Identifying targets of DSX with ChIP-seq, DamID-seq and DamID-chip and transcriptional response to DSX isoform switch
Ontology highlight
ABSTRACT:
ORGANISM(S): Drosophila melanogaster
PROVIDER: GSE49480 | GEO | 2014/10/30
SECONDARY ACCESSION(S): PRJNA214013
REPOSITORIES: GEO
Similar Datasets
Project description:We have used microarrays to identify DSX occupancy signal in adult female fatbody using the DamID protocol. We have performed DamID-chip on adult female fatbody with three biological replicates.
2014-10-30 | E-GEOD-49478 | biostudies-arrayexpress
Project description:We have used microarrays to identify DSX occupancy signal in adult female fatbody using the DamID protocol.
2014-10-30 | GSE49478 | GEO
Project description:We have adapted the DamID protocol for use with high throughput sequencing. We have used DamID to identify the positions within the Drosophila genome where the transcription factor DSX is bound. We sequenced DpnI-digested genomic DNA from fly tissues containing UAS-Dam (control) or UAS-Dam-DsxF or UAS-Dam-DsxM. We have performed DamID-seq on adult male and female fatbody and on ovary. We used two biological replicates for each tissue and sex.
2014-10-30 | E-GEOD-27269 | biostudies-arrayexpress
Project description:We have adapted the DamID protocol for use with high throughput sequencing. We have used DamID to identify the positions within the Drosophila genome where the transcription factor DSX is bound. We sequenced DpnI-digested genomic DNA from fly tissues containing UAS-Dam (control) or UAS-Dam-DsxF or UAS-Dam-DsxM.
2014-10-30 | GSE27269 | GEO
Project description:We have used ChIP-seq on Drosophila S2 cells to characterize genome-wide DSX occupancy.
2014-10-30 | GSE49511 | GEO
Project description:In recent years, DNA adenine methyltransferase identification (DamID) has emerged as a powerful tool to profile protein-DNA interaction on a genome-wide scale. While DamID has been primarily combined with microarray analyses, which limits the spatial resolution and full potential of this technique, our group was the first to combine DamID with sequencing (DamID-Seq) for characterizing the binding loci and properties of a transcription factor (Tox) (sequencing data available at NCBI's Gene Expression Omnibus under the accession number GSE64240). Our approach was based on the combination and optimization of several bioinformatics tools that are here described in detail. Analysis of Tox proximity to transcriptional start sites, profiling on enhancers and binding motif has allowed us to identify this transcription factor as an important new regulator of neural stem cells differentiation and newborn neurons maturation during mouse cortical development. Here we provide a valuable resource to study the role of Tox as a novel key determinant of mammalian somatic stem cells during development of the nervous and lymphatic system, in which this factor is known to be active, and describe a useful pipeline to perform DamID-Seq analyses for any other transcription factor.
| S-EPMC4778673 | biostudies-literature
Project description:Model-based analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modification from ChIP-seq data. MACS consists of four steps: removing redundant reads, adjusting read position, calculating peak enrichment and estimating the empirical false discovery rate (FDR). In this protocol, we provide a detailed demonstration of how to install MACS and how to use it to analyze three common types of ChIP-seq data sets with different characteristics: the sequence-specific transcription factor FoxA1, the histone modification mark H3K4me3 with sharp enrichment and the H3K36me3 mark with broad enrichment. We also explain how to interpret and visualize the results of MACS analyses. The algorithm requires ?3 GB of RAM and 1.5 h of computing time to analyze a ChIP-seq data set containing 30 million reads, an estimate that increases with sequence coverage. MACS is open source and is available from http://liulab.dfci.harvard.edu/MACS/.
| S-EPMC3868217 | biostudies-literature
Project description:ChIP seq is a widely used assay to measure genome-wide protein binding. The decrease in costs associated with sequencing has led to a rise in the number of studies that investigate protein binding across treatment conditions or cell lines. In addition to the identification of binding sites, new studies evaluate the variation in protein binding between conditions. A number of approaches to study differential transcription factor binding have recently been developed. Several of these methods build upon established methods from RNA-seq to quantify differences in read counts. We compare how these new approaches perform on different data sets from the ENCODE project to illustrate the impact of data processing pipelines under different study designs. The performance of normalization methods for differential ChIP-seq depends strongly on the variation in total amount of protein bound between conditions, with total read count outperforming effective library size, or variants thereof, when a large variation in binding was studied. Use of input subtraction to correct for non-specific binding showed a relatively modest impact on the number of differential peaks found and the fold change accuracy to biological validation, however a larger impact might be expected for samples with more extreme copy number variations between them. Still, it did identify a small subset of novel differential regions while excluding some differential peaks in regions with high background signal. These results highlight proper scaling for between-sample data normalization as critical for differential transcription factor binding analysis and suggest bioinformaticians need to know about the variation in level of total protein binding between conditions to select the best analysis method. At the same time, validation using fold-change estimates from qRT-PCR suggests there is still room for further method improvement.
| S-EPMC4413818 | biostudies-literature
Project description:Thousands of non-coding SNPs have been linked to human diseases in the past. The identification of causal alleles within this pool of disease-associated non-coding SNPs is largely impossible due to the inability to accurately quantify the impact of non-coding variation. To overcome this challenge, we developed a computational model that uses ChIP-seq intensity variation in response to non-coding allelic change as a proxy to the quantification of the biological role of non-coding SNPs. We applied this model to HepG2 enhancers and detected 4796 enhancer SNPs capable of disrupting enhancer activity upon allelic change. These SNPs are significantly over-represented in the binding sites of HNF4 and FOXA families of liver transcription factors and liver eQTLs. In addition, these SNPs are strongly associated with liver GWAS traits, including type I diabetes, and are linked to the abnormal levels of HDL and LDL cholesterol. Our model is directly applicable to any enhancer set for mapping causal regulatory SNPs.
| S-EPMC4288203 | biostudies-literature
Project description:MotivationPost-translational modifications to histones have several well known associations with regulation of gene expression. While some modifications appear concentrated narrowly, covering promoters or enhancers, others are dispersed as epigenomic domains. These domains mark contiguous regions sharing an epigenomic property, such as actively transcribed or poised genes, or heterochromatically silenced regions. While high-throughput methods like ChIP-Seq have led to a flood of high-quality data about these epigenomic domains, there remain important analysis problems that are not adequately solved by current analysis tools.ResultsWe present the RSEG method for identifying epigenomic domains from ChIP-Seq data for histone modifications. In contrast with other methods emphasizing the locations of 'peaks' in read density profiles, our method identifies the boundaries of domains. RSEG is also able to incorporate a control sample and find genomic regions with differential histone modifications between two samples.AvailabilityRSEG, including source code and documentation, is freely available at http://smithlab.cmb.usc.edu/histone/rseg/.
| S-EPMC3051331 | biostudies-literature