Saccharomyces cerevisiae
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA107001 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:By integrating sequence information from closely related bacteria with a compendium of high-throughput gene expression datasets, a large-scale transcriptional regulatory networks was constructed for Rhodobacter sphaeroides. Predictions from this network were validated in part using genome-wide analysis for 3 transcription factors (PpsR, RSP_0489 and RSP_3341). Genome-wide protein-DNA interaction analysis of 3 transcription factors predicted to be involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341) were used to validate predictions from a large-scale reconstruction of R. sphaeroides transcriptional regulatory network.
2014-07-01 | E-GEOD-58648 | biostudies-arrayexpress
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2017-03-30 | MSV000080810 | MassIVE
Project description:By integrating sequence information from closely related bacteria with a compendium of high-throughput gene expression datasets, a large-scale transcriptional regulatory networks was constructed for Rhodobacter sphaeroides. Predictions from this network were validated in part using genome-wide analysis for 3 transcription factors (PpsR, RSP_0489 and RSP_3341).
2014-07-01 | GSE58648 | GEO
Project description:Multiscale Gene Networks Dissect the Complexity of Breast Cancer
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Project description:Genetic complexity in yeast transcripts
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Project description:A core task to understand the consequences of non-coding single nucleotide polymorphisms (SNP) is to identify their genotype specific binding of transcription factor (TF). Here, we generate a large-scale TF-SNP interaction map for a selection of 116 colorectal cancer (CRC) risk loci and validated TF binding to 10 putatively functional SNPs. Our data further revealed TF binding complexity adjacent to the 116 risk loci, adding an additional layer of understanding to regulatory networks associated with CRC relevant loci.
2017-02-23 | PXD004435 | Pride
Project description:ChIP-on-chip has emerged as a powerful tool to dissect the complex network of regulatory interactions between transcription factors and their targets. However, most ChIP-on-chip analysis methods use conservative approaches aimed to minimize false-positive transcription factor targets. We present a model with improved sensitivity in detecting binding events from ChIP-on-chip data. Its application to human T-cells, followed by extensive biochemical validation, reveals that three transcription factor oncogenes, NOTCH1, MYC, and HES1, bind to several thousands target gene promoters, up to an order of magnitude increase over conventional analysis methods. Gene expression profiling upon NOTCH1 inhibition shows broad-scale functional regulation across the entire range of predicted target genes, establishing a closer link between occupancy and regulation. Finally, the increased sensitivity reveals a combinatorial regulatory program in which MYC co-binds to virtually all NOTCH1-bound promoters. Overall, these results suggest an unappreciated complexity of transcriptional regulatory networks and highlight the fundamental importance of genome-scale analysis to represent transcriptional programs.
2008-12-04 | GSE12868 | GEO
Project description:Building an integrated view of cellular responses to environmental cues remains a fundamental challenge due to the complexity of intracellular networks in mammalian cells. Here we introduce an integrative biochemical and genetic framework to dissect signal transduction events using multiple data types, and in particular, to unify signaling and transcriptional networks. Using the Toll-like receptor (TLR) system as a model cellular response, we generate comprehensive datasets of physical, enzymatic, and functional interactions, and integrate these data to reveal biochemical paths that connect TLR4 signaling to transcription. We define the roles of proximal TLR4 kinases, identify and functionally test two dozen candidate regulators, and demonstrate a role for Ap1ar (encoding the Gadkin protein) and its binding partner Picalm, potentially linking vesicle transport with pro-inflammatory responses. Our study thus demonstrates how deciphering dynamic cellular responses by integrating datasets on various regulatory layers defines key components and higher-order logic underlying signaling-to-transcription pathways.
2017-05-10 | MSV000081071 | MassIVE
Project description:ChIP-on-chip has emerged as a powerful tool to dissect the complex network of regulatory interactions between transcription factors and their targets. However, most ChIP-on-chip analysis methods use conservative approaches aimed to minimize false-positive transcription factor targets. We present a model with improved sensitivity in detecting binding events from ChIP-on-chip data. Its application to human T-cells, followed by extensive biochemical validation, reveals that three transcription factor oncogenes, NOTCH1, MYC, and HES1, bind to several thousands target gene promoters, up to an order of magnitude increase over conventional analysis methods. Gene expression profiling upon NOTCH1 inhibition shows broad-scale functional regulation across the entire range of predicted target genes, establishing a closer link between occupancy and regulation. Finally, the increased sensitivity reveals a combinatorial regulatory program in which MYC co-binds to virtually all NOTCH1-bound promoters. Overall, these results suggest an unappreciated complexity of transcriptional regulatory networks and highlight the fundamental importance of genome-scale analysis to represent transcriptional programs. Experiment Overall Design: T-ALL cell lines harboring activating mutations in NOTCH1 were treated with vehicle only (DMSO) or a highly active gamma-secretase inhibitor (COMPE, 100nM) for 72 hs and processed for gene expression profiling analysis. Gene expression signatures associated with inhibition of NOTCH1 signaling with CompE were correlated with findings on NOTCH1 direct target genes identified by ChIP-on-chip analysis.
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Project description:Application of genome-scale 'omics approaches to dissect subcellular pathways and regulatory networks governing the fast-growing response of Synechococcus sp. PCC 7002 response to variable irradience levels.
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