atac seq data (10X Genomics)
86
Structured Review
10X Genomics
atac seq data
![The ocrRBBR framework for OCR-driven Boolean rule inference explaining gene expression variability. ( A ) In the mouse multiome dataset, nine blood cell lineages—stromal cells, stem cells, DC, myeloid cells, ILC, B, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\alpha \beta$\end{document} T, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\gamma \delta$\end{document} T, and activated T (ActT) cells—are shown in distinct colors. ( B <t>)</t> <t>ATAC-seq</t> data are used to identify all OCRs within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\pm$\end{document} 100 kb of gene promoters. ocrRBBR derives Boolean rules among OCRs to explain gene expression variability, as measured by RNA-seq, across 85 cell types spanning nine blood lineages. ( C ) Candidate models are constructed using all combinations of single-, double-, and triple-OCR subsets from the available OCR repertoire (e.g. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\lbrace OCR_A, OCR_B, OCR_C, \dots , OCR_H, OCR_K, OCR_L\rbrace$\end{document} ). ( D ) Each OCR subset is transformed into a set of Boolean rules, which serve as inputs to a ridge regression model used to predict gene expression across cell types. For example, the double-OCR subset \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\lbrace OCR_A, OCR_D\rbrace$\end{document} yields rules such as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(OCR_A \wedge OCR_D)$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(\lnot OCR_A \wedge OCR_D)$\end{document} , and so on. Boolean rules receiving positive (orange) coefficients in the fitted model are associated with cell types where the gene is expressed, whereas those with negative (blue) coefficients correspond to cell types with low or no expression. ( E ) Fitted models and their associated Boolean rule sets—such as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(OCR_A \wedge OCR_D) \ \mathrm{or}\ (\lnot OCR_A \wedge OCR_D)$\end{document} , and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(OCR_A \wedge OCR_B \wedge OCR_D) \ \mathrm{or}\ (\lnot OCR_A \wedge OCR_B \wedge OCR_D)$\end{document} —are ranked according to their BIC scores. ( F ) Boolean rules are categorized based on the cell types in which they act as active regulators of gene expression.](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_8175/pmc13148175/pmc13148175__gkag230fig1.jpg)
Atac Seq Data, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/atac+seq+data/pmc13148175-130-3-16?v=10X+Genomics
Average 86 stars, based on 1 article reviews
Atac Seq Data, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/atac+seq+data/pmc13148175-130-3-16?v=10X+Genomics
Average 86 stars, based on 1 article reviews
atac seq data - by Bioz Stars,
2026-07
86/100 stars
Images
1) Product Images from "Boolean logic links chromatin accessibility states to gene expression variability across cell types"
Article Title: Boolean logic links chromatin accessibility states to gene expression variability across cell types
Journal: Nucleic Acids Research
doi: 10.1093/nar/gkag230
Figure Legend Snippet: The ocrRBBR framework for OCR-driven Boolean rule inference explaining gene expression variability. ( A ) In the mouse multiome dataset, nine blood cell lineages—stromal cells, stem cells, DC, myeloid cells, ILC, B, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\alpha \beta$\end{document} T, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\gamma \delta$\end{document} T, and activated T (ActT) cells—are shown in distinct colors. ( B ) ATAC-seq data are used to identify all OCRs within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\pm$\end{document} 100 kb of gene promoters. ocrRBBR derives Boolean rules among OCRs to explain gene expression variability, as measured by RNA-seq, across 85 cell types spanning nine blood lineages. ( C ) Candidate models are constructed using all combinations of single-, double-, and triple-OCR subsets from the available OCR repertoire (e.g. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\lbrace OCR_A, OCR_B, OCR_C, \dots , OCR_H, OCR_K, OCR_L\rbrace$\end{document} ). ( D ) Each OCR subset is transformed into a set of Boolean rules, which serve as inputs to a ridge regression model used to predict gene expression across cell types. For example, the double-OCR subset \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\lbrace OCR_A, OCR_D\rbrace$\end{document} yields rules such as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(OCR_A \wedge OCR_D)$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(\lnot OCR_A \wedge OCR_D)$\end{document} , and so on. Boolean rules receiving positive (orange) coefficients in the fitted model are associated with cell types where the gene is expressed, whereas those with negative (blue) coefficients correspond to cell types with low or no expression. ( E ) Fitted models and their associated Boolean rule sets—such as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(OCR_A \wedge OCR_D) \ \mathrm{or}\ (\lnot OCR_A \wedge OCR_D)$\end{document} , and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(OCR_A \wedge OCR_B \wedge OCR_D) \ \mathrm{or}\ (\lnot OCR_A \wedge OCR_B \wedge OCR_D)$\end{document} —are ranked according to their BIC scores. ( F ) Boolean rules are categorized based on the cell types in which they act as active regulators of gene expression.
Techniques Used: Gene Expression, RNA Sequencing, Construct, Transformation Assay, Expressing
![OCR-driven Boolean rules for Spi1 are associated with cell-type-specific chromatin structures involved in its regulation. ( A ) ATAC-seq readouts within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\pm$\end{document} 100 kb of the Spi1 promoter identify candidate OCRs, and RNA-seq quantifies Spi1 expression levels across 85 cell types spanning nine blood lineages. ( B ) A double-OCR subset \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\lbrace \mathrm{E}_{-12}, \mathrm{Promoter}\rbrace$\end{document} —including an enhancer located 12 kb upstream of the TSS and the promoter—predicts gene expression variability with the optimal BIC. In the circular heatmap, blood lineages are color-coded similar to panel (A); OCR accessibility, Spi1 expression, and Boolean rule values are shown, with cold colors representing low levels and warm colors high levels. ( C, D ) MSE and Boolean rule coefficients are shown as a function of the regularization parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\log (\lambda )$\end{document} in the fitted ridge regression model, where Boolean rules derived from the double-OCR subset are used as predictors of Spi1 expression. ( E ) Two boolean rules \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(\mathrm{E}_{-12} \wedge \mathrm{Promoter}) \ \mathrm{or}\ (\lnot \mathrm{E}_{-12} \wedge \mathrm{Promoter})$\end{document} receive positive coefficients, corresponding to chromatin structures associated with gene regulation in myeloid and B cells.](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_8175/pmc13148175/pmc13148175__gkag230fig2.jpg "... chromatin structures involved in its regulation. ( A ) ATAC-seq readouts within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} ...")
Figure Legend Snippet: OCR-driven Boolean rules for Spi1 are associated with cell-type-specific chromatin structures involved in its regulation. ( A ) ATAC-seq readouts within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\pm$\end{document} 100 kb of the Spi1 promoter identify candidate OCRs, and RNA-seq quantifies Spi1 expression levels across 85 cell types spanning nine blood lineages. ( B ) A double-OCR subset \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\lbrace \mathrm{E}_{-12}, \mathrm{Promoter}\rbrace$\end{document} —including an enhancer located 12 kb upstream of the TSS and the promoter—predicts gene expression variability with the optimal BIC. In the circular heatmap, blood lineages are color-coded similar to panel (A); OCR accessibility, Spi1 expression, and Boolean rule values are shown, with cold colors representing low levels and warm colors high levels. ( C, D ) MSE and Boolean rule coefficients are shown as a function of the regularization parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\log (\lambda )$\end{document} in the fitted ridge regression model, where Boolean rules derived from the double-OCR subset are used as predictors of Spi1 expression. ( E ) Two boolean rules \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(\mathrm{E}_{-12} \wedge \mathrm{Promoter}) \ \mathrm{or}\ (\lnot \mathrm{E}_{-12} \wedge \mathrm{Promoter})$\end{document} receive positive coefficients, corresponding to chromatin structures associated with gene regulation in myeloid and B cells.
Techniques Used: RNA Sequencing, Expressing, Gene Expression, Derivative Assay
Figure Legend Snippet: Cell-type-specific OCR-driven Boolean rules predict gene expression from ATAC-seq data. ( A ) Heatmap of gene scores derived from 1194 cell-type-specific Boolean rules associated with 661 genes. The Boolean rules were selected based on differential gene score patterns across various blood cell lineages, reflecting cell-type-specific regulatory logic. ( B ) Corresponding RNA-seq expression levels for the genes shown in panel a, demonstrating strong concordance between the Boolean rule-derived gene scores and transcriptomic measurements. This comparison highlights the predictive power of the OCR-driven Boolean rules for gene expression across different cell types.
Techniques Used: Gene Expression, Derivative Assay, RNA Sequencing, Expressing, Comparison
