1
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Huo Q, Song R, Ma Z. Recent advances in exploring transcriptional regulatory landscape of crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1421503. [PMID: 38903438 PMCID: PMC11188431 DOI: 10.3389/fpls.2024.1421503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/23/2024] [Indexed: 06/22/2024]
Abstract
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.
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Affiliation(s)
| | | | - Zeyang Ma
- State Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Joint International Research Laboratory of Crop Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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2
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Peng D, Cahan P. OneSC: A computational platform for recapitulating cell state transitions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.31.596831. [PMID: 38895453 PMCID: PMC11185539 DOI: 10.1101/2024.05.31.596831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
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.
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Affiliation(s)
- Da Peng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Institute for Cell Engineering, Johns Hopkins University, Baltimore, Maryland, 21205, USA
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, Maryland, 21205, USA
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3
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Moyung K, Li Y, Hartemink AJ, MacAlpine DM. Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595391. [PMID: 38826400 PMCID: PMC11142231 DOI: 10.1101/2024.05.24.595391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
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.
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Affiliation(s)
- Kevin Moyung
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710
| | - Yulong Li
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710
- Department of Computer Science, Duke University, Durham, NC 27708
| | - Alexander J. Hartemink
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708
- Department of Computer Science, Duke University, Durham, NC 27708
| | - David M. MacAlpine
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710
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4
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Sunitha Kumary VUN, Venters BJ, Raman K, Sen S, Estève PO, Cowles MW, Keogh MC, Pradhan S. Emerging Approaches to Profile Accessible Chromatin from Formalin-Fixed Paraffin-Embedded Sections. EPIGENOMES 2024; 8:20. [PMID: 38804369 PMCID: PMC11130958 DOI: 10.3390/epigenomes8020020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
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.
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Affiliation(s)
| | - Bryan J. Venters
- EpiCypher Inc., Durham, NC 27709, USA; (V.U.N.S.K.); (B.J.V.); (M.W.C.)
| | - Karthikeyan Raman
- Genome Biology Division, New England Biolabs, Ipswich, MA 01983, USA; (K.R.); (S.S.); (P.-O.E.)
| | - Sagnik Sen
- Genome Biology Division, New England Biolabs, Ipswich, MA 01983, USA; (K.R.); (S.S.); (P.-O.E.)
| | - Pierre-Olivier Estève
- Genome Biology Division, New England Biolabs, Ipswich, MA 01983, USA; (K.R.); (S.S.); (P.-O.E.)
| | - Martis W. Cowles
- EpiCypher Inc., Durham, NC 27709, USA; (V.U.N.S.K.); (B.J.V.); (M.W.C.)
| | | | - Sriharsa Pradhan
- Genome Biology Division, New England Biolabs, Ipswich, MA 01983, USA; (K.R.); (S.S.); (P.-O.E.)
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5
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Badia-I-Mompel P, Wessels L, Müller-Dott S, Trimbour R, Ramirez Flores RO, Argelaguet R, Saez-Rodriguez J. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet 2023; 24:739-754. [PMID: 37365273 DOI: 10.1038/s41576-023-00618-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 06/28/2023]
Abstract
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.
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Affiliation(s)
- Pau Badia-I-Mompel
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Lorna Wessels
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Department of Vascular Biology and Tumor Angiogenesis, European Center for Angioscience, Medical Faculty, MannHeim Heidelberg University, Mannheim, Germany
| | - Sophia Müller-Dott
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Rémi Trimbour
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France
| | - Ricardo O Ramirez Flores
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | | | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.
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6
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Caranica C, Lu M. A data-driven optimization method for coarse-graining gene regulatory networks. iScience 2023; 26:105927. [PMID: 36698721 PMCID: PMC9868542 DOI: 10.1016/j.isci.2023.105927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/19/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
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.
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Affiliation(s)
- Cristian Caranica
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA,Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA,Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA,The Jackson Laboratory, Bar Harbor, ME 04609, USA,Corresponding author
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7
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Panara V, Monteiro R, Koltowska K. Epigenetic Regulation of Endothelial Cell Lineages During Zebrafish Development-New Insights From Technical Advances. Front Cell Dev Biol 2022; 10:891538. [PMID: 35615697 PMCID: PMC9125237 DOI: 10.3389/fcell.2022.891538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/10/2022] [Indexed: 01/09/2023] Open
Abstract
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.
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Affiliation(s)
- Virginia Panara
- Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Rui Monteiro
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom,Birmingham Centre of Genome Biology, University of Birmingham, Birmingham, United Kingdom
| | - Katarzyna Koltowska
- Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden,*Correspondence: Katarzyna Koltowska,
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8
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Smith JP, Corces MR, Xu J, Reuter VP, Chang HY, Sheffield NC. PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments. NAR Genom Bioinform 2021; 3:lqab101. [PMID: 34859208 PMCID: PMC8632735 DOI: 10.1093/nargab/lqab101] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 09/30/2021] [Accepted: 11/15/2021] [Indexed: 12/18/2022] Open
Abstract
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.
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Affiliation(s)
- Jason P Smith
- Center for Public Health Genomics, University of Virginia, VA,22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, VA 22908 USA
| | - M Ryan Corces
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94304, USA
| | - Jin Xu
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94304, USA
| | - Vincent P Reuter
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, PA 19087, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94304, USA
| | - Nathan C Sheffield
- Center for Public Health Genomics, University of Virginia, VA,22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, VA 22908 USA
- Department of Public Health Sciences, University of Virginia, VA 22908, USA
- Department of Biomedical Engineering, University of Virginia, VA 22908, USA
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9
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Délot EC, Vilain E. Towards improved genetic diagnosis of human differences of sex development. Nat Rev Genet 2021; 22:588-602. [PMID: 34083777 PMCID: PMC10598994 DOI: 10.1038/s41576-021-00365-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/14/2021] [Indexed: 02/05/2023]
Abstract
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.
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Affiliation(s)
- Emmanuèle C Délot
- Center for Genetic Medicine Research, Children's Research Institute, Children's National Hospital, Washington, DC, USA
- Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC, USA
| | - Eric Vilain
- Center for Genetic Medicine Research, Children's Research Institute, Children's National Hospital, Washington, DC, USA.
- Department of Genomics and Precision Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC, USA.
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10
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Liu S, Li D, Lyu C, Gontarz PM, Miao B, Madden PAF, Wang T, Zhang B. AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:641-651. [PMID: 34273560 PMCID: PMC9040017 DOI: 10.1016/j.gpb.2020.06.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Revised: 05/28/2020] [Accepted: 10/27/2020] [Indexed: 12/17/2022]
Abstract
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.
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Affiliation(s)
- Shaopeng Liu
- Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Daofeng Li
- Department of Genetics, Center for Genomic Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Cheng Lyu
- Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Paul M Gontarz
- Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Benpeng Miao
- Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Genetics, Center for Genomic Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Ting Wang
- Department of Genetics, Center for Genomic Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA.
| | - Bo Zhang
- Department of Developmental Biology, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63108, USA.
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11
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Katebi A, Ramirez D, Lu M. Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2021; 1:e1021. [PMID: 34164628 PMCID: PMC8219219 DOI: 10.1002/cso2.1021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
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.
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Affiliation(s)
- Ataur Katebi
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, Massachusetts, USA
| | - Daniel Ramirez
- Center for Theoretical Biological Physics, Northeastern University, Boston, Massachusetts, USA
- College of Health Solutions, Arizona State University, Tempe, Arizona, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, Massachusetts, USA
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12
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Smith JP, Sheffield NC. Analytical Approaches for ATAC-seq Data Analysis. CURRENT PROTOCOLS IN HUMAN GENETICS 2020; 106:e101. [PMID: 32543102 PMCID: PMC8191135 DOI: 10.1002/cphg.101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
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.
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Affiliation(s)
- Jason P. Smith
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia
| | - Nathan C. Sheffield
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
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13
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Reske JJ, Wilson MR, Chandler RL. ATAC-seq normalization method can significantly affect differential accessibility analysis and interpretation. Epigenetics Chromatin 2020; 13:22. [PMID: 32321567 PMCID: PMC7178746 DOI: 10.1186/s13072-020-00342-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 04/11/2020] [Indexed: 11/10/2022] Open
Abstract
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.
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Affiliation(s)
- Jake J Reske
- Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA
| | - Mike R Wilson
- Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA
| | - Ronald L Chandler
- Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, 49503, USA. .,Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, 49503, USA.
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14
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Yan F, Powell DR, Curtis DJ, Wong NC. From reads to insight: a hitchhiker's guide to ATAC-seq data analysis. Genome Biol 2020; 21:22. [PMID: 32014034 PMCID: PMC6996192 DOI: 10.1186/s13059-020-1929-3] [Citation(s) in RCA: 204] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 01/08/2020] [Indexed: 12/16/2022] Open
Abstract
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.
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Affiliation(s)
- Feng Yan
- Australian Centre for Blood Diseases, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - David R Powell
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - David J Curtis
- Australian Centre for Blood Diseases, Central Clinical School, Monash University, Melbourne, VIC, Australia.,Department of Clinical Haematology, Alfred Health, Melbourne, VIC, Australia
| | - Nicholas C Wong
- Australian Centre for Blood Diseases, Central Clinical School, Monash University, Melbourne, VIC, Australia. .,Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia.
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15
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Sun Y, Miao N, Sun T. Detect accessible chromatin using ATAC-sequencing, from principle to applications. Hereditas 2019; 156:29. [PMID: 31427911 PMCID: PMC6696680 DOI: 10.1186/s41065-019-0105-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 08/12/2019] [Indexed: 02/07/2023] Open
Abstract
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.
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Affiliation(s)
- Yuanyuan Sun
- Center for Precision Medicine, School of Medicine and School of Biomedical Sciences, Huaqiao University, 668 Jimei Road, Xiamen, 361021 Fujian China
| | - Nan Miao
- Center for Precision Medicine, School of Medicine and School of Biomedical Sciences, Huaqiao University, 668 Jimei Road, Xiamen, 361021 Fujian China
| | - Tao Sun
- Center for Precision Medicine, School of Medicine and School of Biomedical Sciences, Huaqiao University, 668 Jimei Road, Xiamen, 361021 Fujian China
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16
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Oh KS, Ha J, Baek S, Sung MH. XL-DNase-seq: improved footprinting of dynamic transcription factors. Epigenetics Chromatin 2019; 12:30. [PMID: 31164146 PMCID: PMC6547507 DOI: 10.1186/s13072-019-0277-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 05/17/2019] [Indexed: 02/08/2023] Open
Abstract
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.
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Affiliation(s)
- Kyu-Seon Oh
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, National Institutes of Health, 251 Bayview Boulevard, Baltimore, MD, 21224, USA
| | - Jisu Ha
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, National Institutes of Health, 251 Bayview Boulevard, Baltimore, MD, 21224, USA
| | - Songjoon Baek
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, 41 Library Drive, Bethesda, MD, 20892, USA
| | - Myong-Hee Sung
- Laboratory of Molecular Biology and Immunology, National Institute on Aging, National Institutes of Health, 251 Bayview Boulevard, Baltimore, MD, 21224, USA.
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17
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Pranzatelli TJF, Michae DG, Chiorini JA. Correction to: ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference. BMC Genomics 2019; 20:44. [PMID: 30646842 PMCID: PMC6332551 DOI: 10.1186/s12864-019-5441-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 01/09/2019] [Indexed: 11/10/2022] Open
Abstract
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).
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Affiliation(s)
- Thomas J F Pranzatelli
- National Institute of Dental and Craniofacial Research, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20816, USA
| | - Drew G Michae
- National Institute of Dental and Craniofacial Research, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20816, USA
| | - John A Chiorini
- National Institute of Dental and Craniofacial Research, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20816, USA.
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18
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Michael DG, Pranzatelli TJF, Warner BM, Yin H, Chiorini JA. Integrated Epigenetic Mapping of Human and Mouse Salivary Gene Regulation. J Dent Res 2018; 98:209-217. [PMID: 30392435 DOI: 10.1177/0022034518806518] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
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.
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Affiliation(s)
- D G Michael
- 1 Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - T J F Pranzatelli
- 1 Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - B M Warner
- 1 Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - H Yin
- 1 Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - J A Chiorini
- 1 Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
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