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  • Cold Spring Harbor Laboratory  (2)
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  • Cold Spring Harbor Laboratory  (2)
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  • 1
    Online Resource
    Online Resource
    Cold Spring Harbor Laboratory ; 2019
    In:  Genome Research Vol. 29, No. 4 ( 2019-04), p. 657-667
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 29, No. 4 ( 2019-04), p. 657-667
    Abstract: Compared to enhancers, silencers are notably difficult to identify and validate experimentally. In search for human silencers, we utilized H3K27me3-DNase I hypersensitive site (DHS) peaks with tissue specificity negatively correlated with the expression of nearby genes across 25 diverse cell lines. These regions are predicted to be silencers since they are physically linked, using Hi-C loops, or associated, using expression quantitative trait loci (eQTL) results, with a decrease in gene expression much more frequently than general H3K27me3-DHSs. Also, these regions are enriched for the binding sites of transcriptional repressors (such as CTCF, MECOM, SMAD4, and SNAI3) and depleted of the binding sites of transcriptional activators. Using sequence signatures of these regions, we constructed a computational model and predicted approximately 10,000 additional silencers per cell line and demonstrated that the majority of genes linked to these silencers are expressed at a decreased level. Furthermore, single nucleotide polymorphisms (SNPs) in predicted silencers are significantly associated with disease phenotypes. Finally, our results show that silencers commonly interact with enhancers to affect the transcriptional dynamics of tissue-specific genes and to facilitate fine-tuning of transcription in the human genome.
    Type of Medium: Online Resource
    ISSN: 1088-9051 , 1549-5469
    RVK:
    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2019
    detail.hit.zdb_id: 1483456-X
    SSG: 12
    Location Call Number Limitation Availability
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  • 2
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 31, No. 10 ( 2021-10), p. 1843-1855
    Abstract: Recent technological advances have enabled spatially resolved measurements of expression profiles for hundreds to thousands of genes in fixed tissues at single-cell resolution. However, scalable computational analysis methods able to take into consideration the inherent 3D spatial organization of cell types and nonuniform cellular densities within tissues are still lacking. To address this, we developed MERINGUE, a computational framework based on spatial autocorrelation and cross-correlation analysis to identify genes with spatially heterogeneous expression patterns, infer putative cell–cell communication, and perform spatially informed cell clustering in 2D and 3D in a density-agnostic manner using spatially resolved transcriptomic data. We applied MERINGUE to a variety of spatially resolved transcriptomic data sets including multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial transcriptomics, Slide-seq, and aligned in situ hybridization (ISH) data. We anticipate that such statistical analysis of spatially resolved transcriptomic data will facilitate our understanding of the interplay between cell state and spatial organization in tissue development and disease.
    Type of Medium: Online Resource
    ISSN: 1088-9051 , 1549-5469
    RVK:
    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2021
    detail.hit.zdb_id: 1483456-X
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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