Yingqian A Zhan

3 days ago | nature.com | Xueqian Zhuang |Alexander Ferrena |David Humphreys |Melissa Blum |Yingqian A Zhan |Yang Zhao | +10 more

Correction to: Nature https://doi.org/10.1038/s41586-024-08285-0 Published online 4 December 2024In the version of the article initially published, the “Transferrin” inset image in Fig. 5b was missing and has now been included in the HTML and PDF versions of the article. About this articleZhuang, X., Wang, Q., Joost, S. et al. Publisher Correction: Ageing limits stemness and tumorigenesis by reprogramming iron homeostasis. Nature (2025).

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.