Mapping ONECUT1 TF binding with ChIP-exo
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ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR498074.fastq.gz | Fastqsanger.gz | |||
| ERR498075.fastq.gz | Fastqsanger.gz | |||
| ERR498076.fastq.gz | Fastqsanger.gz |
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Project description:The aim of this project is to locate the precise binding of the ONECUT1 transcription factor. NOTE: This study was updated on 7th May 2014. All samples, experiments, runs and files were replaced. This was due to an incorrect reagent being used in the earlier version.
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Project description:Mapping ultra high resolution of Brachyury:DNA interaction would provide us with valuable new mechanistic insights into complex molecular transactions at Brachyury-bound enhancers. Embryonic stem cells were differentiated into Brachyury-positive mesoendoderm cells. And, ChIP-exo experiment was then performed to identify detailed Brachyury-DNA binding profiles.
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Project description:Fine Resolution Mapping of TF binding and Chromatin Interactions
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Project description:Monitoring the location of transcription factors (TFs) binding to DNA is key to understanding transcriptional regulation. The main tool for mapping TF binding is ChIP-seq and its variants. However, current ChIP-based methods are hampered by at least one of the following limitations: large input requirements, low spatial resolution, and limited compatibility with high-throughput automation. Here, we describe SLIM-ChIP (Short fragment enriched, Low input, Indexed, MNase ChIP), which overcomes these challenges by combining enzymatic fragmentation of chromatin and on-bead indexing of immobilized TF-DNA complexes. We show that SLIM-ChIP reproduces high resolution binding map of yeast Reb1 similarly to the high-resolution TF mapping methods ChIP-exo and ORGANIC. Yet, SLIM-ChIP requires substantially less input material, and is fully compatible with high-throughput procedures. We further demonstrate the robustness and flexibility of SLIM-ChIP by probing Abf1 and Rap1 in yeast and CTCF in mouse embryonic stem cells. Finally, we show that the unique combination of high resolution and preservation of DNA protection patterns by SLIM-ChIP provide an additional layer of information on the chromatin landscape surrounding the bound TF. We used this information to identify a class of Reb1 sites in which the proximal -1 nucleosome tightly interacts with Reb1 and unlike in most Reb1 sites is refractory to remodeling by the RSC complex. Importantly, the interaction of Reb1 with the -1 nucleosome prevents transcription initiation and can serve as a more general mechanism for maintaining unidirectional transcription. Altogether, SLIM-ChIP is an attractive solution for mapping DNA binding proteins in a more informative context regarding their surrounding chromatin occupancy landscape at a single cell level.
2018-03-06 | GSE108948 | GEO
Project description:Mapping ultra-high resolution of Sp1:DNA interaction would provide us with valuable new mechanistic insights into Sp1-mediated gene regulatory network in Huntington Disease cell culture model. STHdh Q7/Q7 cells were directly fixed and used for the ChIP-exo experiment.
2016-07-15 | E-GEOD-84056 | biostudies-arrayexpress
Project description:Genes involved in distinct diabetes types suggest shared disease mechanisms. We show that rare ONECUT1 coding variants cause monogenic recessive diabetes (neonatal or very early-onset, syndromic) in two unrelated patients, and monogenic dominant diabetes (early adult-onset) in heterozygous relatives of these and 13 additional unrelated cases. Patients heterozygous for rare ONECUT1 coding variants define a subgroup of T2D with early-onset diabetes and other features. In addition, common regulatory ONECUT1 variants are associated with multifactorial T2D. Directed differentiation of human pluripotent stem cells to the pancreatic lineage revealed that loss of ONECUT1 impairs pancreatic progenitor formation and a subsequent endocrine program. We uncovered that ONECUT1 activates the pro-endocrine genes NKX6.1 and NKX2.2 through binding to their cis-regulatory elements. Globally, ONECUT1-directed gene transcription occurs in association with major islet transcription factors, at clusters of pancreas- and endocrine-specific enhancers within open chromatin. ONECUT1 regulates a transcriptional and epigenetic machinery critical for proper endocrine pancreatic development, involved in a spectrum of diabetes, monogenic recessive and dominant, and multifactorial.
2021-11-03 | PXD018887 | Pride
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
Project description:TF co-binding to nucleosome arrays
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Project description:We performed ChIP-Seq for 5 different transcription factors (Pol II, JunD, cFos, Max and cMyc) as part of the ENCODE project in order to determine sites of allele-specific binding. This was done in the GM12878 cell line which was genotyped as part of the pilot II phase of the 1000 genomes project. There is a matching RNA-Seq experiemnt performed on the same cell line. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Mapping TF binding sites for 5 different TFs to assess allele-specific binding
2011-07-09 | E-GEOD-30399 | 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.
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