Similar Datasets
Project description:Abstract: Gene expression is controlled by transcription factors (TFs) that bind cognate DNA motifs in cis-regulatory elements (CREs), however, the combinations of motifs that mediate cell type-specific gene regulation are undefined. Chromatin accessibility, TF footprinting, and H3K27ac-dependent DNA looping data were generated to identify 7,531 cell type-specific CREs across 15 normal human cell types. A co-enrichment framework nominated 838 cell type-specific, recurrent heterotypic DNA motif combinations (DMCs), which were functionally validated using massively parallel reporter assays. Compared to their normal counterparts, cancer cells induced DMCs linked to neoplasia-enabling processes that operate in normal cells while also engaging entirely new DMCs. These data identify a cis-regulatory combinatorial DNA motif lexicon for cell type-specific gene regulation in diverse normal and diseased human cells. Purpose: To identify a cis-regulatory combinatorial DNA motif lexicon mediating cell type-specific gene regulation in diverse normal and diseased human cells, matched RNA-seq, chromatin accessibility profiling via ATAC-seq, and enhancer-promoter looping via H3K27ac HiChIP was performed in 15 primary human cell types and 6 human cancer cell lines. Processed and raw data for all cell types can be found in this repository. Squamous cell carcinoma cell line raw and processed data can be found at Jung, et al. "XXX". All biological replicates (n=2) are merged.
2022-08-17 | GSE188403 | GEO
Project description:Abstract: Gene expression is controlled by transcription factors (TFs) that bind cognate DNA motifs in cis-regulatory elements (CREs), however, the combinations of motifs that mediate cell type-specific gene regulation are undefined. Chromatin accessibility, TF footprinting, and H3K27ac-dependent DNA looping data were generated to identify 7,531 cell type-specific CREs across 15 normal human cell types. A co-enrichment framework nominated 838 cell type-specific, recurrent heterotypic DNA motif combinations (DMCs), which were functionally validated using massively parallel reporter assays. Compared to their normal counterparts, cancer cells induced DMCs linked to neoplasia-enabling processes that operate in normal cells while also engaging entirely new DMCs. These data identify a cis-regulatory combinatorial DNA motif lexicon for cell type-specific gene regulation in diverse normal and diseased human cells. Purpose: To identify a cis-regulatory combinatorial DNA motif lexicon mediating cell type-specific gene regulation in diverse normal and diseased human cells, matched RNA-seq, chromatin accessibility profiling via ATAC-seq, and enhancer-promoter looping via H3K27ac HiChIP was performed in 15 primary human cell types and 6 human cancer cell lines. Processed and raw data for all cell types can be found in this repository. Squamous cell carcinoma cell line raw and processed data can be found at Jung, et al. "XXX". All biological replicates (n=2) are merged.
2022-08-17 | GSE188401 | GEO
Project description:Abstract: Gene expression is controlled by transcription factors (TFs) that bind cognate DNA motifs in cis-regulatory elements (CREs), however, the combinations of motifs that mediate cell type-specific gene regulation are undefined. Chromatin accessibility, TF footprinting, and H3K27ac-dependent DNA looping data were generated to identify 7,531 cell type-specific CREs across 15 normal human cell types. A co-enrichment framework nominated 838 cell type-specific, recurrent heterotypic DNA motif combinations (DMCs), which were functionally validated using massively parallel reporter assays. Compared to their normal counterparts, cancer cells induced DMCs linked to neoplasia-enabling processes that operate in normal cells while also engaging entirely new DMCs. These data identify a cis-regulatory combinatorial DNA motif lexicon for cell type-specific gene regulation in diverse normal and diseased human cells. Purpose: To identify a cis-regulatory combinatorial DNA motif lexicon mediating cell type-specific gene regulation in diverse normal and diseased human cells, matched RNA-seq, chromatin accessibility profiling via ATAC-seq, and enhancer-promoter looping via H3K27ac HiChIP was performed in 15 primary human cell types and 6 human cancer cell lines. Processed and raw data for all cell types can be found in this repository. Squamous cell carcinoma cell line raw and processed data can be found at Jung, et al. "XXX". All biological replicates (n=2) are merged.
2022-08-17 | GSE188398 | GEO
Project description:Abstract: Gene expression is controlled by transcription factors (TFs) that bind cognate DNA motifs in cis-regulatory elements (CREs), however, the combinations of motifs that mediate cell type-specific gene regulation are undefined. Chromatin accessibility, TF footprinting, and H3K27ac-dependent DNA looping data were generated to identify 7,531 cell type-specific CREs across 15 normal human cell types. A co-enrichment framework nominated 838 cell type-specific, recurrent heterotypic DNA motif combinations (DMCs), which were functionally validated using massively parallel reporter assays. Compared to their normal counterparts, cancer cells induced DMCs linked to neoplasia-enabling processes that operate in normal cells while also engaging entirely new DMCs. These data identify a cis-regulatory combinatorial DNA motif lexicon for cell type-specific gene regulation in diverse normal and diseased human cells. Purpose: To identify a cis-regulatory combinatorial DNA motif lexicon mediating cell type-specific gene regulation in diverse normal and diseased human cells, matched RNA-seq, chromatin accessibility profiling via ATAC-seq, and enhancer-promoter looping via H3K27ac HiChIP was performed in 15 primary human cell types and 6 human cancer cell lines. Processed and raw data for all cell types can be found in this repository. Squamous cell carcinoma cell line raw and processed data can be found at Jung, et al. "XXX". All biological replicates (n=2) are merged.
2022-08-17 | GSE186947 | GEO
Project description:A cis-regulatory lexicon of DNA motif combinations mediating cell type-specific gene regulation
| PRJNA778734 | ENA
Project description:A cis-regulatory lexicon of DNA motif combinations mediating cell type-specific gene regulation [MPRA]
| PRJNA778736 | ENA
Project description:A cis-regulatory lexicon of DNA motif combinations mediating cell type-specific gene regulation [RNA-Seq]
| PRJNA777829 | ENA
Project description:A cis-regulatory lexicon of DNA motif combinations mediating cell type-specific gene regulation [ATAC-Seq]
| PRJNA778732 | ENA
Project description:A cis-regulatory lexicon of DNA motif combinations mediating cell type-specific gene regulation [HiChIP-Seq]
| PRJNA778728 | ENA
Project description:It has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types. If this is the case, it may be possible to observe different combinations of TF motifs - a motif grammar - located at the TF binding sites in different cell types. In this study, we develop a bioinformatics method to systematically identify DNA motifs in TF binding sites across multiple cell types based on published ChIP-seq data, and address two questions: (1) can we build a machine learning classifier to predict cell-type specificity based on motif combinations alone, and (2) can we extract meaningful cell-type specific motif grammars from this classifier model.We present a Random Forest (RF) based approach to build a multi-class classifier to predict the cell-type specificity of a TF binding site given its motif content. We applied this RF classifier to two published ChIP-seq datasets of TF (TCF7L2 and MAX) across multiple cell types. Using cross-validation, we show that motif combinations alone are indeed predictive of cell types. Furthermore, we present a rule mining approach to extract the most discriminatory rules in the RF classifier, thus allowing us to discover the underlying cell-type specific motif grammar.Our bioinformatics analysis supports the hypothesis that combinatorial TF motif patterns are cell-type specific.
| S-EPMC5780765 | biostudies-literature