Sequence Features Accurately Predict Genome-wide MeCP2 Binding in vivo
Transcriptomics Dataset
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General Information
AccessionGSE71126
TitleSequence Features Accurately Predict Genome-wide MeCP2 Binding in vivo
DescriptionMECP2 is critical for proper brain development and expressed at near-histone levels in neurons, but the mechanism of its genomic localization remains poorly understood. Using high-resolution MeCP2 binding data, we show that genetic features alone can predict binding with 88%% accuracy. Integrating MeCP2 binding and DNA methylation in a probabilistic graphical model, we demonstrate that previously reported methylation preferences may be due to MeCP2s affinity to GC-rich chromatin, a result replicated using published data. Furthermore, MeCP2 co-localized with nucleosomes, and Mecp2 deletion led to nucleosome repositioning. Finally, MeCP2 binding downstream of promoters correlated with increased expression in Mecp2 deficient neurons.
Landing Pagehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71126
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
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Tool Name
Interactive heatmap visualization of RNA-seq dataset GSE71126
ARCHS4, Clustergrammer
Highly interactive web-based heatmap visualization of the top 500 most variable genes in t...
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