Eren Can Ekşi

CREsted is a software package for modeling and designing cell type-specific enhancers using deep learning, validated across tissues and species.

The paper introduces HyDrop v2, a microfluidic system for generating large scATAC-seq atlases, and shows its data is comparable to commercial platforms for training sequence-to-function models.

The paper evaluates computational methods for predicting cell type-specific enhancers in the mouse cortex using in vivo validation data and finds that open chromatin and sequence models improve prediction accuracy.

This study uses deep learning to design synthetic enhancers that target specific cell types in fruit flies and humans, revealing the enhancer code as optimal TF activator motifs and absence of repressor motifs.