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Series GSE80105 Query DataSets for GSE80105
Status Public on Jul 20, 2016
Title Synergistic model of chromatin predicts Dnase-I accessibility
Organisms Homo sapiens; Mus musculus
Experiment type Genome binding/occupancy profiling by high throughput sequencing
Methylation profiling by high throughput sequencing
Summary Enhancers and promoters commonly occur in accessible chromatin characterized by depleted nucleosome contact; however, it is unclear how chromatin accessibility is governed. We show that a logic of cis-acting DNA sequence features can predict the majority of chromatin accessibility at high spatial resolution. We develop a new type of high-dimensional machine learning model, the Cooperative Chromatin Model (CCM), that is capable of predicting a large fraction of genome-widepromoters chromatin accessibility at basepair-resolution in a range of human and mouse cell types from DNA sequence alone. We confirm that a CCM accurately predicts chromatin accessibility, even of a vast array of synthetic DNA sequences, with a novel CrispR-based method of highly efficient site-specific DNA library integration. CCMs are directly interpretable and reveal that a logic based on local, non-specific cooperation, largely among pioneer TFs, is sufficient to predict a large fraction of cellular chromatin accessibility in a wide variety of cell types.
Overall design Dnase-seq on human and mouse cells as well as massively parallel report assay (MPRA) validation using CRISPR editing of native genomic loci.
Contributor(s) Hashimoto TB, Sherwood RI
Citation(s) 27456004
NIH grant(s)
Grant ID Grant title Affiliation Name
P01 NS055923 Transcriptional Regulation of Stem Cell Differentiation into Motor Neurons: Computational models of motor neuron differentiation: Genome wide analysis of factors in motor neuron differentiation: Transcriptional determinants of motor neuron identity MASSACHUSETTS INSTITUTE OF TECHNOLOGY Gifford
U01 HG007037 Integrated Genome Discovery at Single Base Pair Resolution MASSACHUSETTS INSTITUTE OF TECHNOLOGY Gifford
K01 DK101684 Predictive transcription factor modeling to program endodermal cell fates BRIGHAM AND WOMEN'S HOSPITAL Sherwood
Submission date Apr 10, 2016
Last update date May 15, 2019
Contact name David Gifford
Organization name MIT
Street address 32 Vassar St
City Cambridge
State/province MA
ZIP/Postal code 02139
Country USA
Platforms (3)
GPL11154 Illumina HiSeq 2000 (Homo sapiens)
GPL13112 Illumina HiSeq 2000 (Mus musculus)
GPL16417 Illumina MiSeq (Mus musculus)
Samples (8)
GSM2112885 Nrf ChIP-seq (nodox)
GSM2112886 Nrf ChIP-seq (dox)
GSM2112887 Nrf ChIP-seq (sr3)
BioProject PRJNA318029
SRA SRP073108

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SOFT formatted family file(s) SOFTHelp
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Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE80105_RAW.tar 39.7 Gb (http)(custom) TAR (of TAR, TSV)
GSE80105_nrf_GEM_events_mm10.txt.gz 323.4 Kb (ftp)(http) TXT
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Processed data provided as supplementary file
Processed data are available on Series record
Raw data are available in SRA

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