TY - JOUR
T1 - Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening
AU - Tan, Jimin
AU - Shenker-Tauris, Nina
AU - Rodriguez-Hernaez, Javier
AU - Wang, Eric
AU - Sakellaropoulos, Theodore
AU - Boccalatte, Francesco
AU - Thandapani, Palaniraja
AU - Skok, Jane
AU - Aifantis, Iannis
AU - Fenyö, David
AU - Xia, Bo
AU - Tsirigos, Aristotelis
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/8
Y1 - 2023/8
N2 - Investigating how chromatin organization determines cell-type-specific gene expression remains challenging. Experimental methods for measuring three-dimensional chromatin organization, such as Hi-C, are costly and have technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. We present C.Origami, a multimodal deep neural network that performs de novo prediction of cell-type-specific chromatin organization using DNA sequence and two cell-type-specific genomic features—CTCF binding and chromatin accessibility. C.Origami enables in silico experiments to examine the impact of genetic changes on chromatin interactions. We further developed an in silico genetic screening approach to assess how individual DNA elements may contribute to chromatin organization and to identify putative cell-type-specific trans-acting regulators that collectively determine chromatin architecture. Applying this approach to leukemia cells and normal T cells, we demonstrate that cell-type-specific in silico genetic screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulation circuits in both normal and disease-related biological systems.
AB - Investigating how chromatin organization determines cell-type-specific gene expression remains challenging. Experimental methods for measuring three-dimensional chromatin organization, such as Hi-C, are costly and have technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. We present C.Origami, a multimodal deep neural network that performs de novo prediction of cell-type-specific chromatin organization using DNA sequence and two cell-type-specific genomic features—CTCF binding and chromatin accessibility. C.Origami enables in silico experiments to examine the impact of genetic changes on chromatin interactions. We further developed an in silico genetic screening approach to assess how individual DNA elements may contribute to chromatin organization and to identify putative cell-type-specific trans-acting regulators that collectively determine chromatin architecture. Applying this approach to leukemia cells and normal T cells, we demonstrate that cell-type-specific in silico genetic screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulation circuits in both normal and disease-related biological systems.
UR - http://www.scopus.com/inward/record.url?scp=85145922748&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145922748&partnerID=8YFLogxK
U2 - 10.1038/s41587-022-01612-8
DO - 10.1038/s41587-022-01612-8
M3 - Article
C2 - 36624151
AN - SCOPUS:85145922748
SN - 1087-0156
VL - 41
SP - 1140
EP - 1150
JO - Nature biotechnology
JF - Nature biotechnology
IS - 8
ER -