely used to investigate genome-wide chromatin accessibility. The recently published Omni-ATAC-seq protocol substantially improves the signal/noise ratio and reduces the input cell number. High-quality data are critical to ensure accurate analysis. Several tools have been developed for assessing sequencing quality and insertion size distribution for ATAC-seq data; however, key quality control (QC) metrics have not yet been established to accurately determine the quality of ATAC-seq data. Here, we optimized the analysis strategy for ATAC-seq and defined a series of QC metrics for ATAC-seq data, including reads under peak ratio (RUPr), background (BG), promoter enrichment (ProEn), subsampling enrichment (SubEn), and other measurements. We incorporated these QC tests into our recently developed ATAC-seq Integrative Analysis Package (AIAP) to provide a complete ATAC-seq analysis system, including quality assurance, improved peak calling, and downstream differential analysis. We demonstrated a significant improvement of sensitivity (20%–60%) in both peak calling and differential analysis by processing paired-end ATAC-seq datasets using AIAP. AIAP is compiled into Docker/Singularity, and it can be executed by one command line to generate a comprehensive QC report. We used ENCODE ATAC-seq data to benchmark and generate QC recommendations, and developed qATACViewer for the user-friendly interaction with the QC report. The software, source code, and documentation of AIAP are freely available at https://github.com/Zhang-lab/ATAC-seq_QC_analysis.

Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions

Epithelial-mesenchymal transition (EMT) is an important biological process through which epithelial cells undergo phenotypic transitions to mesenchymal cells by losing cell-cell adhesion and gaining migratory properties that cells use in embryogenesis, wound healing, and cancer metastasis. An important research topic is to identify the underlying gene regulatory networks (GRNs) governing the decision making of EMT and develop predictive models based on the GRNs. The advent of recent genomic technology, such as single-cell RNA sequencing, has opened new opportunities to improve our understanding about the dynamical controls of EMT. In this article, we review three major types of computational and mathematical approaches and methods for inferring and modeling GRNs driving EMT. We emphasize (1) the bottom-up approaches, where GRNs are constructed through literature search; (2) the top-down approaches, where GRNs are derived from genome-wide sequencing data; (3) the combined top-down and bottom-up approaches, where EMT GRNs are constructed and simulated by integrating bioinformatics and mathematical modeling. We discuss the methodologies and applications of each approach and the available resources for these studies.

Analytical Approaches for ATAC-seq Data Analysis

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

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

From reads to insight: a hitchhiker's guide to ATAC-seq data analysis

Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) is widely used in studying chromatin biology, but a comprehensive review of the analysis tools has not been completed yet. Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). We also review the reconstruction of transcriptional regulatory networks with multiomics data and highlight the current challenges of each step. Finally, we describe the potential of single-cell ATAC-seq and highlight the necessity of developing ATAC-seq specific analysis tools to obtain biologically meaningful insights.

Detect accessible chromatin using ATAC-sequencing, from principle to applications

Background

Chromatin accessibility is crucial for gene expression regulation in specific cells and in multiple biological processes. Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is an effective way to reveal chromatin accessibility at a genome-wide level. Through ATAC-seq, produced reads from a small number of cells reflect accessible regions that correspond to nucleosome positioning and transcription factor binding sites, due to probing hyperactive Tn5 transposase to DNA sequence.

Conclusion

In this review, we summarize both principle and features of ATAC-seq, highlight its applications in basic and clinical research. ATAC-seq has generated comprehensive chromatin accessible maps, and is becoming a powerful tool to understand dynamic gene expression regulation in stem cells, early embryos and tumors.

XL-DNase-seq: improved footprinting of dynamic transcription factors

Background

As the cost of high-throughput sequencing technologies decreases, genome-wide chromatin accessibility profiling methods such as the assay of transposase-accessible chromatin using sequencing (ATAC-seq) are employed widely, with data accumulating at an unprecedented rate. However, accurate inference of protein occupancy requires higher-resolution footprinting analysis where major hurdles exist, including the sequence bias of nucleases and the short-lived chromatin binding of many transcription factors (TFs) with consequent lack of footprints.

Results

Here we introduce an assay termed cross-link (XL)-DNase-seq, designed to capture chromatin interactions of dynamic TFs. Mild cross-linking improved the detection of DNase-based footprints of dynamic TFs but interfered with ATAC-based footprinting of the same TFs.

Conclusions

XL-DNase-seq may help extract novel gene regulatory circuits involving previously undetectable TFs. The DNase-seq and ATAC-seq data generated in our systematic comparison of various cross-linking conditions also represent an unprecedented-scale resource derived from activated mouse macrophage-like cells which share many features of inflammatory macrophages.

Electronic supplementary material

The online version of this article (10.1186/s13072-019-0277-6) contains supplementary material, which is available to authorized users.

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

Following the publication of this article [1], the authors informed us of the following typographical errors in the Results section (the changes are marked in bold).

Integrated Epigenetic Mapping of Human and Mouse Salivary Gene Regulation

Significant effort has been applied to identify the genome-wide gene expression profiles associated with salivary gland development and pathophysiology. However, relatively little is known about the regulators that control salivary gland gene expression. We integrated data from DNase1 digital genomic footprinting, RNA-seq, and gene expression microarrays to comprehensively characterize the cis- and trans-regulatory components controlling gene expression of the healthy submandibular salivary gland. Analysis of 32 human tissues and 87 mouse tissues was performed to identify the highly expressed and tissue-enriched transcription factors driving salivary gland gene expression. Following RNA analysis, protein expression levels and subcellular localization of 39 salivary transcription factors were confirmed by immunohistochemistry. These expression analyses revealed that the salivary gland highly expresses transcription factors associated with endoplasmic reticulum stress, human T-cell lymphotrophic virus 1 expression, and Epstein-Barr virus reactivation. DNase1 digital genomic footprinting to a depth of 333,426,353 reads was performed and utilized to generate a salivary gland gene regulatory network describing the genome-wide chromatin accessibility and transcription factor binding of the salivary gland at a single-nucleotide resolution. Analysis of the DNase1 gene regulatory network identified dense interconnectivity among PLAG1, MYB, and 13 other transcription factors associated with balanced chromosomal translocations and salivary gland tumors. Collectively, these analyses provide a comprehensive atlas of the cis- and trans-regulators of the salivary gland and highlight known aberrantly regulated pathways of diseases affecting the salivary glands.