Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex
Nelson Johansen, Niklas Kempynck, Nathan R. Zemke, Saroja Somasundaram, Seppe De Winter, Marcus Hooper, Deepanjali Dwivedi, Ruchi Lohia, Fabien Wehbe, Bocheng Li, Darina Abaffyová, Ethan J. Armand, Julie De Man, Eren Can Ekşi, Nikolai Hecker, Gert Hulselmans, Vasileios Konstantakos, David Mauduit, John K. Mich, Gabriele Partel, Tanya L. Daigle, Boaz P. Levi, Kai Zhang, Yoshiaki Tanaka, Jesse Gillis, Jonathan T. Ting, Yoav Ben‐Simon, Jeremy A. Miller, Joseph R. Ecker, Bing Ren, Stein Aerts, Ed S. Lein, Bosiljka Tasic, Trygve E. Bakken
Abstract
Identifying cell-type-specific enhancers is critical for developing genetic tools to study the mammalian brain. We organized the "Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics" to evaluate machine learning and feature-based methods for nominating enhancer sequences targeting mouse cortical cell types. Methods were assessed using in vivo data from hundreds of adeno-associated virus (AAV)-packaged, retro-orbitally delivered enhancers. Open chromatin was the strongest predictor of functional enhancers, while sequence models improved prediction of non-functional enhancers and identified cell-type-specific transcription factor codes to inform in silico enhancer design. This challenge establishes a benchmark for enhancer prioritization and highlights computational and molecular features critical for identifying functional cortical enhancers, advancing efforts to map and manipulate gene regulation in the mammalian cortex.