Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: DNA-binding specificities of human transcription factors

PROVIDER: PRJEB3289 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	ERR173154.fastq.gz	Fastqsanger.gz
	ERR173155.fastq.gz	Fastqsanger.gz
	ERR173156.fastq.gz	Fastqsanger.gz
	ERR173157.fastq.gz	Fastqsanger.gz
	ERR185024.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 2726

Similar Datasets

Project description:DNA-binding specificities of human transcription factors

| PRJEB3291 | ENA

Comparing DNA binding specificities between orthologous Pho4 transcription factors

Project description:Comparing DNA binding specificities between orthologous Pho4 transcription factors

| PRJNA1244235 | ENA

synthetic construct

Project description:Impact of cytosine methylation on DNA binding specificities of human transcription factors.

| PRJNA371642 | ENA

Project description:Binding specificities of Drosophila melanogaster transcription factors

| PRJEB7373 | ENA

The Compendium of DNA-Binding Specificities of Transcription Factors in a Pathogenic Bacterium

Project description:The Compendium of DNA-Binding Specificities of Transcription Factors in a Pathogenic Bacterium

| PRJNA611637 | ENA

Quantitative modeling of transcription factor binding specificities using DNA shape

Project description:Accurate predictions of the DNA binding specificities of transcription factors (TFs) are necessary for understanding gene regulatory mechanisms. Traditionally, predictive models are built based on nucleotide sequence features. Here, we employed three- dimensional DNA shape information obtained on a high-throughput basis to integrate intuitive DNA structural features into the modeling of TF binding specificities using support vector regression. We performed quantitative predictions of DNA binding specificities, using the DREAM5 dataset for 65 mouse TFs and genomic-context protein binding microarray data for three human basic helix-loop-helix TFs. DNA shape-augmented models compared favorably with sequence-based models for these predictions. Although both k-mer and DNA shape features encoded the interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space compared to k-mer use. Finally, analyzing the weights of DNA shape-augmented models uncovered TF family- specific structural readout mechanisms that were not obvious from the nucleotide sequence.

2014-11-04 | GSE59845 | GEO

Inherent DNA binding specificities of the HIF-1α and HIF-2α transcription factors in chromatin

Project description:Inherent DNA binding specificities of the HIF-1α and HIF-2α transcription factors in chromatin

| PRJNA494827 | ENA

Project description:SMiLe-seq specificities of transcription factors

| PRJNA318578 | ENA

Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights

Project description:Transcription factors (TFs) play a central role in regulating gene expression by interacting with cis regulatory DNA elements associated with their target genes. Recent surveys have examined the DNA binding specificities of most Saccharomyces cerevisiae transcription factors but a comprehensive evaluation of their data has been lacking. Results: We analyzed in vitro and in vivo TF-DNA binding data reported in previous large-scale studies to generate a comprehensive, curated resource of DNA binding specificity data for all characterized S. cerevisiae transcription factors. Our collection comprises DNA binding site motifs and comprehensive in vitro DNA binding specificity data for all possible 8 bp sequences. Included in this database is DNA binding specificity data for 27 TFs independently generated by PBM analysis in this current study. Investigation of the DNA binding specificities within the basic leucine zipper (bZIP) and VHR transcription factor families revealed unexpected plasticity in TF-DNA recognition: intriguingly, the VHR transcription factors, newly characterized by protein binding microarrays in this study, recognize bZIP like DNA motifs, while the bZIP transcription factor Hac1 recognizes a motif highly similar to the canonical E-box motif of basic helix-loop-helix (bHLH) transcription factors. We identified several transcription factors with distinct primary and secondary motifs, which might be associated with different regulatory functions. Finally, integrated analysis of in vivo transcription factor binding data with protein binding microarray data lends further support for indirect DNA binding in vivo by sequence-specific transcription factors.

2011-12-10 | GSE34306 | GEO

Homo sapiens

Project description:Quantitative modeling of transcription factor binding specificities using DNA shape

| PRJNA256346 | ENA