Ontology highlight
ABSTRACT:
PROVIDER: PRJNA248387 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1296530.fastq.gz | Fastqsanger.gz | |||
| SRR1296531.fastq.gz | Fastqsanger.gz | |||
| SRR1296532.fastq.gz | Fastqsanger.gz | |||
| SRR1296529.fastq.gz | Fastqsanger.gz |
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Project description:We determined DNA-binding sites of the yeast transcription factor Yfl052w by ChIP-exo. Cells were grown in the YP media containing palatinose. Yfl052w was tagged with HA tag and anti-HA antibody was used for the immunoprecipitation. Examination of Yfl052 trancription factor in HA-tagged and wt cells (as a control)
2014-07-24 | E-GEOD-57900 | biostudies-arrayexpress
Project description:We determined DNA-binding sites of the yeast transcription factor Yfl052w by ChIP-exo. Cells were grown in the YP media containing palatinose. Yfl052w was tagged with HA tag and anti-HA antibody was used for the immunoprecipitation. Examination of Yfl052 trancription factor in HA-tagged and wt cells (as a control)
2014-07-24 | E-GEOD-57901 | biostudies-arrayexpress
Project description:We determined DNA-binding sites of the yeast transcription factor Yfl052w by ChIP-exo. Cells were grown in the YP media containing palatinose. Yfl052w was tagged with HA tag and anti-HA antibody was used for the immunoprecipitation.
2014-07-24 | GSE57901 | GEO
Project description:We determined DNA-binding sites of the yeast transcription factor Yfl052w by ChIP-exo. Cells were grown in the YP media containing palatinose. Yfl052w was tagged with HA tag and anti-HA antibody was used for the immunoprecipitation.
2014-07-24 | GSE57900 | GEO
Project description:ChIP-exo analysis of the DNA-binding sites of the yeast transcription factor Yfl052w sequenced by Illumina
| PRJNA248390 | ENA
Project description:ChIP-exo analysis of the DNA-binding sites of the yeast transcription factor Yfl052w sequenced by SOLiD
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Project description:Chromatin immunoprecipitation (ChIP) and its derivatives are the main techniques used to determine transcription factor binding sites. However, conventional ChIP with sequencing (ChIP-seq) has problems with poor resolution and newer techniques require significant experimental alterations and complex bioinformatics. Here we build upon our high-resolution crosslinking ChIP-seq (X-ChIP-seq) method and compare it to existing methodologies. By using micrococcal nuclease, which has both endo- and exo-nuclease activity to fragment the chromatin and thereby generate precise protein-DNA footprints, high-resolution X-ChIP-seq achieves single base pair resolution of transcription factor binding. A significant advantage of this protocol is the minimal alteration to the conventional ChIP-seq workflow and simple bioinformatic processing. Using High-resolution X-ChIP-seq we determined the genome-wide binding profile of various DNA binding proteins.
2015-06-12 | E-GEOD-67454 | biostudies-arrayexpress
Project description:A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human. Standard analysis of the genes whose regulatory DNA is bound by a TF, assayed by ChIP-chip/seq, and the genes that respond to a perturbation of that TF, shows that these two data sources rarely converge on a common set of direct, functional targets. Even taking the few genes that are both bound and responsive as direct functional targets is not safe -- when there are many non-functional binding sites and many indirect targets, non-functional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce Dual Threshold Optimization, a new method for setting significance thresholds on binding and response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that has been processed by network inference algorithms, which further improves convergence. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. In yeast, measuring the response shortly after inducing TF overexpression and measuring binding locations by using transposon calling cards or ChIP-exo improve the network synergistically.
2020-02-04 | GSE144657 | GEO
Project description:Identfification of MEF2A target genes using ChIP-exo in skeletla muscle and primary cardiomyocytes. Identfification of MEF2A target genes using ChIP-exo and RNA-seq in skeletal muscle and primary cardiomyocytes. MEF2 plays a profound role in the regulation of transcription in cardiac and skeletal muscle lineages. To define the overlapping and unique MEF2A genomic targets, we utilized ChIP-exo analysis of cardiomyocytes and skeletal myoblasts. Of the 2783 and 1648 MEF2A binding peaks in skeletal myoblasts and cardiomyocytes, respectively, 294 common binding sites were identified. Genomic targets were compared to differentially expressed genes in RNA-seq analysis of MEF2A depleted myogenic cells. MEF2A target genes were identified in 48 hr DM C2C12 myoblasts cells and primary cardiomyocytes using ChIP-exo. Binding profiles on MEF2A in each cell type were compared. Cross sectional-analysis between ChIP-exo identified targets and RNA-seq analysis of MEF2A deplted myoblasts was also done.
2014-09-09 | E-GEOD-61204 | biostudies-arrayexpress
Project description:Regulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each genomic recruitment mechanism may be associated with distinct motifs, and may also result in distinct characteristic patterns in high-resolution protein-DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein-DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5’ to 3’ exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo sequencing tag patterns should enable detection of multiple protein-DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type, or rely on the presence of DNA motifs to cluster binding events into subtypes. To systematically detect multiple protein-DNA interaction modes in a single ChIP-exo experiment, we introduce the ChIP-exo mixture model (ChExMix). ChExMix probabilistically models the genomic locations and subtype membership of protein-DNA binding events using both ChIP-exo tag enrichment patterns and DNA sequence information, thus offering a principled and robust approach to characterizing binding subtypes in ChIP-exo data. We demonstrate that ChExMix achieves accurate detection and classification of binding event subtypes using in silico mixed ChIP-exo data. We further demonstrate the unique analysis abilities of ChExMix using a collection of ChIP-exo experiments that profile the binding of key transcription factors in MCF-7 cells. In these data, ChExMix detects cooperative binding interactions between FoxA1, ERalpha, and CTCF, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes.
2018-02-13 | GSE110502 | GEO