We propose to produce computationally predicted and experimentally improved single-base-pair resolution maps of genome regulatory elements and their higher-level architectures with ENCODE consortium data. To accomplish this goal, we will accomplish four Aims:
Aim 1 will discover genome regulatory elements at single base pair resolution by simultaneously modeling ChIP-seq data, DNase-seq data, and genome sequence to discover where regulators bind to the genome along with explanatory DNA sequence motifs;
Aim 2 will use integrative analysis to learn probabilistic models of enhancer grammars that include symbol spacing models;
Aim 3 will develop active learning methods to precisely design synthetic enhancer sequences to construct Enhancer Grammar Activity Models (EGAMs) that explain the consequences of different forms of enhancer grammar on gene regulation, and will also learn regulatory factors that are associated with unlinked motifs;
Aim 4 will discover regulatory networks that describe how chromatin and gene expression state is established based on regulator activity, and relate human disease associated genomic variation to potential disease mechanisms. The results of our Aims will be validated with both experimental and computational studies.

Public Health Relevance

We will develop and use new methods to understand the language of the genome - the words and sentences of symbols that describe how cells function both in health and disease. Because the language is complicated, we will use new experimental methods to write and test thousands of genomic sentences for function in a dish. Our ultimate goal is to improve human health by understanding how disease related changes in our genome cause things to go wrong.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01HG007037-02
Application #
8546274
Study Section
Special Emphasis Panel (ZHG1-HGR-M (M2))
Program Officer
Pazin, Michael J
Project Start
2012-09-17
Project End
2015-06-30
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
2
Fiscal Year
2013
Total Cost
$609,880
Indirect Cost
$189,106
Name
Massachusetts Institute of Technology
Department
None
Type
Organized Research Units
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
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Arbab, Mandana; Srinivasan, Sharanya; Hashimoto, Tatsunori et al. (2015) Cloning-free CRISPR. Stem Cell Reports 5:908-17
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Sherwood, Richard I; Hashimoto, Tatsunori; O'Donnell, Charles W et al. (2014) Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat Biotechnol 32:171-8

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