Inferring CTCF-binding patterns and anchored loops across human tissues and cell types

Hang Xu; Xianfu Yi; Xutong Fan; Chengyue Wu; Wei Wang; Xinlei Chu; Shijie Zhang; Xiaobao Dong; Zhao Wang; Jianhua Wang; Yao Zhou; Ke Zhao; Hongcheng Yao; Nan Zheng; Junwen Wang; Yupeng Chen; Dariusz Plewczynski; Pak Chung Sham; Kexin Chen; Dandan Huang; Mulin Jun Li

doi:10.1016/j.patter.2023.100798

Inferring CTCF-binding patterns and anchored loops across human tissues and cell types

Hang Xu, Xianfu Yi, Xutong Fan, Chengyue Wu, Wei Wang, Xinlei Chu, Shijie Zhang, Xiaobao Dong, Zhao Wang, Jianhua Wang, Yao Zhou, Ke Zhao, Hongcheng Yao, Nan Zheng, Junwen Wang, Yupeng Chen, Dariusz Plewczynski, Pak Chung Sham, Kexin Chen, Dandan HuangMulin Jun Li

Research

Research output: Contribution to journal › Article › peer-review

Abstract

CCCTC-binding factor (CTCF) is a transcription regulator with a complex role in gene regulation. The recognition and effects of CTCF on DNA sequences, chromosome barriers, and enhancer blocking are not well understood. Existing computational tools struggle to assess the regulatory potential of CTCF-binding sites and their impact on chromatin loop formation. Here we have developed a deep-learning model, DeepAnchor, to accurately characterize CTCF binding using high-resolution genomic/epigenomic features. This has revealed distinct chromatin and sequence patterns for CTCF-mediated insulation and looping. An optimized implementation of a previous loop model based on DeepAnchor score excels in predicting CTCF-anchored loops. We have established a compendium of CTCF-anchored loops across 52 human tissue/cell types, and this suggests that genomic disruption of these loops could be a general mechanism of disease pathogenesis. These computational models and resources can help investigate how CTCF-mediated cis-regulatory elements shape context-specific gene regulation in cell development and disease progression.

Original language	English (US)
Article number	100798
Journal	Patterns
Volume	4
Issue number	8
DOIs	https://doi.org/10.1016/j.patter.2023.100798
State	Published - Aug 11 2023

Keywords

3D genome
CTCF
CTCF-mediated loop
DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
cis-regulatory element
deep neural networks

ASJC Scopus subject areas

General Decision Sciences

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10.1016/j.patter.2023.100798

Cite this

Xu, H., Yi, X., Fan, X., Wu, C., Wang, W., Chu, X., Zhang, S., Dong, X., Wang, Z., Wang, J., Zhou, Y., Zhao, K., Yao, H., Zheng, N., Wang, J., Chen, Y., Plewczynski, D., Sham, P. C., Chen, K., ... Li, M. J. (2023). Inferring CTCF-binding patterns and anchored loops across human tissues and cell types. Patterns, 4(8), Article 100798. https://doi.org/10.1016/j.patter.2023.100798

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abstract = "CCCTC-binding factor (CTCF) is a transcription regulator with a complex role in gene regulation. The recognition and effects of CTCF on DNA sequences, chromosome barriers, and enhancer blocking are not well understood. Existing computational tools struggle to assess the regulatory potential of CTCF-binding sites and their impact on chromatin loop formation. Here we have developed a deep-learning model, DeepAnchor, to accurately characterize CTCF binding using high-resolution genomic/epigenomic features. This has revealed distinct chromatin and sequence patterns for CTCF-mediated insulation and looping. An optimized implementation of a previous loop model based on DeepAnchor score excels in predicting CTCF-anchored loops. We have established a compendium of CTCF-anchored loops across 52 human tissue/cell types, and this suggests that genomic disruption of these loops could be a general mechanism of disease pathogenesis. These computational models and resources can help investigate how CTCF-mediated cis-regulatory elements shape context-specific gene regulation in cell development and disease progression.",

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author = "Hang Xu and Xianfu Yi and Xutong Fan and Chengyue Wu and Wei Wang and Xinlei Chu and Shijie Zhang and Xiaobao Dong and Zhao Wang and Jianhua Wang and Yao Zhou and Ke Zhao and Hongcheng Yao and Nan Zheng and Junwen Wang and Yupeng Chen and Dariusz Plewczynski and Sham, {Pak Chung} and Kexin Chen and Dandan Huang and Li, {Mulin Jun}",

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year = "2023",

month = aug,

day = "11",

doi = "10.1016/j.patter.2023.100798",

language = "English (US)",

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AU - Xu, Hang

AU - Yi, Xianfu

AU - Fan, Xutong

AU - Wu, Chengyue

AU - Wang, Wei

AU - Chu, Xinlei

AU - Zhang, Shijie

AU - Dong, Xiaobao

AU - Wang, Zhao

AU - Wang, Jianhua

AU - Zhou, Yao

AU - Zhao, Ke

AU - Yao, Hongcheng

AU - Zheng, Nan

AU - Wang, Junwen

AU - Chen, Yupeng

AU - Plewczynski, Dariusz

AU - Sham, Pak Chung

AU - Chen, Kexin

AU - Huang, Dandan

AU - Li, Mulin Jun

PY - 2023/8/11

Y1 - 2023/8/11

N2 - CCCTC-binding factor (CTCF) is a transcription regulator with a complex role in gene regulation. The recognition and effects of CTCF on DNA sequences, chromosome barriers, and enhancer blocking are not well understood. Existing computational tools struggle to assess the regulatory potential of CTCF-binding sites and their impact on chromatin loop formation. Here we have developed a deep-learning model, DeepAnchor, to accurately characterize CTCF binding using high-resolution genomic/epigenomic features. This has revealed distinct chromatin and sequence patterns for CTCF-mediated insulation and looping. An optimized implementation of a previous loop model based on DeepAnchor score excels in predicting CTCF-anchored loops. We have established a compendium of CTCF-anchored loops across 52 human tissue/cell types, and this suggests that genomic disruption of these loops could be a general mechanism of disease pathogenesis. These computational models and resources can help investigate how CTCF-mediated cis-regulatory elements shape context-specific gene regulation in cell development and disease progression.

AB - CCCTC-binding factor (CTCF) is a transcription regulator with a complex role in gene regulation. The recognition and effects of CTCF on DNA sequences, chromosome barriers, and enhancer blocking are not well understood. Existing computational tools struggle to assess the regulatory potential of CTCF-binding sites and their impact on chromatin loop formation. Here we have developed a deep-learning model, DeepAnchor, to accurately characterize CTCF binding using high-resolution genomic/epigenomic features. This has revealed distinct chromatin and sequence patterns for CTCF-mediated insulation and looping. An optimized implementation of a previous loop model based on DeepAnchor score excels in predicting CTCF-anchored loops. We have established a compendium of CTCF-anchored loops across 52 human tissue/cell types, and this suggests that genomic disruption of these loops could be a general mechanism of disease pathogenesis. These computational models and resources can help investigate how CTCF-mediated cis-regulatory elements shape context-specific gene regulation in cell development and disease progression.

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KW - deep neural networks

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Inferring CTCF-binding patterns and anchored loops across human tissues and cell types

Abstract

Keywords

ASJC Scopus subject areas

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