We propose to develop statistical and computational methods for interpreting data on gene regulation in a variety of cell types from the ENCODE Project. Our work will focus on (i) developing methods for integrating diverse data types in many tissues to infer chromatin architecture;(ii) inferring the sequence determinants and regulatory consequences of differences in chromatin across cell types;and (iii) empirical tests of our predictions using both natural genetic variation and experimental approaches.

Public Health Relevance

The purpose of this project is to develop new statistical and computational tools for analyzing and interpreting data from the ENCODE project. These data relate to the controls of gene regulation in a wide variety of cell types.

National Institute of Health (NIH)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZHG1)
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Gilchrist, Daniel A
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Stanford University
Schools of Medicine
United States
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Sharon, Eilon; Sibener, Leah V; Battle, Alexis et al. (2016) Genetic variation in MHC proteins is associated with T cell receptor expression biases. Nat Genet 48:995-1002
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