Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

Huan, Tao; Li, Hao; Xu, Kang; Hong, Huasheng; Jiang, Shuai; Du, Guangwei; Wang, Junting; Sun, Yu; Huang, Xin; Ding, Yang; Li, Fēi; Zheng, Xiaofei; Chen, Hebing; Bo, Xiaochen

doi:10.1093/bib/bbaa405

Cited by 23 publications

(21 citation statements)

References 127 publications

(219 reference statements)

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“…Predicting long range interactions is challenging and computationally extremely expensive (47). Many factors must be taken into account when comparing two models of chromosome architecture, including the the amount and types of input features (e.g.…”

Section: Discussionmentioning

confidence: 99%

Predicting 3D genome architecture directly from the nucleotide sequence with DNA-DDA

Lainscsek

Taher

2022

Preprint

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3D genome architecture is characterized by multi-scale patterns and plays an essential role in proper cellular function. Chromatin conformation capturing experiments have revealed many properties underlying 3D genome architecture. However, they are complex, costly, and time consuming, and therefore only a limited number of cell types have been examined using these techniques. Increasing effort is being directed towards deriving computational methods that can predict chromatin conformation and associated structures. Here we present DNA-DDA, a purely sequence-based method that relies on nonlinear dynamics to predict genome-wide chromatin contacts and A/B compartments. We show that the DNA sequence and A/B compartment labels of a 20Mbp-long region of the human genome is sufficient to predict the 3D architecture of the entire genome. Although this is a proof-of-concept study, our method shows promise in elucidating the mechanisms responsible for genome folding as well as modeling the impact of genetic variation on 3D genome architecture and the processes regulated thereby.

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Section: Discussionmentioning

confidence: 99%

Predicting 3D genome architecture directly from the nucleotide sequence with DNA-DDA

Lainscsek

Taher

2022

Preprint

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“…Robust experimental techniques allow nowadays to obtain various datasets describing chromosome organisation and dynamics quantitatively. The plethora of genome-wide experimental data provides opportunities to infer relationships between 1D (epi)genomic and 3D genome folding properties, as well as the underlying molecular mechanisms, using statistical analysis ( 1 , 2 ) or biophysical modelling ( 3 , 4 ).…”

Section: Introductionmentioning

confidence: 99%

3DGenBench: a web-server to benchmark computational models for 3D Genomics

Belokopytova¹,

Viesná²,

Chiliński³

et al. 2022

Nucleic Acids Research

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Modeling 3D genome organisation has been booming in the last years thanks to the availability of experimental datasets of genomic contacts. However, the field is currently missing the standardisation of methods and metrics to compare predictions and experiments. We present 3DGenBench, a web server available at https://inc-cost.eu/benchmarking/, that allows benchmarking computational models of 3D Genomics. The benchmark is performed using a manually curated dataset of 39 capture Hi-C profiles in wild type and genome-edited mouse cells, and five genome-wide Hi-C profiles in human, mouse, and Drosophila cells. 3DGenBench performs two kinds of analysis, each supplied with a specific scoring module that compares predictions of a computational method to experimental data using several metrics. With 3DGenBench, the user obtains model performance scores, allowing an unbiased comparison with other models. 3DGenBench aims to become a reference web server to test new 3D genomics models and is conceived as an evolving platform where new types of analysis will be implemented in the future.

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“…HiC-Reg ( Zhang et al , 2019 )], boundaries of chromatin domains [e.g. nTDP ( Sefer and Kingsford, 2015 ), Lollipop ( Kai et al , 2018 ), 3DEpiLoop ( Al Bkhetan and Plewczynski, 2018 ), TAD-Lactuca ( Gan et al , 2019 )], with 48 methods recently reviewed by Tao et al (2021) [see also Belokopytova and Fishman 2020 for a broader overview ( Belokopytova and Fishman, 2020 )]. Yet, these methods operate at the resolution of Hi-C data.…”

Section: Introductionmentioning

confidence: 99%

preciseTAD: a transfer learning framework for 3D domain boundary prediction at base-pair resolution

2021

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Motivation Chromosome conformation capture technologies (Hi-C) revealed extensive DNA folding into discrete 3D domains, such as Topologically Associating Domains and chromatin loops. The correct binding of CTCF and cohesin at domain boundaries is integral in maintaining the proper structure and function of these 3D domains. 3D domains have been mapped at the resolutions of 1 kilobase and above. However, it has not been possible to define their boundaries at the resolution of boundary-forming proteins. Results To predict domain boundaries at base-pair resolution, we developed preciseTAD, an optimized transfer learning framework trained on high-resolution genome annotation data. In contrast to current TAD/loop callers, preciseTAD-predicted boundaries are strongly supported by experimental evidence. Importantly, this approach can accurately delineate boundaries in cells without Hi-C data. preciseTAD provides a powerful framework to improve our understanding of how genomic regulators are shaping the 3D structure of the genome at base-pair resolution. Availability preciseTAD is an R/Bioconductor package available at https://bioconductor.org/packages/preciseTAD/ Supplementary information Supplementary data are available at Bioinformatics online.

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Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

Cited by 23 publications

References 127 publications

Predicting 3D genome architecture directly from the nucleotide sequence with DNA-DDA

Predicting 3D genome architecture directly from the nucleotide sequence with DNA-DDA

3DGenBench: a web-server to benchmark computational models for 3D Genomics

preciseTAD: a transfer learning framework for 3D domain boundary prediction at base-pair resolution

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