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.
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.
Project description:Gene Expression Profiling Using Huntington Disease Cell Culture Model and High-resolution Sp1 DNA-binding Site Mapping by ChIP-exo in STHdh Q7/Q7 cells
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.
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.
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.
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.
Project description:ChIP-exo/nexus experiments present modifications on the commonly used ChIP-seq protocol for high resolution mapping of transcription factor binding sites. Although many aspects of the ChIP-exo data analysis are similar to those of ChIP-seq, these high throughput experiments pose a number of unique quality control and analysis challenges. We develop a statistical quality control pipeline and accompanying R package, ChIPexoQual, to enable exploration and analysis of ChIP-exo and related experiments. ChIPexoQual evaluates a number of key issues including strand imbalance, library complexity, and signal enrichment of data. Assessment of these features are facilitated through diagnostic plots and summary statistics calculated over regions of the genome with varying levels of coverage. We evaluated our QC pipeline with both large collections of public ChIP-exo/nexus data and multiple, new ChIP-exo datasets from E. coli. ChIPexoQual analysis of these datasets resulted in guidelines for using these QC metrics across a wide range of sequencing depths and provided further insights for modelling ChIP-exo data. Finally, although ChIP-exo experiments have been compared to ChIP-seq experiments with single-end (SE) sequencing, we provide, for the first time, comparisons with paired-end (PE) ChIP-seq experiments. We illustrate that, at fixed sequencing depths, ChIP-exo provides higher sensitivity, specificity, and spatial resolution than PE ChIP-seq and both significantly outperform their SE ChIP-seq counterpart.