An identification method of chromatin topological associated domains based on spatial density clustering_Journal of Biomedical Engineering

Authors：

 GONG Haiyan ^1,3 , ZHANG Sichen ² , ZHANG Xiaotong ^2,3

1. Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China;
2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China;
3. Shunde Innovation School, University of Science and Technology Beijing, Foshan, Guangdong 528399, P. R. China;

Corresponding author：

GONG Haiyan, Email: ghaiyan@ustb.edu.cn

Keywords：

High-throughput chromatin conformation capture; Topologically associated domain; Chromatin structure; Density-based clustering; Bioinformatics analysis

DOI：

10.7507/1001-5515.202311059

Video：

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Abstract Full text Figures/Tables Video References Cited by

The rapid development of high-throughput chromatin conformation capture (Hi-C) technology provides rich genomic interaction data between chromosomal loci for chromatin structure analysis. However, existing methods for identifying topologically associated domains (TADs) based on Hi-C data suffer from low accuracy and sensitivity to parameters. In this context, a TAD identification method based on spatial density clustering was designed and implemented in this paper. The method preprocessed the raw Hi-C data to obtain normalized Hi-C contact matrix data. Then, it computed the distance matrix between loci, generated a reachability graph based on the core distance and reachability distance of loci, and extracted clustering clusters. Finally, it extracted TAD boundaries based on clustering results. This method could identify TAD structures with higher coherence, and TAD boundaries were enriched with more ChIP-seq factors. Experimental results demonstrate that our method has advantages such as higher accuracy and practical significance in TAD identification.

Citation： GONG Haiyan, ZHANG Sichen, ZHANG Xiaotong. An identification method of chromatin topological associated domains based on spatial density clustering. Journal of Biomedical Engineering, 2024, 41(3): 552-559. doi: 10.7507/1001-5515.202311059 Copy

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2.	Lieberman-Aiden E, Van Berkum N L, Williams L, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 2009, 326(5950): 289-293.
3.	Rocha P P, Raviram R, Bonneau R, et al. Breaking TADs: insights into hierarchical genome organization. Epigenomics-UK, 2015, 7(4): 523-526.
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5.	Filippova D, Patro R, Duggal G, et al. Identification of alternative topological domains in chromatin. Algorithm Mol Bio, 2014, 9(1): 14.
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7.	Zhan Y, Mariani L, Barozzi I, et al. Reciprocal insulation analysis of Hi-C data shows that TADs represent a functionally but not structurally privileged scale in the hierarchical folding of chromosomes. Genome Res, 2017, 27(3): 479-490.
8.	Yu W, He B, Tan K. Identifying topologically associating domains and subdomains by Gaussian Mixture model and Proportion test. Nat Commun, 2017, 8(1): 535.
9.	Wang X T, Cui W, Peng C. HiTAD: detecting the structural and functional hierarchies of topologically associating domains from chromatin interactions. Nucleic Acids Res, 2017, 45(19): e163.
10.	Levy-Leduc C, Delattre M, Mary-Huard T, et al. Two-dimensional segmentation for analyzing Hi-C data. Bioinformatics, 2014, 30(17): i386-i392.
11.	Weinreb C, Raphael B J. Identification of hierarchical chromatin domains. Bioinformatics, 2016, 32(11): 1601-1609.
12.	Ron G, Globerson Y, Moran D, et al. Promoter-enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains. Nat Commun, 2017, 8(1): 2237.
13.	Serra F, Baù D, Goodstadt M, et al. Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors. Plos Comput Biol, 2017, 13(7): e1005665.
14.	Xing H, Wu Y, Zhang M Q, et al. Deciphering hierarchical organization of topologically associated domains through change-point testing. BMC Bioinformatics, 2021, 22(1): 183.
15.	Oluwadare O, Cheng J. ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data. BMC Bioinformatics, 2017, 18(1): 480.
16.	Haddad N, Vaillant C, Jost D. IC-Finder: inferring robustly the hierarchical organization of chromatin folding. Nucleic Acids Res, 2017, 45(10): e81.
17.	Gong H, Yang Y, Zhang X, et al. CASPIAN: A method to identify chromatin topological associated domains based on spatial density cluster. Comput Struct Biotec, 2022, 20: 4816-4824.
18.	Gong H, Zhang D, Zhang X. TOAST: A novel method for identifying topologically associated domains based on graph auto-encoders and clustering. Comput Struct Biotec, 2023, 21: 4759-4768.
19.	Yan K K, Lou S, Gerstein M. MrTADFinder: A network modularity based approach to identify topologically associating domains in multiple resolutions. PLoS Comput Biol, 2017, 13(7): e1005647.
20.	Norton H K, Emerson D J, Huang H, et al. Detecting hierarchical genome folding with network modularity. Nat Methods, 2018, 15(2): 119-122.
21.	Imakaev M, Fudenberg G, Mccord R P, et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat Methods, 2012, 9(10): 999-1003.
22.	Knight P A, Ruiz D. A fast algorithm for matrix balancing. IMA J Numer Anal, 2012, 33(3): 1029-1047.
23.	Ankerst M, Breunig M M, Kriegel H-P, et al. OPTICS: ordering points to identify the clustering structure. SIGMOD Rec, 1999, 28(2): 49-60.
24.	Bentley J L. Multidimensional binary search trees used for associative searching. Commun Acm, 1975, 18(9): 509-517.
25.	Wang Y, Li Y, Gao J, et al. A novel method to identify topological domains using Hi-C data. Quant Biol, 2015, 3(2): 81-89.
26.	Trussart M, Serra F, Baù D, et al. Assessing the limits of restraint-based 3D modeling of genomes and genomic domains. Nucleic Acids Res, 2015, 43(7): 3465-3477.
27.	Durand N C, Robinson J T, Shamim M S, et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst, 2016, 3(1): 99-101.
28.	Kim S, Yu N-K, Kaang B-K. CTCF as a multifunctional protein in genome regulation and gene expression. Exp Mol Med, 2015, 47(6): e166-e166.
29.	Zuin J, Dixon J R, Van Der Reijden M I J A, et al. Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells. Proc Nat Acad Sci, 2014, 111(3): 996-1001.
30.	Crane E, Bian Q, McCord R P, et al. Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature, 2015, 523(7559): 240-244.
31.	Roayaei Ardakany A, Lonardi S. Efficient and accurate detection of topologically associating domains from contact maps// 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017, 88: 22: 1-22: 11.

1. Razin S V, Ulianov S V. Gene functioning and storage within a folded genome. Cell Mol Biol Lett, 2017, 22(1): 18.
2. Lieberman-Aiden E, Van Berkum N L, Williams L, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 2009, 326(5950): 289-293.
3. Rocha P P, Raviram R, Bonneau R, et al. Breaking TADs: insights into hierarchical genome organization. Epigenomics-UK, 2015, 7(4): 523-526.
4. Dixon J R, Selvaraj S, Yue F, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 2012, 485(7398): 376-80.
5. Filippova D, Patro R, Duggal G, et al. Identification of alternative topological domains in chromatin. Algorithm Mol Bio, 2014, 9(1): 14.
6. Shin H, Shi Y, Dai C, et al. TopDom: an efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Res, 2016, 44(7): e70.
7. Zhan Y, Mariani L, Barozzi I, et al. Reciprocal insulation analysis of Hi-C data shows that TADs represent a functionally but not structurally privileged scale in the hierarchical folding of chromosomes. Genome Res, 2017, 27(3): 479-490.
8. Yu W, He B, Tan K. Identifying topologically associating domains and subdomains by Gaussian Mixture model and Proportion test. Nat Commun, 2017, 8(1): 535.
9. Wang X T, Cui W, Peng C. HiTAD: detecting the structural and functional hierarchies of topologically associating domains from chromatin interactions. Nucleic Acids Res, 2017, 45(19): e163.
10. Levy-Leduc C, Delattre M, Mary-Huard T, et al. Two-dimensional segmentation for analyzing Hi-C data. Bioinformatics, 2014, 30(17): i386-i392.
11. Weinreb C, Raphael B J. Identification of hierarchical chromatin domains. Bioinformatics, 2016, 32(11): 1601-1609.
12. Ron G, Globerson Y, Moran D, et al. Promoter-enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains. Nat Commun, 2017, 8(1): 2237.
13. Serra F, Baù D, Goodstadt M, et al. Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors. Plos Comput Biol, 2017, 13(7): e1005665.
14. Xing H, Wu Y, Zhang M Q, et al. Deciphering hierarchical organization of topologically associated domains through change-point testing. BMC Bioinformatics, 2021, 22(1): 183.
15. Oluwadare O, Cheng J. ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data. BMC Bioinformatics, 2017, 18(1): 480.
16. Haddad N, Vaillant C, Jost D. IC-Finder: inferring robustly the hierarchical organization of chromatin folding. Nucleic Acids Res, 2017, 45(10): e81.
17. Gong H, Yang Y, Zhang X, et al. CASPIAN: A method to identify chromatin topological associated domains based on spatial density cluster. Comput Struct Biotec, 2022, 20: 4816-4824.
18. Gong H, Zhang D, Zhang X. TOAST: A novel method for identifying topologically associated domains based on graph auto-encoders and clustering. Comput Struct Biotec, 2023, 21: 4759-4768.
19. Yan K K, Lou S, Gerstein M. MrTADFinder: A network modularity based approach to identify topologically associating domains in multiple resolutions. PLoS Comput Biol, 2017, 13(7): e1005647.
20. Norton H K, Emerson D J, Huang H, et al. Detecting hierarchical genome folding with network modularity. Nat Methods, 2018, 15(2): 119-122.
21. Imakaev M, Fudenberg G, Mccord R P, et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat Methods, 2012, 9(10): 999-1003.
22. Knight P A, Ruiz D. A fast algorithm for matrix balancing. IMA J Numer Anal, 2012, 33(3): 1029-1047.
23. Ankerst M, Breunig M M, Kriegel H-P, et al. OPTICS: ordering points to identify the clustering structure. SIGMOD Rec, 1999, 28(2): 49-60.
24. Bentley J L. Multidimensional binary search trees used for associative searching. Commun Acm, 1975, 18(9): 509-517.
25. Wang Y, Li Y, Gao J, et al. A novel method to identify topological domains using Hi-C data. Quant Biol, 2015, 3(2): 81-89.
26. Trussart M, Serra F, Baù D, et al. Assessing the limits of restraint-based 3D modeling of genomes and genomic domains. Nucleic Acids Res, 2015, 43(7): 3465-3477.
27. Durand N C, Robinson J T, Shamim M S, et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst, 2016, 3(1): 99-101.
28. Kim S, Yu N-K, Kaang B-K. CTCF as a multifunctional protein in genome regulation and gene expression. Exp Mol Med, 2015, 47(6): e166-e166.
29. Zuin J, Dixon J R, Van Der Reijden M I J A, et al. Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells. Proc Nat Acad Sci, 2014, 111(3): 996-1001.
30. Crane E, Bian Q, McCord R P, et al. Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature, 2015, 523(7559): 240-244.
31. Roayaei Ardakany A, Lonardi S. Efficient and accurate detection of topologically associating domains from contact maps// 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017, 88: 22: 1-22: 11.

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