Mapping nucleosome and chromatin architectures: A survey of computational methods

Fang, Kun; Wang, Junbai; Liu, Lu; Jin, Victor X.

doi:10.1016/j.csbj.2022.07.037

Cited by 2 publications

(4 citation statements)

References 118 publications

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“…AI models based on machine learning and deep learning show prominent prediction capabilities in the field of 3D genome (Fig. 6) [35][36][37][38][39]. It not only can identify hierarchical 3D chromatin structures, including chromatin compartments, TADs, and loops, but also can improve the Hi-C data with low-resolution.…”

Section: Computational Tools For Identifying Cancer 3d Genomementioning

confidence: 99%

“…The ensemble machine learning model-LoopPredictor can be applied to predict enhancer- 3D chromatin structural alterations in cancer mediated genome-wide interactions which can isolate cell type-specific gene regulatory networks from three different cancer cell lines [38]. Recently, EPIXplorer has been developed to predict long distance E-P interactions which facilitate us understand how genomewide association study (GWAS) variants affect the development of cancer [39].…”

Section: Identification Of Chromatin Loopsmentioning

confidence: 99%

“…Machine learning and deep learning-based computational methods for the identification of 3D chromatin structures provide an excellent opportunity to explore 3D genome changes in cancer cells [35][36][37][38][39]. Moreover, computational tools have been developed to enhance the sequencing depth of Hi-C data which make up the experimental limitations to some extent [40]. AI improves our understanding of hidden patterns in large and complex genomics data sets from cancer patients.…”

Section: Introductionmentioning

confidence: 99%

“…The rapid development on 3D genome research benefits from the application of artificial intelligence (AI) [33,34]. Machine learning and deep learning-based computational methods for the identification of 3D chromatin structures provide an excellent opportunity to explore 3D genome changes in cancer cells [35][36][37][38][39]. Moreover, computational tools have been developed to enhance the sequencing depth of Hi-C data which make up the experimental limitations to some extent [40].…”

Section: Introductionmentioning

confidence: 99%

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3D genomic organization in cancers

Wang,

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et al. 2023

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published as an Online First article. Please note that technical editing may introduce minor changes to the manuscript text and/or graphics which may affect the content, and all legal disclaimers that apply to the journal pertain. In no event shall HEP be held responsible for errors or consequences arising from the use of any information contained in these "Just Accepted" manuscripts. To cite this manuscript please use its Digital Object Identifier (DOI(r)), which is identical for all formats of publication."Quantitative Biology

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Section: Computational Tools For Identifying Cancer 3d Genomementioning

confidence: 99%

Section: Identification Of Chromatin Loopsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

3D genomic organization in cancers

Wang,

Tao,

et al. 2023

Quant. Biol.

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The 3D genome and its impacts on human health and disease

Wang

Luo

et al. 2023

Life Medicine

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Eukaryotic genomes are highly compacted in the cell nucleus. Two loci separated by a long linear distance can be brought into proximity in space through DNA binding proteins and RNAs, which contributes profoundly to the regulation of gene expression. Recent technology advances have enabled the development and application of the chromosome conformation capture (3C) technique and a host of 3C-based methods that enable genome-scale investigations into changes in chromatin high-order structures during diverse physiological processes and diseases. In this review, we introduce 3C-based technologies and discuss how they can be utilized to glean insights into the impacts of three-dimensional (3D) genome organization in normal physiological and disease processes.

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Mapping nucleosome and chromatin architectures: A survey of computational methods

Cited by 2 publications

References 118 publications

3D genomic organization in cancers

3D genomic organization in cancers

The 3D genome and its impacts on human health and disease

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