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Generative AI approach to encode cell-type-specific chromatin accessibility and gene expression

Award Number: R01HG014326
ORGANIZATION: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/11/2026
PERIOD OF PERFORMANCE END DATE: 06/30/2030

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Generative AI approach to encode cell-type-specific chromatin accessibility and gene expression - PROJECT SUMMARY Understanding and controlling gene regulation is critical for advancing our understanding of the human genome and developing precise therapeutic interventions. Cis-regulatory elements (CREs) are DNA sequences that govern gene expression. However, deciphering how CREs encode spatio-temporal transcriptional control remains a major challenge due to the combinatorial grammar and complex syntactical logic of transcription factor binding and function. The development of accurate models capable of predicting CRE function from sequence alone have begun to broadly transform CRE interpretation. Recent work applying generative algorithms to these CRE models has demonstrated the capability of creating synthetic CREs with desired regulatory properties, including cell-type specificity. This project aims to learn the fundamental rules necessary to efficiently ‘read’ and ‘write’ the complex regulatory logic of CREs and create synthetic elements with bespoke gene expression objectives for research and therapeutic applications. To accomplish this, we propose to combine our team’s diverse expertise in state-of-the-art approaches for machine-learning models of CREs, generative AI, and high-throughput experimental assays to model, predict, and validate synthetic CREs at multiple levels. In Aim 1, using an iterative design framework we will develop, benchmark, and compare complementary approaches predicting CRE function using both episomal- and chromatin-based models, extending our existing work to 10 new diverse cell types. In Aim 2, we will develop multimodal models that integrate chromatin accessibility and gene expression data, including the development of DNA-Composer, a large language model for regulatory sequence design. These models will be validated using PASTE-mediated genomic integration to directly assess endogenous activity at-scale. Finally, in Aim 3 we will generate synthetic CREs addressing key biomedical challenges, including tuning expression levels of endogenous genes, designing minimal promoters for AAV gene therapy, and deploying CREs that mediate β-cell differentiation from stem cells. By systematically investigating all aspects of the CRE design process, including different models, training data, and objectives, we will establish best practices for synthetic CRE development. Finally, the generated data and models will be released quickly along with open-source packages, to promote their use and development by the entire genomics community.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $888,829 )
2026	2026	JACKSON LABORATORY	600 MAIN ST	BAR HARBOR	ME	04609	HANCOCK	USA	Human Genome Research	000	1	9/11/2026	NEW	$888,829
														Subtotal = $888,829

Grand Total All Awards = $888,829

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Generative AI approach to encode cell-type-specific chromatin accessibility and gene expression

Award Number: R01HG014326

ORGANIZATION: NATIONAL HUMAN GENOME RESEARCH INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/11/2026

PERIOD OF PERFORMANCE END DATE: 06/30/2030

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