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bioRxiv · 10.1101/2019.12.21.885814

Learning immune cell differentiation

Maslova, A.·Ramirez, R.·Ma, K.·Schmutz, H.·Wang, C.·Fox, C.·Ng, B.·Benoist, C.·Mostafavi, S.
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Abstract

The mammalian genome contains several million cis-regulatory elements, whose differential activity marked by open chromatin determines organogenesis and differentiation. This activity is itself embedded in the DNA sequence, decoded by sequence-specific transcription factors. Leveraging a granular ATAC-seq atlas of chromatin activity across 81 immune cell-types we show that a convolutional neural network ("AI-TAC") can learn to infer cell-type-specific chromatin activity solely from the DNA sequence. AI-TAC does so by rediscovering, with astonishing precision, binding motifs for known regulators, and some unknown ones, mapping them with high concordance to positions validated by ChIP-seq data. AI-TAC also uncovers combinatorial influences, establishing a hierarchy of transcription factors (TFs) and their interactions involved in immunocyte specification, with intriguingly different strategies between lineages. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs. Thus, Deep Learning can reveal the regulatory syntax that drives the full differentiative complexity of the immune system.

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Maslova, A., Ramirez, R., Ma, K., Schmutz, H., Wang, C., Fox, C., Ng, B., Benoist, C., Mostafavi, S.. 2019-12-23. Learning immune cell differentiation. https://doi.org/10.1101/2019.12.21.885814

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