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Chromatin accessibility is directly linked with transcription in eukaryotes. Accessible regions associated with regulatory proteins are highly sensitive to DNase I digestion and are termed DNase I hypersensitive sites (DHSs). DHSs can be identified by DNase I digestion, followed by high-throughput DNA sequencing (DNase-seq). The single-base-pair resolution digestion patterns from DNase-seq allows identifying transcription factor (TF) footprints of local DNA protection that predict TF-DNA binding. The identification of differential footprinting between two conditions allows mapping relevant TF regulatory interactions. Here, we provide step-by-step instructions to build gene regulatory networks from DNase-seq data. Our pipeline includes steps for DHSs calling, identification of differential TF footprints between treatment and control conditions, and construction of gene regulatory networks. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be adapted to work with DNase-seq data from any organism with a sequenced genome.
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http://dx.doi.org/10.1007/978-1-0716-1534-8_3 | DOI Listing |
Bioinformatics
August 2025
Department of Computer Science, University of Colorado at Colorado Springs, Colorado Springs, Colorado 80918, USA1.
Motivation: The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains the leading method for unraveling 3D genome structures, but the limited availability of high-resolution Hi-C data poses significant challenges for comprehensive analysis. Deep learning models have been developed to predict high-resolution Hi-C data from low-resolution counterparts.
View Article and Find Full Text PDFReprod Fertil Dev
July 2025
School of Basic Medical Sciences, Hubei University of Medicine, Shiyan, Hubei 442000, China.
Context The induction of oocytes from embryonic stem cells (ESCs) in vitro provides a promising tool for the treatment of female infertility. Various molecules are involved in this complex process, which requires further elucidation. Aims This study aims to screen for factors that induce the differentiation of ESCs into oocytes in vitro by constructing transcription factor (TF)-mediated gene regulatory networks (GRNs) during the formation of oocytes.
View Article and Find Full Text PDFMethods Mol Biol
May 2025
Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
The dynamic gene expression program of hematopoiesis is controlled by a complex network of regulatory modules consisting of transcription factors, chromatin modifiers, and genomic organizers. Genetic abnormalities or changes in the levels of these factors can disrupt normal development and often lead to malignant transformation into leukemic cells. Open chromatin regions are hallmarks of regulatory elements that can be profiled by their susceptibility to DNase I and Tn5 transposase.
View Article and Find Full Text PDFNat Commun
February 2025
Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia.
High-throughput sequencing facilitates large-scale studies of gene regulation and allows tracing the associations of individual genomic variants with changes in gene regulation and expression. Compared to classic association studies, the assessment of an allelic imbalance at heterozygous variants captures functional variant effects with smaller sample sizes, higher sensitivity, and better resolution. Yet, identification of allele-specific variants from allelic read counts remains challenging due to data-dependent biases and overdispersion arising from technical and biological variability.
View Article and Find Full Text PDFComput Struct Biotechnol J
January 2025
Department of Genome Sciences, University of Virginia, Charlottesville, VA 22908, USA.
Chromatin accessibility profiles generated using ATAC-seq or DNase-seq carry functional information of the regulatory genome that controls gene expression. Appropriate normalization of ATAC-seq and DNase-seq data is essential for accurate differential analysis when studying chromatin dynamics. Existing normalization methods usually assume the same distribution of genomic signals across samples; however, this assumption may not be appropriate when there are global changes in chromatin accessibility levels between experimental conditions/samples.
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