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Deep learning of immune differentiation

Sara Mostafavi
Paul G. Allen School of Computer Science and Engineering, University of Washington
Deep learning of immune differentiation

The mammalian genome contains several million cis-regulatory elements, whose differential activity marked by open chromatin determines cellular differentiation. While the growing availability of functional genomics assays allows us to systematically identify cis-regulatory elements across varied cell types, how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine cellular differentiation is beyond our grasp. In this talk, I’ll present recent work using machine learning as a tool to derive an understanding of the relationship between regulatory sequence and cellular function in the context of immune cell differentiation. In particular, I’ll present our deep learning approach (AI-TAC) to combining a large and granular compendium of epigenomic data and will describe approaches to robustly interpreting complex, black-box models in order to uncover mechanistic insights into immune gene regulation (Yoshida et al., Cell 2019; Maslova et al., PNAS 2020).  Our work shows that a deep learning approach to genome-wide chromatin accessibility can uncover patterns of immune transcriptional regulators that are directly coded in the DNA sequence, and thus providing a powerful in-silico framework (an in-silico assay of sorts) to mechanistically probe the relationship between regulatory sequence and its function.