Efficient double fragmentation ChIP-seq provides nucleotide resolution protein-DNA binding profiles.
Ontology highlight
ABSTRACT:
SUBMITTER: Mokry M
PROVIDER: S-EPMC2994895 | biostudies-literature | 2010
REPOSITORIES: biostudies-literature
Publications
Efficient double fragmentation ChIP-seq provides nucleotide resolution protein-DNA binding profiles.
Mokry Michal M Hatzis Pantelis P de Bruijn Ewart E Koster Jan J Versteeg Rogier R Schuijers Jurian J van de Wetering Marc M Guryev Victor V Clevers Hans H Cuppen Edwin E
PloS one 20101130 11
Immunoprecipitated crosslinked protein-DNA fragments typically range in size from several hundred to several thousand base pairs, with a significant part of chromatin being much longer than the optimal length for next-generation sequencing (NGS) procedures. Because these larger fragments may be non-random and represent relevant biology that may otherwise be missed, but also because they represent a significant fraction of the immunoprecipitated material, we designed a double-fragmentation ChIP-s ...[more]
Similar Datasets
Project description:ChIP-seq has become the method of choice for studying functional DNA-protein interactions on a genome wide scale. The method is based on co-immunoprecipitation of DNA binding proteins with formaldehyde cross-linked DNA, followed by deep sequencing of immunoprecipitated chromatin fragments, allowing for the identification of binding sites with high accuracy [1-5]. Traditionally, genome-wide tiling path microarrays were used for the detection of specifically immunoprecipitated DNA fragments, but current next-generation sequencers now generate sufficient data to assay multiple samples in a single sequencing run, making this method more time and cost effective compared to microarray-based approaches [2]. However, due to limitations in read length of next generation sequencing technologies (50-76bp), it is not possible to sequence the complete length of immunoprecipitated DNA fragments which can be up to 2kb long reflecting biology in case of big protein complexes. As a consequence only the ends of the immunoprecipitated DNA fragments are sequenced. Deconvolution of sequencing reads mapping to the positive and negative strand is required to identify the real DNA binding site [4-9]. This limitation is most obvious in case of large complex regions where multiple binding sites of various regulatory elements are clustered close to each other. Deconvolution of such regions and the exact identification of individual binding positions is very challenging [4]. Similar problems can occur when studying histone positioning in cases where the length of ChIP fragments is bigger than the average distance of 2 neighboring nucleosomes. In addition frequently only narrow size range of immunoprecipitated fragments is selected for sequencing and thus possible bias towards binding regions within the selected range can be expected by loosing larger fragments possible originated from protein complexes interacting with DNA. To circumvent these complications, we modified the procedure for the preparation of ChIP-seq samples by introducing an additional extensive fragmentation round after isolation of immunoprecipitated chromatin to generate 70-110 bp long DNA fragments. For testing, we choose Tcf4 protein which is a well-studied downstream element of the Wnt-pathway for which the genome wide binding site profile is known was already known from ChIP-on-CHIP experiments [10]. Using the modified approach, we were able to identify Tcf4 binding sites at near nucleotide resolution without computational deconvolution. Furthermore, high-resolution information on genome-wide binding site regions allowed for the identification of potential novel Tcf4 co-factors as well as target genes. 5 samples + 3 input samples
2010-11-01 | E-GEOD-18481 | biostudies-arrayexpress
Project description:ChIP-seq has become the method of choice for studying functional DNA-protein interactions on a genome wide scale. The method is based on co-immunoprecipitation of DNA binding proteins with formaldehyde cross-linked DNA, followed by deep sequencing of immunoprecipitated chromatin fragments, allowing for the identification of binding sites with high accuracy [1-5]. Traditionally, genome-wide tiling path microarrays were used for the detection of specifically immunoprecipitated DNA fragments, but current next-generation sequencers now generate sufficient data to assay multiple samples in a single sequencing run, making this method more time and cost effective compared to microarray-based approaches [2]. However, due to limitations in read length of next generation sequencing technologies (50-76bp), it is not possible to sequence the complete length of immunoprecipitated DNA fragments which can be up to 2kb long reflecting biology in case of big protein complexes. As a consequence only the ends of the immunoprecipitated DNA fragments are sequenced. Deconvolution of sequencing reads mapping to the positive and negative strand is required to identify the real DNA binding site [4-9]. This limitation is most obvious in case of large complex regions where multiple binding sites of various regulatory elements are clustered close to each other. Deconvolution of such regions and the exact identification of individual binding positions is very challenging [4]. Similar problems can occur when studying histone positioning in cases where the length of ChIP fragments is bigger than the average distance of 2 neighboring nucleosomes. In addition frequently only narrow size range of immunoprecipitated fragments is selected for sequencing and thus possible bias towards binding regions within the selected range can be expected by loosing larger fragments possible originated from protein complexes interacting with DNA. To circumvent these complications, we modified the procedure for the preparation of ChIP-seq samples by introducing an additional extensive fragmentation round after isolation of immunoprecipitated chromatin to generate 70-110 bp long DNA fragments. For testing, we choose Tcf4 protein which is a well-studied downstream element of the Wnt-pathway for which the genome wide binding site profile is known was already known from ChIP-on-CHIP experiments [10]. Using the modified approach, we were able to identify Tcf4 binding sites at near nucleotide resolution without computational deconvolution. Furthermore, high-resolution information on genome-wide binding site regions allowed for the identification of potential novel Tcf4 co-factors as well as target genes.
2010-11-01 | GSE18481 | GEO
Project description:Hypoxia-inducible factor (HIF) regulates the major transcriptional cascade central to the response of all mammalian cells to alterations in oxygen tension. Expression arrays indicate that many hundreds of genes are regulated by this pathway, controlling diverse processes that in turn orchestrate both oxygen delivery and utilization. However, the extent to which HIF exerts direct versus indirect control over gene expression together with the factors dictating the range of HIF-regulated genes remains unclear. Using chromatin immunoprecipitation linked to high throughput sequencing, we identify HIF-binding sites across the genome, independently of gene architecture. Using gene set enrichment analysis, we demonstrate robust associations with the regulation of gene expression by HIF, indicating that these sites operate over long genomic intervals. Analysis of HIF-binding motifs demonstrates sequence preferences outside of the core RCGTG-binding motif but does not reveal any additional absolute sequence requirements. Across the entire genome, only a small proportion of these potential binding sites are bound by HIF, although occupancy of potential sites was enhanced approximately 20-fold at normoxic DNAse1 hypersensitivity sites (irrespective of distance from promoters), suggesting that epigenetic regulation of chromatin may have an important role in defining the response to hypoxia.
| S-EPMC3374576 | biostudies-literature
Project description:MotivationChromatin immunoprecipitation coupled to next-generation sequencing (ChIP-seq) is widely used to study the in vivo binding sites of transcription factors (TFs) and their regulatory targets. Recent improvements to ChIP-seq, such as increased resolution, promise deeper insights into transcriptional regulation, yet require novel computational tools to fully leverage their advantages.ResultsTo this aim, we have developed peakzilla, which can identify closely spaced TF binding sites at high resolution (i.e. resolves individual binding sites even if spaced closely), as we demonstrate using semisynthetic datasets, performing ChIP-seq for the TF Twist in Drosophila embryos with different experimental fragment sizes, and analyzing ChIP-exo datasets. We show that the increased resolution reached by peakzilla is highly relevant, as closely spaced Twist binding sites are strongly enriched in transcriptional enhancers, suggesting a signature to discriminate functional from abundant non-functional or neutral TF binding. Peakzilla is easy to use, as it estimates all the necessary parameters from the data and is freely available.Availability and implementationThe peakzilla program is available from https://github.com/steinmann/peakzilla or http://www.starklab.org/data/peakzilla/.Contactstark@starklab.org.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3799470 | biostudies-literature
Project description:A key challenge in quantitative ChIP combined with high-throughput sequencing (ChIP-seq) is the normalization of data in the presence of genome-wide changes in occupancy. Analysis-based normalization methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor (TF) binding, these assumptions do not hold true. The challenges in normalization are confounded by experimental variability during sample preparation, processing and recovery. We present a novel normalization strategy utilizing an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalization. We compare our approach to normalization by total read depth and two alternative methods that utilize external experimental controls to study TF binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in patient-derived xenographs. This is supported by an adaptable pipeline to normalize and quantify differential TF binding genome-wide and generate metrics for differential binding at individual sites.
| S-EPMC6093181 | biostudies-literature
Project description:MOTIVATION: ChIP-seq and ChIP-chip experiments have been widely used to identify transcription factor (TF) binding sites and target genes. Conventionally, a fairly 'simple' approach is employed for target gene identification e.g. finding genes with binding sites within 2 kb of a transcription start site (TSS). However, this does not take into account the number of sites upstream of the TSS, their exact positioning or the fact that different TFs appear to act at different characteristic distances from the TSS. RESULTS: Here we propose a probabilistic model called target identification from profiles (TIP) that quantitatively measures the regulatory relationships between TFs and target genes. For each TF, our model builds a characteristic, averaged profile of binding around the TSS and then uses this to weight the sites associated with a given gene, providing a continuous-valued 'regulatory' score relating each TF and potential target. Moreover, the score can readily be turned into a ranked list of target genes and an estimate of significance, which is useful for case-dependent downstream analysis. CONCLUSION: We show the advantages of TIP by comparing it to the 'simple' approach on several representative datasets, using motif occurrence and relationship to knock-out experiments as metrics of validation. Moreover, we show that the probabilistic model is not as sensitive to various experimental parameters (including sequencing depth and peak-calling method) as the simple approach; in fact, the lesser dependence on sequencing depth potentially utilizes the result of a ChIP-seq experiment in a more 'cost-effective' manner. CONTACT: mark.gerstein@yale.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
| S-EPMC3223362 | biostudies-other
Project description:The comprehension of protein and DNA binding in vivo is essential to understand gene regulation. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) provides a global map of the regulatory binding network. Most ChIP-seq analysis tools focus on identifying binding regions from coverage enrichment. However, less work has been performed to infer the physical and regulatory details inside the enriched regions. This research extends a previous blind-deconvolution approach to develop a post-peak-calling algorithm that improves binding site resolution and predicts cooperative interactions. At the core of our new method is a physically motivated model that characterizes the binding signal as an extreme value distribution. This model suggests a mathematical framework to study physical properties of DNA shearing from the ChIP-seq coverage. The model explains the ChIP-seq coverage with two signals: The first considers DNA fragments with only a single binding event, whereas the second considers fragments with two binding events (a double-binding signal). The model incorporates motif discovery and is able to detect multiple sites in an enriched region with single-nucleotide resolution, high sensitivity, and high specificity. Our method improves peak caller sensitivity, from less than 45% up to 94%, at a false positive rate < 11% for a set of 47 experimentally validated prokaryotic sites. It also improves resolution of highly enriched regions of large-scale eukaryotic data sets. The double-binding signal provides a novel application in ChIP-seq analysis: the identification of cooperative interaction. Predictions of known cooperative binding sites show a 0.85 area under an ROC curve.
| S-EPMC4199365 | biostudies-literature
Project description:Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) has been successfully used for genome-wide profiling of transcription factor binding sites, histone modifications, and nucleosome occupancy in many model organisms and humans. Because the compact genomes of prokaryotes harbor many binding sites separated by only few base pairs, applications of ChIP-Seq in this domain have not reached their full potential. Applications in prokaryotic genomes are further hampered by the fact that well studied data analysis methods for ChIP-Seq do not result in a resolution required for deciphering the locations of nearby binding events. We generated single-end tag (SET) and paired-end tag (PET) ChIP-Seq data for ??? factor in Escherichia coli (E. coli). Direct comparison of these datasets revealed that although PET assay enables higher resolution identification of binding events, standard ChIP-Seq analysis methods are not equipped to utilize PET-specific features of the data. To address this problem, we developed dPeak as a high resolution binding site identification (deconvolution) algorithm. dPeak implements a probabilistic model that accurately describes ChIP-Seq data generation process for both the SET and PET assays. For SET data, dPeak outperforms or performs comparably to the state-of-the-art high-resolution ChIP-Seq peak deconvolution algorithms such as PICS, GPS, and GEM. When coupled with PET data, dPeak significantly outperforms SET-based analysis with any of the current state-of-the-art methods. Experimental validations of a subset of dPeak predictions from ??? PET ChIP-Seq data indicate that dPeak can estimate locations of binding events with as high as 2 to 21 bp resolution. Applications of dPeak to ??? ChIP-Seq data in E. coli under aerobic and anaerobic conditions reveal closely located promoters that are differentially occupied and further illustrate the importance of high resolution analysis of ChIP-Seq data.
| S-EPMC3798280 | biostudies-literature
Project description:As chromatin immunoprecipitation (ChIP) sequencing is becoming the dominant technique for studying chromatin modifications, new protocols surface to improve the method. Bioinformatics is also essential to analyze and understand the results, and precise analysis helps us to identify the effects of protocol optimizations. We applied iterative sonication - sending the fragmented DNA after ChIP through additional round(s) of shearing - to a number of samples, testing the effects on different histone marks, aiming to uncover potential benefits of inactive histone marks specifically. We developed an analysis pipeline that utilizes our unique, enrichment-type specific approach to peak calling. With the help of this pipeline, we managed to accurately describe the advantages and disadvantages of the iterative refragmentation technique, and we successfully identified possible fields for its applications, where it enhances the results greatly. In addition to the resonication protocol description, we provide guidelines for peak calling optimization and a freely implementable pipeline for data analysis.
| S-EPMC5081244 | biostudies-literature
Project description:Genome-wide binding data from transcription factor ChIP-seq experiments is the best source of information for inferring the relative DNA-binding affinity of these proteins in vivo. However, standard motif enrichment analysis and motif discovery approaches sometimes fail to correctly identify the binding motif for the ChIP-ed factor. To overcome this problem, we propose 'central motif enrichment analysis' (CMEA), which is based on the observation that the positional distribution of binding sites matching the direct-binding motif tends to be unimodal, well centered and maximal in the precise center of the ChIP-seq peak regions. We describe a novel visualization and statistical analysis tool--CentriMo--that identifies the region of maximum central enrichment in a set of ChIP-seq peak regions and displays the positional distributions of predicted sites. Using CentriMo for motif enrichment analysis, we provide evidence that one transcription factor (Nanog) has different binding affinity in vivo than in vitro, that another binds DNA cooperatively (E2f1), and confirm the in vivo affinity of NFIC, rescuing a difficult ChIP-seq data set. In another data set, CentriMo strongly suggests that there is no evidence of direct DNA binding by the ChIP-ed factor (Smad1). CentriMo is now part of the MEME Suite software package available at http://meme.nbcr.net. All data and output files presented here are available at: http://research.imb.uq.edu.au/t.bailey/sd/Bailey2011a.
| S-EPMC3458523 | biostudies-literature