ChEC-Seq: a robust method to identify protein-DNA interactions genome-wide
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA552083 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13733837.fastq.gz | Fastqsanger.gz | |||
| SRR13733838.fastq.gz | Fastqsanger.gz | |||
| SRR13733839.fastq.gz | Fastqsanger.gz | |||
| SRR13733840.fastq.gz | Fastqsanger.gz | |||
| SRR9624521.fastq.gz | Fastqsanger.gz |
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Project description:Here we show that the ChEC-Seq technique is able to differentiate the binding specificities of Esa1 and Gcn5 two chromatin-binding factors displaying widespread genome-wide associations. We also show that the ChEC-Seq technique reveals strong binding of the transcription factor Sfp1 at Ribi gene promoters. Furthermore, our data provide the first evidence that a specific DNA motif previously identified by ChEC-Seq (Albert 2019 PMID: 30804227) is in fact an in vivo binding site for Sfp1.
2020-07-01 | GSE133645 | GEO
Project description:Chromatin endogenous cleavage (ChEC) uses fusion of a protein of interest to micrococcal nuclease (MNase) to target calcium-dependent cleavage to specific genomic loci in vivo. Here we report the combination of ChEC with high-throughput sequencing (ChEC-seq) to map budding yeast transcription factor (TF) binding. Temporal analysis of ChEC-seq data reveals two classes of sites for TFs, one displaying rapid cleavage at sites with robust consensus motifs and the second showing slow cleavage at largely unique sites with low-scoring motifs. Sites with high-scoring motifs also display asymmetric cleavage, indicating that ChEC-seq provides information on the directionality of TF-DNA interactions. Strikingly, similar DNA shape patterns are observed regardless of motif strength, indicating that the kinetics of ChEC-seq discriminates DNA recognition through sequence and/or shape. We propose that time-resolved ChEC-seq detects both high-affinity interactions of TFs with consensus motifs and sites preferentially sampled by TFs during diffusion and sliding.
2015-10-22 | GSE67453 | GEO
Project description:We validated ChEC-seq2 with MNase-fusions to transcription factors with well-documented motifs and binding sites in S. cerevisiae.
2023-11-07 | GSE246951 | GEO
Project description:Chromatin endogenous cleavage (ChEC) uses fusion of a protein of interest to micrococcal nuclease (MNase) to target calcium-dependent cleavage to specific genomic loci in vivo. Here we report the combination of ChEC with high-throughput sequencing (ChEC-seq) to determine genomic occupancy of the generalist transcription factor Nsi1 and the catalytic subunit of the SWI/SNF chromatin remodelling complex Snf2 in the opportunistic yeast Candida albicans. Time-based analysis of ChEC-seq data reveals two classes of sites for each transcriptional regulator, one exhibiting rapid cleavage during the first 5 min with robust consensus motifs and the second showing slow cleavage at largely unique sites with low-scoring motifs. The ChEC-seq procedure described here will allow a high-resolution genomic location definition which will enable a better understanding of transcriptional regulatory circuits that govern fungal fitness and drug resistance in these medically important fungi.
2020-05-08 | GSE150063 | GEO
Project description:HiCuT: an efficient and low input method to identify protein-centric chromatin interactions
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Project description:Genome occupancy of RNA Polymerase II and its phosphorylated forms were determined by ChEC-seq2 in S. cerevisiae. We test the dependency of RNAPII interactions on TFIIB, TFIIH Kinase, and the transcription factor GCN4.
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Project description:A rapid and robust method for Mitochondrial DNA profiling
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Project description:4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.
2016-02-27 | GSE77645 | GEO
Project description:4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes. 4C-Seq experiments from Igh and Cd83 bait in activated B cells and Tcrb (Eb) bait in double negative T cells and immature B cells. RNA-Seq and ATAC-Seq experiments in DN and Immature B cells.
2016-02-27 | E-GEOD-77645 | biostudies-arrayexpress
Project description:This series is a complementary data set for the manuscript entitled "Nup-PI: The Nucleopore-Promoter Interactions of Genes in Yeast." (Mol. Cell, 2006). Experimental Background: Genomic interaction sites of nuclear proteins were mapped by the in vivo ChEC technique described in Schmid et al. (2004) Mol. Cell 16, 147-157. Genome-wide probes that are suitable for hybridization of microarrays were prepared. In brief, MboI restriction fragments that were internally cleaved by the MN moiety of the fusion proteins were amplified and labelled. The procedure is explained in detail in the manuscript. "Nup-PI: The Nucleopore-Promoter Interactions of Genes in Yeast." (Schmid et al., Mol. Cell 2006). Experiments: Genome-wide probes were prepared from control, uncleaved chromatin, as well as chromatin cleaved by H2b-MN and Nup2-MN. All samples were from raffinose grown cells induced for 1 hour by galactose in logarithmic growth phase. Keywords: Genome-wide ChEC analysis, Mapping of Nuclear Pore Proteins 3 independent genome-wide probes were prepared for each Sample. That is, the ChEC experiments as well as preparation of genome-wide probes and hybridization of microarrays were carried independently (on different days). RMA values were calculated from the original .CEL files of each array and Average and standard deviation calculated.
2010-06-05 | E-GEOD-3756 | biostudies-arrayexpress