ChIP-exo analysis of the DNA-binding sites of the yeast transcription factor Yfl052w
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ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE57902 | GEO | 2014/07/22
SECONDARY ACCESSION(S): PRJNA248387
REPOSITORIES: GEO
Similar Datasets
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2014-07-22 | E-GEOD-57902 | 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: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:BACKGROUND: Context-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context-the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding? RESULTS: We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity. CONCLUSIONS: Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data-ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure-we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.
| S-EPMC3574057 | biostudies-literature
Project description:BackgroundChromatin immunoprecipitation combined with the next-generation DNA sequencing technologies (ChIP-seq) becomes a key approach for detecting genome-wide sets of genomic sites bound by proteins, such as transcription factors (TFs). Several methods and open-source tools have been developed to analyze ChIP-seq data. However, most of them are designed for detecting TF binding regions instead of accurately locating transcription factor binding sites (TFBSs). It is still challenging to pinpoint TFBSs directly from ChIP-seq data, especially in regions with closely spaced binding events.ResultsWith the aim to pinpoint TFBSs at a high resolution, we propose a novel method named SeqSite, implementing a two-step strategy: detecting tag-enriched regions first and pinpointing binding sites in the detected regions. The second step is done by modeling the tag density profile, locating TFBSs on each strand with a least-squares model fitting strategy, and merging the detections from the two strands. Experiments on simulation data show that SeqSite can locate most of the binding sites more than 40-bp from each other. Applications on three human TF ChIP-seq datasets demonstrate the advantage of SeqSite for its higher resolution in pinpointing binding sites compared with existing methods.ConclusionsWe have developed a computational tool named SeqSite, which can pinpoint both closely spaced and isolated binding sites, and consequently improves the resolution of TFBS detection from ChIP-seq data.
| S-EPMC3287483 | biostudies-literature
Project description:We developed a computational procedure for optimizing the binding site detections in a given ChIP-seq experiment by maximizing their reproducibility under bootstrap sampling. We demonstrate how the procedure can improve the detection accuracies beyond those obtained with the default settings of popular peak calling software, or inform the user whether the peak detection results are compromised, circumventing the need for arbitrary re-iterative peak calling under varying parameter settings. The generic, open-source implementation is easily extendable to accommodate additional features and to promote its widespread application in future ChIP-seq studies. The peakROTS R-package and user guide are freely available at http://www.nic.funet.fi/pub/sci/molbio/peakROTS.
| S-EPMC3245948 | biostudies-literature
Project description:In the development of the Drosophila embryo, gene expression is directed by the sequence-specific interactions of a large network of protein transcription factors (TFs) and DNA cis-regulatory binding sites. Once the identity of the typically 8-10bp binding sites for any given TF has been determined by one of several experimental procedures, the sequences can be represented in a position weight matrix (PWM) and used to predict the location of additional TF binding sites elsewhere in the genome. Often, alignments of large (>200bp) genomic fragments that have been experimentally determined to bind the TF of interest in Chromatin Immunoprecipitation (ChIP) studies are trimmed under the assumption that the majority of the binding sites are located near the center of all the aligned fragments. In this study, ChIP/chip datasets are analyzed using the corresponding PWMs for the well-studied TFs; CAUDAL, HUNCHBACK, KNIRPS and KRUPPEL, to determine the distribution of predicted binding sites. All four TFs are critical regulators of gene expression along the anterio-posterior axis in early Drosophila development. For all four TFs, the ChIP peaks contain multiple binding sites that are broadly distributed across the genomic region represented by the peak, regardless of the prediction stringency criteria used. This result suggests that ChIP peak trimming may exclude functional binding sites from subsequent analyses.
| S-EPMC5441388 | biostudies-literature
Project description:MotivationRegulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each 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' → 3' exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo tag enrichment 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 motifs to cluster binding events into subtypes.ResultsTo 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 memberships of binding events using both ChIP-exo tag distribution patterns and DNA motifs. 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 identifies possible recruitment mechanisms of FoxA1 and ERα, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes.Availability and implementationChExMix is available from https://github.com/seqcode/chexmix.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6419906 | biostudies-literature