Analysis of DNase I-hypersensitive sites in the chromatin of the chicken adenosine receptor 2B gene reveals multiple cell-type-specific cis-regulatory elements

The Ly-6E.1/A.2 gene product recognized by the Sca-1 antibody has been found on murine hematopoietic stem cells and some hematopoietic precursors, T lymphocytes, and nonhematopoietic cell lineages, suggesting a complex array of gene regulatory elements. The ability to use the Ly6E.1/A.2 transcriptional regulatory elements to direct expression of heterologous genes will allow for the manipulation of these cells during development and in hematopoietic cell transplantations. To identify the elements necessary for high-level expression, we have made deletion constructs of Ly-6E.1 gene flanking regions containing DNase I hypersensitive sites, tested them for expression in hematopoietic cells, and have performed kinetic analyses to correlate the appearance of hypersensitive sites with gene transcription and protein expression. We show that a 3′ region containing two DNase I hypersensitive sites at +8.7 and +8.9 kb is required for high-level, gamma-interferon (gamma-IFN)-induced expression of the Ly-6E.1 gene and that a consensus sequence for a gamma-IFN-responsive element localizes to the +8.7 site. We also provide a description of allele- and cell-specific DNase I hypersensitive site patterns of the Ly-6E.1 and Ly-6A.2 genes. Taken together, these data indicate that while both 5′ and 3′ hypersensitive sites are rapidly induced with gamma-IFN, the 3′ most distal hypersensitive sites are involved in directing high levels of expression of Sca-1 in hematopoietic cells.

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accuEnhancer: Accurate enhancer prediction by integration of multiple cell type data with deep learning

10.1101/2020.11.10.375717 ◽

2020 ◽

Author(s):

Yi-An Tung ◽

Wen-Tse Yang ◽

Tsung-Ting Hsieh ◽

Yu-Chuan Chang ◽

June-Tai Wu ◽

...

Keyword(s):

Deep Learning ◽

Cell Types ◽

Regulatory Elements ◽

Sequence Motifs ◽

Cell Type ◽

Enhancer Activity ◽

Multiple Cell ◽

Type Data ◽

Different Cell Types ◽

Multiple Cell Type

AbstractEnhancers are one class of the regulatory elements that have been shown to act as key components to assist promoters in modulating the gene expression in living cells. At present, the number of enhancers as well as their activities in different cell types are still largely unclear. Previous studies have shown that enhancer activities are associated with various functional data, such as histone modifications, sequence motifs, and chromatin accessibilities. In this study, we utilized DNase data to build a deep learning model for predicting the H3K27ac peaks as the active enhancers in a target cell type. We propose joint training of multiple cell types to boost the model performance in predicting the enhancer activities of an unstudied cell type. The results demonstrated that by incorporating more datasets across different cell types, the complex regulatory patterns could be captured by deep learning models and the prediction accuracy can be largely improved. The analyses conducted in this study demonstrated that the cell type-specific enhancer activity can be predicted by joint learning of multiple cell type data using only DNase data and the primitive sequences as the input features. This reveals the importance of cross-cell type learning, and the constructed model can be applied to investigate potential active enhancers of a novel cell type which does not have the H3K27ac modification data yet.AvailabilityThe accuEnhancer package can be freely accessed at: https://github.com/callsobing/accuEnhancer

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Evaluation of potential regulatory elements identified as DNase I hypersensitive sites in the CFTR gene

European Journal of Biochemistry ◽

10.1046/j.0014-2956.2001.02679.x ◽

2002 ◽

Vol 269 (2) ◽

pp. 553-559 ◽

Cited By ~ 26

Author(s):

Marios Phylactides ◽

Rebecca Rowntree ◽

Hugh Nuthall ◽

David Ussery ◽

Ann Wheeler ◽

...

Keyword(s):

Regulatory Elements ◽

Dnase I ◽

Cftr Gene ◽

Dnase I Hypersensitive Sites ◽

Hypersensitive Sites

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Identifying DNase I hypersensitive sites as driver distal regulatory elements in breast cancer

Nature Communications ◽

10.1038/s41467-017-00100-x ◽

2017 ◽

Vol 8 (1) ◽

Cited By ~ 12

Author(s):

Matteo D′Antonio ◽

Donate Weghorn ◽

Agnieszka D′Antonio-Chronowska ◽

Florence Coulet ◽

Katrina M. Olson ◽

...

Keyword(s):

Breast Cancer ◽

Regulatory Elements ◽

Dnase I ◽

Dnase I Hypersensitive Sites ◽

Hypersensitive Sites ◽

Distal Regulatory Elements

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Genome-scale identification of Caenorhabditis elegans regulatory elements by tiling-array mapping of DNase I hypersensitive sites

BMC Genomics ◽

10.1186/1471-2164-10-92 ◽

2009 ◽

Vol 10 (1) ◽

pp. 92 ◽

Cited By ~ 10

Author(s):

Baochen Shi ◽

Xiangqian Guo ◽

Tao Wu ◽

Sitong Sheng ◽

Jie Wang ◽

...

Keyword(s):

Caenorhabditis Elegans ◽

Tiling Array ◽

Regulatory Elements ◽

Dnase I ◽

Dnase I Hypersensitive Sites ◽

Hypersensitive Sites ◽

Genome Scale

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Multiple cell-type-specific elements regulate Myc protein stability

Oncogene ◽

10.1038/sj.onc.1207492 ◽

2004 ◽

Vol 23 (21) ◽

pp. 3863-3871 ◽

Cited By ~ 28

Author(s):

Andreas Herbst ◽

Simone E Salghetti ◽

So Young Kim ◽

William P Tansey

Keyword(s):

Protein Stability ◽

Cell Type ◽

Multiple Cell ◽

Cell Type Specific ◽

Multiple Cell Type

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DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

10.1101/224527 ◽

2017 ◽

Cited By ~ 1

Author(s):

Balachandran Manavalan ◽

Tae Hwan Shin ◽

Gwang Lee

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Dna Sequences ◽

Feature Selection Method ◽

Regulatory Elements ◽

Dnase I ◽

Support Vector ◽

Large Set ◽

Dnase I Hypersensitive Sites ◽

Hypersensitive Sites

AbstractDNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at:http://www.thegleelab.org/DHSpred.html.

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