Relevant bibliographies by topics / DNase-seq

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'DNase-seq'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "DNase-seq"

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Liu, Yongjing, Liangyu Fu, Kerstin Kaufmann, Dijun Chen, and Ming Chen. "A practical guide for DNase-seq data analysis: from data management to common applications." Briefings in Bioinformatics 20, no. 5 (June 4, 2019): 1865–77. http://dx.doi.org/10.1093/bib/bby057.

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Abstract 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.
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Sun, H., B. Qin, T. Liu, Q. Wang, J. Liu, J. Wang, X. Lin, et al. "CistromeFinder for ChIP-seq and DNase-seq data reuse." Bioinformatics 29, no. 10 (March 18, 2013): 1352–54. http://dx.doi.org/10.1093/bioinformatics/btt135.

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Zhong, Jianling, Kaixuan Luo, Peter S. Winter, Gregory E. Crawford, Edwin S. Iversen, and Alexander J. Hartemink. "Mapping nucleosome positions using DNase-seq." Genome Research 26, no. 3 (January 15, 2016): 351–64. http://dx.doi.org/10.1101/gr.195602.115.

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Gao, Weiwu, Wai Lim Ku, Lixia Pan, Jonathan Perrie, Tingting Zhao, Gangqing Hu, Yuzhang Wu, Jun Zhu, Bing Ni, and Keji Zhao. "Multiplex indexing approach for the detection of DNase I hypersensitive sites in single cells." Nucleic Acids Research 49, no. 10 (March 8, 2021): e56-e56. http://dx.doi.org/10.1093/nar/gkab102.

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Abstract Single cell chromatin accessibility assays reveal epigenomic variability at cis-regulatory elements among individual cells. We previously developed a single-cell DNase-seq assay (scDNase-seq) to profile accessible chromatin in a limited number of single cells. Here, we report a novel indexing strategy to resolve single-cell DNase hypersensitivity profiles based on bulk cell analysis. This new technique, termed indexing single-cell DNase sequencing (iscDNase-seq), employs the activities of terminal DNA transferase (TdT) and T4 DNA ligase to add unique cell barcodes to DNase-digested chromatin ends. By a three-layer indexing strategy, it allows profiling genome-wide DHSs for >15 000 single-cells in a single experiment. Application of iscDNase-seq to human white blood cells accurately revealed specific cell types and inferred regulatory transcription factors (TF) specific to each cell type. We found that iscDNase-seq detected DHSs with specific properties related to gene expression and conservation missed by scATAC-seq for the same cell type. Also, we found that the cell-to-cell variation in accessibility computed using iscDNase-seq data is significantly correlated with the cell-to-cell variation in gene expression. Importantly, this correlation is significantly higher than that between scATAC-seq and scRNA-seq, suggesting that iscDNase-seq data can better predict the cellular heterogeneity in gene expression compared to scATAC-seq. Thus, iscDNase-seq is an attractive alternative method for single-cell epigenomics studies.
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Nordström, Karl J. V., Florian Schmidt, Nina Gasparoni, Abdulrahman Salhab, Gilles Gasparoni, Kathrin Kattler, Fabian Müller, et al. "Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data." Nucleic Acids Research 47, no. 20 (October 4, 2019): 10580–96. http://dx.doi.org/10.1093/nar/gkz799.

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Abstract Chromatin accessibility maps are important for the functional interpretation of the genome. Here, we systematically analysed assay specific differences between DNase I-seq, ATAC-seq and NOMe-seq in a side by side experimental and bioinformatic setup. We observe that most prominent nucleosome depleted regions (NDRs, e.g. in promoters) are roboustly called by all three or at least two assays. However, we also find a high proportion of assay specific NDRs that are often ‘called’ by only one of the assays. We show evidence that these assay specific NDRs are indeed genuine open chromatin sites and contribute important information for accurate gene expression prediction. While technically ATAC-seq and DNase I-seq provide a superb high NDR calling rate for relatively low sequencing costs in comparison to NOMe-seq, NOMe-seq singles out for its genome-wide coverage allowing to not only detect NDRs but also endogenous DNA methylation and as we show here genome wide segmentation into heterochromatic B domains and local phasing of nucleosomes outside of NDRs. In summary, our comparisons strongly suggest to consider assay specific differences for the experimental design and for generalized and comparative functional interpretations.
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Taing, Len, Gali Bai, Clara Cousins, Paloma Cejas, Xintao Qiu, Zachary T. Herbert, Myles Brown, et al. "CHIPS: A Snakemake pipeline for quality control and reproducible processing of chromatin profiling data." F1000Research 10 (June 30, 2021): 517. http://dx.doi.org/10.12688/f1000research.52878.1.

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Motivation: The chromatin profile measured by ATAC-seq, ChIP-seq, or DNase-seq experiments can identify genomic regions critical in regulating gene expression and provide insights on biological processes such as diseases and development. However, quality control and processing chromatin profiling data involves many steps, and different bioinformatics tools are used at each step. It can be challenging to manage the analysis. Results: We developed a Snakemake pipeline called CHIPS (CHromatin enrIchment ProcesSor) to streamline the processing of ChIP-seq, ATAC-seq, and DNase-seq data. The pipeline supports single- and paired-end data and is flexible to start with FASTQ or BAM files. It includes basic steps such as read trimming, mapping, and peak calling. In addition, it calculates quality control metrics such as contamination profiles, polymerase chain reaction bottleneck coefficient, the fraction of reads in peaks, percentage of peaks overlapping with the union of public DNaseI hypersensitivity sites, and conservation profile of the peaks. For downstream analysis, it carries out peak annotations, motif finding, and regulatory potential calculation for all genes. The pipeline ensures that the processing is robust and reproducible. Availability: CHIPS is available at https://github.com/liulab-dfci/CHIPS.
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Koohy, Hashem, Thomas A. Down, Mikhail Spivakov, and Tim Hubbard. "A Comparison of Peak Callers Used for DNase-Seq Data." PLoS ONE 9, no. 5 (May 8, 2014): e96303. http://dx.doi.org/10.1371/journal.pone.0096303.

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Tarbell, Evan D., and Tao Liu. "HMMRATAC: a Hidden Markov ModeleR for ATAC-seq." Nucleic Acids Research 47, no. 16 (June 14, 2019): e91-e91. http://dx.doi.org/10.1093/nar/gkz533.

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Abstract ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragments that contain additional nucleosome positioning information. We present the first dedicated ATAC-seq analysis tool, a semi-supervised machine learning approach named HMMRATAC. HMMRATAC splits a single ATAC-seq dataset into nucleosome-free and nucleosome-enriched signals, learns the unique chromatin structure around accessible regions, and then predicts accessible regions across the entire genome. We show that HMMRATAC outperforms the popular peak-calling algorithms on published human ATAC-seq datasets. We find that single-end sequenced or size-selected ATAC-seq datasets result in a loss of sensitivity compared to paired-end datasets without size-selection.
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Cho, Jin Sun, Ira L. Blitz, and Ken W. Y. Cho. "DNase-seq to Study Chromatin Accessibility in Early Xenopus tropicalis Embryos." Cold Spring Harbor Protocols 2019, no. 4 (August 21, 2018): pdb.prot098335. http://dx.doi.org/10.1101/pdb.prot098335.

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Wang, Jiayin, Liubin Chen, Xuanping Zhang, Yao Tong, and Tian Zheng. "OCRDetector: Accurately Detecting Open Chromatin Regions via Plasma Cell-Free DNA Sequencing Data." International Journal of Molecular Sciences 22, no. 11 (May 28, 2021): 5802. http://dx.doi.org/10.3390/ijms22115802.

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Open chromatin regions (OCRs) are special regions of the human genome that can be accessed by DNA regulatory elements. Several studies have reported that a series of OCRs are associated with mechanisms involved in human diseases, such as cancers. Identifying OCRs using ATAC-seq or DNase-seq is often expensive. It has become popular to detect OCRs from plasma cell-free DNA (cfDNA) sequencing data, because both the fragmentation modes of cfDNA and the sequencing coverage in OCRs are significantly different from those in other regions. However, it is a challenging computational problem to accurately detect OCRs from plasma cfDNA-seq data, as multiple factors—e.g., sequencing and mapping bias, insufficient read depth, etc.—often mislead the computational model. In this paper, we propose a novel bioinformatics pipeline, OCRDetector, for detecting OCRs from whole-genome cfDNA sequencing data. The pipeline calculates the window protection score (WPS) waveform and the cfDNA sequencing coverage. To validate the proposed pipeline, we compared the percentage overlap of our OCRs with those obtained by other methods. The experimental results show that 81% of the TSS regions of housekeeping genes are detected, and our results have obvious tissue specificity. In addition, the overlap percentage between our OCRs and the high-confidence OCRs obtained by ATAC-seq or DNase-seq is greater than 70%.
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Dissertations / Theses on the topic "DNase-seq"

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Hashimoto, Tatsunori B. (Tatsunori Benjamin). "Computation identification of transcription factor binding using DNase-seq." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87945.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 41-43).
Here we describe Protein Interaction Quantitation (PIQ), a computational method that models the magnitude and shape of genome-wide DNase profiles to facilitate the identification of transcription factor (TF) binding sites. Through the use of machine learning techniques, PIQ identified binding sites for >700 TFs from one DNase-seq experiment with accuracy comparable to ChIP-seq for motif-associated TFs (median AUC=0.93 across 303 TFs). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into pre-pancreatic and intestinal endoderm. We identified (n=120) and experimentally validated eight 'pioneer' TF families that dynamically open chromatin, enabling other TFs to bind to adjacent DNA. Four pioneer TF families only open chromatin in one direction from their motifs. Furthermore, we identified a class of 'settler' TFs whose genomic binding is principally governed by proximity to open chromatin. Our results support a model of hierarchical TF binding in which directional and non-directional pioneer activity shapes the chromatin landscape for population by settler TFs. Substational parts of this thesis are taken from our publication on PIQ currently in press at Nature biotechnology.
by Tatsunori B. Hashimoto.
S.M.
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Hosseini, Mona. "Genome-wide DNaseI hypersensitive sites profiles in laboratory mouse strains by DNase-seq." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:c76109fc-93b5-4e0b-b7df-0277cbf527a9.

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Variation at regulatory elements, identified through hypersensitivity to digestion by Deoxyribonuclease I (DNase I), is believed to contribute to variation in complex traits, but the extent and consequences of this variation are poorly characterized. To investigate the relationship between sequence variation, and the functional consequences of variation in chromatin accessibility, genome-wide DNase I hypersensitive sites (DHS) of terminally differentiated erythroblasts were studied in eight inbred strains of mice studied (A/J, AKR/J, BALBc/J, C3H/HeJ, C57BL/6J, CBA/J, DBA/2J, and LP/J). These strains were selected because of the availability of their genome sequence and quantitative trait loci (QTL) data. After confirming that next generation sequencing could identify DNase I hypersensitive sites with high sensitivity and specificity, and that differential peaks could be found, an automated peak calling pipeline was developed and optimized. 36,693 DHS peaks were identified covering 9.1 Mb (0.29%) of mouse genome. There was no indication of within strain variation. Between strains reproducible variation was observed at approximately 5% of DNase hypersensitive sites (1,397 DHSs). Variable DHSs were more likely to be enhancers than promoters and less likely to occur at conserved regions of the genome. Only 36% of such variable DHSs contain a sequence variant predictive of site variation and 12% contain at least one variant that disrupts transcription factor binding sites. The majority (86%) of variable DHSs differ in size/shape and the remaining 14% demonstrate discrete variation in single peak or cluster of peaks. Sequence variants within variable DHS are more likely to be associated with complex traits than those in non-variant DHS, and variants associated with complex traits preferentially occur in enhancer-like elements. Changes at a small proportion (7%) of discretely variable DHS are associated with changes in nearby transcriptional activity. Our results show that whilst DNA sequence variation is not the major determinant of variation in open chromatin, where such variants exist they are likely to be causal for complex traits.
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Piper, Jason. "The demarcation of transcription factor binding sites through the analysis of DNase-seq data." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/71314/.

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The expression of eukaryotic genes is controlled by non-coding regulatory elements such as promoters and enhancers, which bind sequence-specific DNA-binding proteins (transcription factors). In multicellular organisms, the characterisation of these elements is required in order to understand how a single genome is utilised to generate a multitude of cell types, and how aberrant regulation of transcription contributes to disease processes. This involves the identification of transcription factor binding sites within regulatory elements that are occupied in a defined regulatory context. Digestion with DNase I and the subsequent analysis of regions protected from digestion followed by high-throughput sequencing (DNase-seq footprinting), allows for the quantification of genome-wide transcription factor binding. However, the handful of methods for analysing DNase-seq data has not been extensively validated or benchmarked. This thesis describes a novel footprinting algorithm, Wellington, which is presented in the context of a comprehensive comparison of several other DNase-seq footprinting algorithms on a multitude of datasets. Wellington outperforms other methods in almost all situations. An open-source software package, pyDNase, that facilitates interacting with DNase-seq data and provides many tools for DNase-seq analysis is also presented. Wellington is used to perform footprinting on clinical samples to validate cell lines as a model system, and to identify the binding partners of the RUNX1/ETO fusion protein in t(8;21) AML. By expanding the Wellington method, differential footprinting is shown to be able to link differences in transcription factor binding at promoters to changes in gene expression. Applying this methodology to a range of haematopoietic cell types illustrates the ability for differential footprinting to identify key regulators in the haematopoietic lineage. These results represent advances in the methods available to analyse DNase-seq data (all of which have been released as free, opensource software) and demonstrate the power of integrating DNase-seq footprinting with other functional genomic assays to study transcriptional regulation.
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Karabacak, Calviello Aslihan. "Characterization of cis-regulatory elements via open chromatin profiling." Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20339.

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Cis-regulatorische Elemente wie Promotoren und Enhancer, die die Regulation der Transkription von Genen steuern, befinden sich in Regionen des dekondensierten Chromatins. DNase-seq und ATAC-seq sind weit verbreitete Verfahren, um solche offenen Chromatinregionen genomweit zu untersuchen. Die einzel-Nukleotid-Auflösung von DNase-seq wurde des Weiteren genutzt, um Transkriptionsfaktor-Bindungsstellen (TFBS) in regulatorischen Regionen durch TF-Footprinting zu bestimmen. Kürzlich durchgeführte Studien haben jedoch gezeigt, dass DNase I einen Sequenzbias aufweist, welcher nachteilige Auswirkungen auf die Footprinting-Effizienz hat. Auch wurden das Footprinting und die Auswirkungen des Sequenzbias auf ATAC-seq noch nicht umfassend untersucht. In dieser Arbeit nehme ich einen systematischen Vergleich der beiden Methoden vor und zeige, dass die beiden Methoden unterschiedliche Sequenzbiases haben und korrigiere diese protokollspezifischen Biases beim Footprinting. Der Einfluss von Bias-Korrekturen der Footprinting Ergebnisse ist für DNase-seq größer als für ATAC-seq, und Footprinting mit DNase-seq führt zu besseren Ergebnissen in unserer Datensätze. Trotz dieser Unterschiede zeige ich, dass die Integration replizierter Experimente die Ableitung von qualitativ hochwertigen Footprints ermöglicht, wobei die beiden Techniken weitgehend übereinstimmen. Diese Techniken werden ferner eingesetzt, um die cis-regulatorischen Elemente zu charakterisieren, die die Embryogenese der Fruchtfliege Drosophila melanogaster bestimmen. Durch die Verwendung von Embryonen die sich im richtigen Entwicklungsstadium befinden, sowie gewebespezifischer Kernsortierung mit offenem Chromatin-Profiling können zeitlich und gewebespezifisch aufgelöste vermeintliche cis-regulatorische Elemente definiert werden. Zusammengenommen demonstrieren diese Analysen die Fähigkeit der offenen Chromatin-Profilierung und der Computeranalyse zur Aufklärung der Mechanismen der Genregulation.
Cis-regulatory elements such as promoters and enhancers, that govern transcriptional gene regulation, reside in regions of open chromatin. 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 (TFBS) in regulatory regions through TF footprinting. However, recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. Furthermore, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq. In this thesis, I undertake a systematic comparison of the two methods and demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. The impact of bias correction on footprinting performance is greater for DNase-seq than for ATAC-seq, and footprinting with DNase-seq leads to better performance in our datasets. Despite these differences, I show that integrating replicate experiments allows the inference of high-quality footprints, with substantial agreement between the two techniques. These techniques are further employed to characterize the cis-regulatory elements governing the embryogenesis of a complex organism, the fruit fly Drosophila melanogaster. Combining tight staging of embryos and tissue-specific nuclear sorting with open chromatin profiling, enables the definition of temporally and tissue-specifically resolved putative cis-regulatory elements. Taken together, these analyses demonstrate the power of open chromatin profiling and computational analysis in elucidating the mechanisms of transcriptional gene regulation.
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Johansson, Annelie. "Identifying gene regulatory interactions using functional genomics data." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230285.

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Previously studies used correlation of DNase I hypersensitivity sites sequencing (DNase-seq) experiments to predict interactions between enhancers and its target promoter gene. We investigate the correlation methods Pearson’s correlation and Mutual Information, using DNase-seq data for 100 cell-types in regions on chromosome one. To assess the performances, we compared our results of correlation scores to Hi-C data from Jin et al. 2013. We showed that the performances are low when comparing it to the Hi-C data, and there is a need of improved correlation metrics. We also demonstrate that the use of Hi-C data as a gold standard is limited, because of its low resolution, and we suggest using another gold standard in further studies.
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Purcaro, Michael J. "Analysis, Visualization, and Machine Learning of Epigenomic Data." eScholarship@UMMS, 2017. https://escholarship.umassmed.edu/gsbs_diss/938.

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The goal of the Encyclopedia of DNA Elements (ENCODE) project has been to characterize all the functional elements of the human genome. These elements include expressed transcripts and genomic regions bound by transcription factors (TFs), occupied by nucleosomes, occupied by nucleosomes with modified histones, or hypersensitive to DNase I cleavage, etc. Chromatin Immunoprecipitation (ChIP-seq) is an experimental technique for detecting TF binding in living cells, and the genomic regions bound by TFs are called ChIP-seq peaks. ENCODE has performed and compiled results from tens of thousands of experiments, including ChIP-seq, DNase, RNA-seq and Hi-C. These efforts have culminated in two web-based resources from our lab—Factorbook and SCREEN—for the exploration of epigenomic data for both human and mouse. Factorbook is a peak-centric resource presenting data such as motif enrichment and histone modification profiles for transcription factor binding sites computed from ENCODE ChIP-seq data. SCREEN provides an encyclopedia of ~2 million regulatory elements, including promoters and enhancers, identified using ENCODE ChIP-seq and DNase data, with an extensive UI for searching and visualization. While we have successfully utilized the thousands of available ENCODE ChIP-seq experiments to build the Encyclopedia and visualizers, we have also struggled with the practical and theoretical inability to assay every possible experiment on every possible biosample under every conceivable biological scenario. We have used machine learning techniques to predict TF binding sites and enhancers location, and demonstrate machine learning is critical to help decipher functional regions of the genome.
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Moore, Jill E. "Defining a Registry of Candidate Regulatory Elements to Interpret Disease Associated Genetic Variation." eScholarship@UMMS, 2017. https://escholarship.umassmed.edu/gsbs_diss/927.

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Over the last decade there has been a great effort to annotate noncoding regions of the genome, particularly those that regulate gene expression. These regulatory elements contain binding sites for transcription factors (TF), which interact with one another and transcriptional machinery to initiate, enhance, or repress gene expression. The Encyclopedia of DNA Elements (ENCODE) consortium has generated thousands of epigenomic datasets, such as DNase-seq and ChIP-seq experiments, with the goal of defining such regions. By integrating these assays, we developed the Registry of candidate Regulatory Elements (cREs), a collection of putative regulatory regions across human and mouse. In total, we identified over 1.3M human and 400k mouse cREs each annotated with cell-type specific signatures (e.g. promoter-like, enhancer-like) in over 400 human and 100 mouse biosamples. We then demonstrated the biological utility of these regions by analyzing cell type enrichments for genetic variants reported by genome wide association studies (GWAS). To search and visualize these cREs, we developed the online database SCREEN (search candidate regulatory elements by ENCODE). After defining cREs, we next sought to determine their potential gene targets. To compare target gene prediction methods, we developed a comprehensive benchmark of enhancer-gene links by curating ChIA-PET, Hi-C and eQTL datasets. We then used this benchmark to evaluate unsupervised linking approaches such as the correlation of epigenomic signal. We determined that these methods have low overall performance and do not outperform simply selecting the closest gene. We then developed a supervised Random Forest model which had notably better performance than unsupervised methods. We demonstrated that this model can be applied across cell types and can be used to predict target genes for GWAS associated variants. Finally, we used the registry of cREs to annotate variants associated with psychiatric disorders. We found that these "psych SNPs" are enriched in cREs active in brain tissue and likely target genes involved in neural development pathways. We also demonstrated that psych SNPs overlap binding sites for TFs involved in neural and immune pathways. Finally, by identifying psych SNPs with allele imbalance in chromatin accessibility, we highlighted specific cases of psych SNPs altering TF binding motifs resulting in the disruption of TF binding. Overall, we demonstrated our collection of putative regulatory regions, the Registry of cREs, can be used to understand the potential biological function of noncoding variation and develop hypotheses for future testing.
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Yardimci, Galip Gurkan. "Tracking Transcription Factors on the Genome by their DNase-seq Footprints." Diss., 2014. http://hdl.handle.net/10161/9084.

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Transcription factors control numerous vital processes in the cell through their ability to control gene expression. Dysfunctional regulation by transcription factors lead to disorders and disease. Transcription factors regulate gene expression by binding to DNA sequences (motifs) on the genome and altering chromatin. DNase-seq footprinting is a well-established assay for identification of DNA sequences that bind to transcription factors. We developed computational techniques to analyze footprints and predict transcription factor binding. These transcription factor specific predictive models are able to correct for DNase sequence bias and characterize variation in DNA binding sequence. We found that DNase-seq footprints are able to identify cell-type or condition specific transcription factor activity and may offer information about the type of the interaction between DNA and transcription factor. Our DNase-seq footprint model is able to accurately discover high confidence transcription factor binding sites and discover alternative interactions between transcription factors and DNA. DNase-seq footprints can be used with ChIP-seq data to discover true binding sites and better understand transcription regulation.


Dissertation
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Sugathan, Aarathi. "Role of growth hormone and chromatin structure in regulation of sex differences in mouse liver gene expression." Thesis, 2013. https://hdl.handle.net/2144/13139.

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Sex differences in mammalian gene expression result from differences in genotypic sex as well as in hormonal regulators between males and females. In rat, mouse and human liver, ~1000 genes are expressed in a sex-dependent manner, and contribute to sex differences in metabolism of drugs, steroids and lipids, and in liver and cardiovascular disease risk. In rats and mice, sex-biased liver gene expression is primarily dictated by the sexually dimorphic pattern of pituitary growth hormone (GH) release and its STAT5-dependent transcriptional activities. Studies presented in this thesis include the following. (1) A computational approach based on DNA sequence and phylogenetic conservation was developed and used to identify novel functional STAT5 binding sites - both consensus and non-consensus STAT5 sequences - near prototypic GH-responsive genes. (2) Global gene expression analysis of livers from pituitary-ablated male and female mice identified four major classes of sex-biased genes differing in their profiles of GH dependence. (3) Sex-differences in DNase-hypersensitive sites (DHS, corresponding to open chromatin regions) were identified genome-wide in mouse liver. These sex-differential DHSs were enriched for association with sex-biased genes, but a majority was distant from sex-biased genes. Furthermore, many were responsive to GH treatment, demonstrating that GH-mediated regulation involves chromatin remodeling. Analysis of sequence motifs enriched at sex-biased DHSs implicated STAT5 and novel transcription factors such as PBX1 and TAL1 in sex-biased gene regulation. (4) Genome-wide mapping of histone modifications revealed distinct mechanisms of sex-biased gene regulation in male and female liver: sex-dependent K27me3-mediated repression is an important mechanism of repression of female-biased, but not of male-biased, genes, and a sex-dependent K4me1 distribution, suggesting nucleosome repositioning by pioneer factors, is observed at male-biased, but not female-biased, regulatory sites. STAT5-mediated activation was most strongly associated with sex-biased chromatin modifications, while BCL6-mediated repression primarily occurs in association with sex-independent chromatin modifications, both at binding sites and at target genes. The relationships between sex-dependent chromatin accessibility, chromatin modifications and transcription-factor binding uncovered by these studies help elucidate the molecular mechanisms governing sex-differential gene expression, and underscore the utility of functional genomic and epigenetic studies as tools for elucidating transcriptional regulation in complex mammalian systems.
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Rampersaud, Andy. "Chromatin accessibility and epigenetic changes induced by xenobiotic and hormone exposure in young adult mouse liver." Thesis, 2019. https://hdl.handle.net/2144/39470.

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Transcription factors activated by exogenous or endogenous stimuli alter gene expression with major effects on chromatin accessibility and the epigenome. This thesis investigates that impact of environmental chemical and hormonal exposure on liver chromatin accessibility in a mouse liver model. Exposure to the constitutive androstane receptor (CAR)-specific agonist ligand 1,4-bis-[2-(3,5-dichloropyridyloxy)]benzene (TCPOBOP) induces localized changes in chromatin accessibility at several thousand DNase hypersensitive sites (DHS). Activating histone marks, associated with enhancers and promoters, were induced by TCPOBOP and were highly enriched at opening DHS. Opening DHS were highly enriched for CAR binding sites and nuclear receptor direct repeat-4 motifs. These DHS were also enriched for the CAR heterodimeric partner RXRA, binding by CEBPA and CEBPB, and motifs for other liver-specific factors. Thus, TCPOBOP alters the enhancer landscape through changes in histone marks and by mechanisms linked to induced CAR binding. In other studies, the impact of pituitary growth hormone (GH) secretion patterns on chromatin accessibility changes associated with sex-biased liver gene expression was examined. In adult male liver, the transcription factor STAT5 is directly activated by each successive plasma GH pulse. In female liver, STAT5 is persistently activated by the near-continuous stimulation by plasma GH. A majority of the ~4,000 GH-regulated, sex-biased DHS have chromatin marks characteristic of enhancers and were enriched for proximity to sex-biased gene promoters. Chromatin accessibility is thus a key feature of sex-differential gene expression. Two major classes of male-biased DHS were identified: dynamic male-biased DHS, almost all bound by STAT5, which undergo repeated cycles of chromatin opening and closing induced by each GH pulse; and static male-biased DHS, whose accessibility is unaffected GH/STAT5 pulses and whose sex bias results from these chromatin sites being more closed in female liver. Sites with STAT5 binding showed greater chromatin opening, many of which also contain the STAT5 motif. Finally, the effect of a single GH pulse on hypophysectomized male mouse liver was investigated to identify DHS responsive to the male, pulsatile-GH, secretion pattern. These studies demonstrate that widespread epigenetic changes associated with target gene expression are induced by xenobiotics and hormones regulating liver gene expression.
2022-01-31T00:00:00Z
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Book chapters on the topic "DNase-seq"

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Moyano, Tomás C., Rodrigo A. Gutiérrez, and José M. Alvarez. "Genomic Footprinting Analyses from DNase-seq Data to Construct Gene Regulatory Networks." In Modeling Transcriptional Regulation, 25–46. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1534-8_3.

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Conference papers on the topic "DNase-seq"

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Xu, Siwen, Ying Wang, Huan Liu, Duojiao Chen, Hongyuan Bi, and Weixing Feng. "A new method for alleviating sequence-specific biases in DNase-seq." In 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS). IEEE, 2017. http://dx.doi.org/10.1109/eiis.2017.8298582.

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Sang, Peichao, Duojiao Chen, Siwen Xu, and Weixing Feng. "Identification method of transcription factor binding sites based on DNase-Seq signal." In 2015 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2015. http://dx.doi.org/10.1109/icma.2015.7237735.

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3

Shams, Shayan, Richard Platania, Joohyun Kim, Jian Zhang, Kisung Lee, Seungwon Yang, and Seung-Jong Park. "A Distributed Semi-Supervised Platform for DNase-Seq Data Analytics using Deep Generative Convolutional Networks." In BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3233547.3233601.

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