Computational 3D genome modeling
Annaël Brunet, Tharvesh M. Liyakat Ali
Computational methods to infer 3D structural views of the genome in the nuclear space can reveal spatial relationships between genomic regions that are not visible in the underlying data. Physics-based modeling infers genome folding principles based on physical properties of chromatin. Other genome modeling approaches consider interaction probabilities between genomic domains determined from e.g. Hi-C data, to infer a 3D positioning of these domains. Analysis of the models provides insights into 3D genome conformation. Spatial genome investigations can also generate hypotheses testable in a wet-lab. For a review from our lab, see Sekelja et al. 2016 Genome Biol.
We are developing computational methods for 3D structural modeling of the genome at multiple scales, using interaction probabilities and polymer physics approaches.
- Computational methods for 3D and 4D modeling of genome topologies
- Role of genomic organizers such as TADs and LADs on nuclear architecture in time series studies
- TAD cliques shape the 4D genome during differentiation (Paulsen et al 2019 Nat Genet 51, 835-843)
- Chrom3D genome structures in Virtual Reality (see our video on Youtube)
- Chrom3D: a computational platform for 3D genome modeling from HiC and lamin-genome contacts (Paulsen et al 2017 Genome Biol 18:21; Paulsen et al 2018 Nat Protoc 13, 1137-1152)
- ChIP protocol for nuclear lamins (Oldenburg et al 2016 Meth Mol Biol 1411, 315-324)
- Manifold-based optimization for 3D modeling of chromatin from sparse HiC data (Paulsen et al 2015, PLoS Comput Biol 11, e1004396)
- Enriched Domain Detector: a domain calling algorithm (https://github.com/CollasLab/edd) to map LADs and oher broad domains from ChIP-seq data (Lund et al 2014 Nucl Acids Res 42, e92)