Renhe Luo

Jan 15, 2025 | nature.com | Rui Yang |Renhe Luo |Martin Rivas |Martín A. Rivas |Darko Barisic |Alireza Karbalayghareh | +11 more

Correction to: Nature Communications https://doi.org/10.1038/s41467-024-53628-0, published online 01 November 2024In this article the affiliation ‘Department of Biochemistry & Molecular Biology; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA’ for Martin A. Rivas was missing. The original article has been corrected.

Nov 1, 2024 | nature.com | Rui Yang |Renhe Luo |Martin Rivas |Martín A. Rivas |Darko Barisic |Alireza Karbalayghareh | +11 more

AbstractIdentifying cell-type-specific 3D chromatin interactions between regulatory elements can help decipher gene regulation and interpret disease-associated non-coding variants. However, achieving this resolution with current 3D genomics technologies is often infeasible given limited input cell numbers. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps, including regulatory interactions, from single-cell ATAC sequencing (scATAC-seq) data alone.