
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

39229027
10.1101/2024.08.21.609075
preprint
2
Article
Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex
Johansen Nelson J. http://orcid.org/0000-0002-4436-969X

Kempynck Niklas http://orcid.org/0000-0002-0104-4844

Zemke Nathan R. http://orcid.org/0000-0002-6326-5925

Somasundaram Saroja http://orcid.org/0000-0002-3729-9849

Winter Seppe De http://orcid.org/0000-0001-7907-1247

Hooper Marcus http://orcid.org/0000-0003-1228-5958

Dwivedi Deepanjali http://orcid.org/0000-0001-9992-9167

Lohia Ruchi http://orcid.org/0000-0002-3496-8197

Wehbe Fabien http://orcid.org/0000-0001-9359-1855

Li Bocheng
Abaffyová Darina http://orcid.org/0000-0002-0636-517X

Armand Ethan J. http://orcid.org/0000-0002-4516-6317

Man Julie De http://orcid.org/0009-0003-7208-8961

Eksi Eren Can http://orcid.org/0000-0002-3122-9858

Hecker Nikolai http://orcid.org/0000-0003-1693-4257

Hulselmans Gert http://orcid.org/0000-0003-2205-1899

Konstantakos Vasilis http://orcid.org/0000-0002-0332-7506

Mauduit David http://orcid.org/0000-0002-2045-227X

Mich John K. http://orcid.org/0000-0002-1626-1139

Partel Gabriele http://orcid.org/0000-0002-4482-3119

Daigle Tanya L. http://orcid.org/0000-0001-9700-8452

Levi Boaz P. http://orcid.org/0000-0002-8346-872X

Zhang Kai http://orcid.org/0000-0003-3454-7357

Tanaka Yoshiaki http://orcid.org/0000-0002-2078-9374

Gillis Jesse http://orcid.org/0000-0002-0936-9774

Ting Jonathan T. http://orcid.org/0000-0001-8266-0392

Ben-Simon Yoav http://orcid.org/0000-0002-7075-097X

Miller Jeremy http://orcid.org/0000-0003-4549-588X

Ecker Joseph R. http://orcid.org/0000-0001-5799-5895

Ren Bing http://orcid.org/0000-0002-5435-1127

Aerts Stein http://orcid.org/0000-0002-8006-0315

Lein Ed S. http://orcid.org/0000-0001-9012-6552

Tasic Bosiljka http://orcid.org/0000-0002-6861-4506

Bakken Trygve E. http://orcid.org/0000-0003-3373-7386

20 9 2024
2024.08.21.609075https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.08.21.609075
nihpp-2024.08.21.609075.pdf
Summary

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet 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 assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for the identification of functional enhancers for mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian brain and help us design more efficient genetic tools and potential gene therapies for human neurological diseases.
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