Transcriptomic analysis and ChIP-seq in CCE-Rx cells to identify SOX2 transcription factor target genes
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE74600 | GEO | 2015/11/04
SECONDARY ACCESSION(S): PRJNA300944
REPOSITORIES: GEO
Similar Datasets
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-11-04 | E-GEOD-74600 | biostudies-arrayexpress
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:Gene transcription by RNA polymerase II (Pol II) is under the control of promoters and distal regulatory elements known as enhancers. Enhancers are themselves transcribed by Pol II which correlates with their activity. How enhancer transcription is regulated and coordinated with transcription at target genes has remained unclear. Here, we developed a high-sensitive native elongating transcript sequencing approach, called HiS-NET-seq, to provide an extended high-resolution view on transcription, especially at lowly transcribed regions such as enhancers. HiS-NET-seq uncovers new transcriptional enhancers in human cells. An integrated multi-omics analysis shows that transcription at most enhancers depends on the BET protein BRD4. We provide evidence for a direct BRD4-mediated coupling of Pol II transcription at enhancers and target genes. Specifically, BRD4 links elongation activation at enhancers and genes by maintaining their proximity. These studies reveal that transcription at enhancers and target genes is coordinated at the level of elongation.
2023-07-06 | GSE214590 | GEO
Project description:Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) or microarray hybridization (ChIP-chip) has been widely used to determine the genomic occupation of transcription factors (TFs). We have previously developed a probabilistic method, called TIP (Target Identification from Profiles), to identify TF target genes using ChIP-seq/ChIP-chip data. To achieve high specificity, TIP applies a conservative method to estimate significance of target genes, with the trade-off being a relatively low sensitivity of target gene identification compared to other methods. Additionally, TIP's output does not render binding-peak locations or intensity, information highly useful for visualization and general experimental biological use, while the variability of ChIP-seq/ChIP-chip file formats has made input into TIP more difficult than desired.To improve upon these facets, here we present are fined TIP with key extensions. First, it implements a Gaussian mixture model for p-value estimation, increasing target gene identification sensitivity and more accurately capturing the shape of TF binding profile distributions. Second, it enables the incorporation of TF binding-peak data by identifying their locations in significant target gene promoter regions and quantifies their strengths. Finally, for full ease of implementation we have incorporated it into a web server ( http://syslab3.nchu.edu.tw/iTAR/ ) that enables flexibility of input file format, can be used across multiple species and genome assembly versions, and is freely available for public use. The web server additionally performs GO enrichment analysis for the identified target genes to reveal the potential function of the corresponding TF.The iTAR web server provides a user-friendly interface and supports target gene identification in seven species, ranging from yeast to human. To facilitate investigating the quality of ChIP-seq/ChIP-chip data, the web server generates the chart of the characteristic binding profiles and the density plot of normalized regulatory scores. The iTAR web server is a useful tool in identifying TF target genes from ChIP-seq/ChIP-chip data and discovering biological insights.
| S-EPMC4983039 | biostudies-literature
Project description:Certain transcription factors are proposed to form functional interactions with RNA to facilitate proper regulation of gene expression. Sox2, a transcription factor critical for maintenance of pluripotency and neurogenesis, has been found associated with several lncRNAs, although it is unknown whether these interactions are direct or via other proteins. Here we demonstrate that human Sox2 interacts directly with one of these lncRNAs with high affinity through its HMG DNA-binding domain in vitro. These interactions are primarily with double-stranded RNA in a non-sequence specific fashion, mediated by a similar but not identical interaction surface. We further determined that Sox2 directly binds RNA in mouse embryonic stem cells by UV-cross-linked immunoprecipitation of Sox2 and more than a thousand Sox2-RNA interactions in vivo were identified using fRIP-seq. Together, these data reveal that Sox2 employs a high-affinity/low-specificity paradigm for RNA binding in vitro and in vivo.
| S-EPMC7156710 | biostudies-literature
Project description:Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed in silico to examine their impact on TFBS clustering and predict changes in gene regulation. Results: The glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, was selected to test this approach. SLC25A32 and TANK exhibited the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required at least 1 information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.
| S-EPMC6464064 | biostudies-literature
Project description:In order to identify target genes of the SOX2 transcription factor, we tried to identify SOX2 binding site in murine genetically modified stem cells overexpressing the RAX gene (CCE-Rx cells) by ChIP-seq. SOX2 gene mutations are involved in anophthalmia and our hypothesis was that target genes of SOX2 transcription factor may also be candidate to be involved in ocular development. High throughput sequencing after chromatin immunoprecipitation using either an antibody against our target transcription factor (SOX2) or using control antobodies (IgG). These experiments were performed on only one immunoprecipitation with each antibody.
2015-11-04 | E-GEOD-74599 | biostudies-arrayexpress
Project description:In order to identify target genes of the SOX2 transcription factor, we tried to identify SOX2 binding site in murine genetically modified stem cells overexpressing the RAX gene (CCE-Rx cells) by ChIP-seq. SOX2 gene mutations are involved in anophthalmia and our hypothesis was that target genes of SOX2 transcription factor may also be candidate to be involved in ocular development.
2015-11-04 | GSE74599 | GEO
Project description:Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) has great potential for elucidating transcriptional networks, by measuring genome-wide binding of transcription factors (TFs) at high resolution. Despite the precision of these experiments, identification of genes directly regulated by a TF (target genes) is not trivial. Numerous target gene scoring methods have been used in the past. However, their suitability for the task and their performance remain unclear, because a thorough comparative assessment of these methods is still lacking. Here we present a systematic evaluation of computational methods for defining TF targets based on ChIP-seq data. We validated predictions based on 68 ChIP-seq studies using a wide range of genomic expression data and functional information. We demonstrate that peak-to-gene assignment is the most crucial step for correct target gene prediction and propose a parameter-free method performing most consistently across the evaluation tests.
| S-EPMC3837635 | biostudies-literature
Project description:We consider integrative modeling of multiple gene networks and diverse genomic data, including protein-DNA binding, gene expression and DNA sequence data, to accurately identify the regulatory target genes of a transcription factor (TF). Rather than treating all the genes equally and independently a priori in existing joint modeling approaches, we incorporate the biological prior knowledge that neighboring genes on a gene network tend to be (or not to be) regulated together by a TF. A key contribution of our work is that, to maximize the use of all existing biological knowledge, we allow incorporation of multiple gene networks into joint modeling of genomic data by introducing a mixture model based on the use of multiple Markov random fields (MRFs). Another important contribution of our work is to allow different genomic data to be correlated and to examine the validity and effect of the independence assumption as adopted in existing methods. Due to a fully Bayesian approach, inference about model parameters can be carried out based on MCMC samples. Application to an E. coli data set, together with simulation studies, demonstrates the utility and statistical efficiency gains with the proposed joint model.
| S-EPMC3298193 | biostudies-literature