20 research outputs found

    A Scalable Epitope Tagging Approach for High Throughput ChIP-Seq Analysis

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    Eukaryotic transcriptional factors (TFs) typically recognize short genomic sequences alone or together with other proteins to modulate gene expression. Mapping of TF-DNA interactions in the genome is crucial for understanding the gene regulatory programs in cells. While chromatin immunoprecipitation followed by sequencing (ChIP-Seq) is commonly used for this purpose, its application is severely limited by the availability of suitable antibodies for TFs. To overcome this limitation, we developed an efficient and scalable strategy named cmChIP-Seq that combines the clustered regularly interspaced short palindromic repeats (CRISPR) technology with microhomology mediated end joining (MMEJ) to genetically engineer a TF with an epitope tag. We demonstrated the utility of this tool by applying it to four TFs in a human colorectal cancer cell line. The highly scalable procedure makes this strategy ideal for ChIP-Seq analysis of TFs in diverse species and cell types

    Frequency of MAPS-identified interactions and control bin pairs versus their rankings in the SPRITE contact matrix (see Methods for details).

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    A bin pair with higher normalized SPRITE interaction frequency tends to rank top, among all bin pairs with the same genomic distance (only bin pairs in “AND” and “XOR” sets from the SPRITE contact matrix are considered).</p

    CTCF motif orientation of MAPS- and hichipper-identified interactions.

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    The proportion of convergent, tandem and divergent CTCF motif pairs among testable MAPS- and hichipper-identified interactions. Only interactions with both ends containing either single CTCF motif or multiple CTCF motifs in the same direction are considered. The dotted vertical line indicates the expected convergent proportion from randomly chosen CTCF motif pairs (25%).</p

    Enrichment of CREs around the target bins of XOR set of MAPS-specific interactions.

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    Enrichment of H3K27ac (ChIP-seq peaks), H3K4me1 (ChIP-seq peaks), ATAC-seq peaks, H3K4me3 (ChIP-seq peaks) and CTCF (ChIP-seq peaks) in a window of 500Kb around the target bins for all four datasets. Due to the definition of XOR set of interactions, H3K27ac, H3K4me3 and CTCF enrichment level is not analyzed for GM12878 H3K27ac HiChIP, mESC H3K4me3 and mESC CTCF PLAC-seq data, respectively.</p

    Comparison of sensitivity of MAPS and hichipper.

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    The Y-axis is the sensitivity, defined as the percentage of detectable HiCCUPS loops of deeply sequenced in situ Hi-C data (S6 Table) recovered by MAPS- or hichipper-identified interactions.</p

    Cis-regulatory elements are enriched in the target bins of MAPS-identified “XOR” interactions.

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    As only interactions from the “XOR” set are considered, CTCF enrichment analysis is not applicable for mESC CTCF PLAC-seq data, H3K4me3 enrichment analysis is not applicable for mESC H3K4me3 PLAC-seq data, and H3K27ac enrichment analysis is not applicable for GM12878 H3K27ac HiChIP data (denoted as N.A. in the heatmap). For each ChIP-seq/ATAC-seq data, we calculated the proportion of target bins and control bins containing ChIP-seq/ATAC-seq peaks, defined as %target and %control, respectively. We further defined the enrichment score as the ratio between %target and %control (numbers in S9 Table).</p

    MAPS-identified interactions from mESC H3K4me3 PLAC-seq data anchored at Med13l promoter.

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    Anchor region around target promoter is highlighted by yellow box. The MAPS-identified interactions overlapping this anchor region are marked by magenta arcs. The deleted enhancer region in Moorthy et al study [24] is marked by magenta box.</p
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