Fig. 1: Deep learning-based design of tissue-specific synthetic enhancers.
From: Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo
a, Overview of the deep and transfer learning strategy for predicting in vivo enhancer activity. First, a CNN is trained to predict quantitative DNA accessibility (pseudo-bulk scATAC-seq data) from the DNA sequence (sequence-to-accessibility model). Shown is a locus from the held-out test chromosome with observed and predicted values for CNS, with a PCC of 0.72. The first model is used to initialize a second model to classify DNA sequences on the basis of their activities in vivo in the respective tissue (sequence-to-activity model; shown is an enhancer active in CNS). This process is done separately for each tissue. b, Comparison of predicted DNA accessibility from the sequence-to-accessibility model and predicted enhancer activity (probability) from the sequence-to-activity model in the CNS for all sequences tested in vivo using tenfold cross-validation (blue, inactive; red, active). Density plots show the respective distributions. Area under the precision-recall curve (AUPRC) values are shown for both models. c, PPV of enhancer activity predictions at different thresholds. For each threshold (x axis, 0–1), the percentage of active sequences among all positive predictions is shown (y axis). Solid lines indicate percentages calculated based on more than 50 positive sequences, and dashed lines represent less confident estimates based on smaller numbers.
