A Stringent Systems Approach Uncovers Gene-Specific Mechanisms Regulating Inflammation [RNA-Seq]
Transcriptomics Dataset
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General Information
AccessionGSE67355
TitleA Stringent Systems Approach Uncovers Gene-Specific Mechanisms Regulating Inflammation [RNA-Seq]
DescriptionMuch has been learned about transcriptional cascades and networks from large-scale systems analyses of high-throughput data sets. However, analysis methods that optimize statistical power through simultaneous evaluation of thousands of ChIP-seq peaks or differentially expressed genes possess substantial limitations in their ability to uncover mechanistic principles of transcriptional control. By examining nascent transcript RNA-seq, ChIP-seq, and binding motif data sets from lipid A-stimulated macrophages with increased attention to the quantitative distribution of signals, we identified unexpected relationships between the in vivo binding properties of inducible transcription factors, motif strength, and transcription. Furthermore, rather than emphasizing common features of large clusters of co-regulated genes, our results highlight the extent to which unique mechanisms regulate individual genes with key biological functions. Our findings demonstrate the mechanistic value of stringent interrogation of well- defined sets of genes as a complement to broader systems analyses of transcriptional cascades and networks.
Landing Pagehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE67355
RepositoryGene Expression Omnibus
Tools used to analyze the dataset

Dataset Accession

ARCHS4
Published September 2017
ARCHS4 provides access to gene counts from HiSeq 2000 and HiSeq 2500 platforms for human and mouse experiments from GEO ...
4645
Clustergrammer
Published October 2017
Web-based tool for visualizing and analyzing high-dimensional data as interactive and shareable heatmaps
4645
Canned Analyses generated with the dataset

Dataset Accession
Tool Name

Interactive heatmap visualization of RNA-seq dataset GSE67355
Highly interactive web-based heatmap visualization of the top 500 most variable genes in t...
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