Seppe De Winter

DeepSCENIC uses transfer learning from sequence-to-function models to infer causal gene regulatory networks from single-cell multiome data.

The paper develops STEAM, an evolution-augmented deep learning model, to predict mammalian enhancer landscapes across species using comparative genomics and single-cell data.

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

The paper extracts cis-regulatory rules from sequence-to-function models in human neural development using new single-cell multiome data and a computational tool called TF-MINDI.

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.

SCENIC+ infers enhancer-driven gene regulatory networks from single-cell multi-omics data, improving TF identification and enabling analysis of regulatory dynamics and perturbations.

The paper profiles cell diversity in the paralarval octopus brain to create a cell type atlas and identifies molecular similarities between octopus and fly glial cells.