|
| 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.
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| |
|
| 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.
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| |
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| 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 |
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| |
| 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)
|
|
| Relations |
| BioProject |
PRJNA318029 |
| SRA |
SRP073108 |
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