New Method for Decoding Topologically Associating Domains in a Chromosome

Recent studies have shown that chromosomes in many organisms divide into discrete sections into different types of chromatin domains. In Mammals, chromosomes form smaller compartments along with small Topologically associating domains or simply TAD’s. These TAD’s will define the functional domains of gene regulation. The full level of functions of a TAD’s are yet to be known, as there is so much to learn from TAD’s functions like what more can these do, and some studies have shown that disrupting TADs leads to disease. Because changing the 3D structure of a chromosome will disrupt the entire gene regulation. The complex structure of a TADs is not fully understood, as researchers are still trying to find new things about TADs.

Prof. Angsheng Li a prominent Chinese scientist from the State Key Laboratory of Software Development Environment, Beihang University has discovered a new method for decoding topologically associating domains with ultra-low resolution Hi-C data. A Hi-C is one of the  Chromosome conformation capture techniques, these are various molecular biology methods used to identify the structure of a chromatin in a cell which includes 3C, 4C, 5C, and Hi-C. Prof. Angsheng Li and his team have developed a new method for decoding TADs by structural entropy. With his research, Submegabase-size topologically associating domains (TAD) were found in high-throughput chromatin interaction data (Hi-C).  But in order to detect the accurate TADs one has to follow ultra-deep sequencing methods or most advanced normalization procedures.

But, Prof. Angsheng Li has proposed a fast and normalization free method to decode the domains in chromosomes (deDoc) using structural information theory, a new theory proposed early by Prof Li and his coworker. In his method, he has treated the Hi-C contact matrix as the representation of a graph, in this, (deDoc) divides the graph into segments with minimal structural entropy and also showed that structural entropy can also be used to determine proper bin size of the Hi-C data. By the application of (deDoc) to collected Hi-C data from 10 single cells, Prof. Angsheng Li and his team were able to detect megabase-size TAD-like domains. This result indicates that the modular structure of a genome spatial organization is common to even a small cohort of single cells. His algorithms will help in conducting further investigations of chromosomal domains on a larger scale.

About the author:

Prof. Angsheng Li is from the State Key Laboratory of Software Development Environment, Beihang University. Angsheng Li was Research Professor of Institute of Software, Chinese Academy of Sciences from 1999 – 2018. He was born in 1964. He got first degree in Mathematics in Yunnan Normal University in 1984, and ph D in 1993 in Institute of Software, Chinese Academy of Sciences. He has been working for the Institute of Software, Chinese Academy of Sciences since 1993 after he finished his ph D. From 1998 to 2002, he was a visiting and Research Fellow in University of Leeds, working with Professor Barry Cooper (an academic descendant of Alan Turing) in Computability Theory. In 2003, he was awarded the Distinguished Young Investigator award of the National Natural Science Foundation of China. In 2008, he was selected by the Hundred Talent Program of Chinese Academy of Sciences. From 2008 to 2009, he was a visiting scientist in Computer Science Department, working with Professor Juris Hartmanis (the founder of Computational Complexity Theory). In 2012, he was invited as a visiting fellow by Isaac Newton Institute of Mathematical Sciences. His research areas include Computability Theory, Computational Theory, Network Theory and Information Science. In Computability theory, he solved a 40-year old open problem proposed by Lachlan. In the theory of information and computation, he proposed the notion of encoding tree of graphs, the metrics of structural entropy of graphs, compressing information of graphs and decoding information of graphs, and established the fundamental theory of structural information theory. The new theory provides the principles for networks and for data analysis. This theory resolved the grand challenges proposed by Shannon and Brooks in 1953 and 2003, respectively.

  1. Roy, A. L., Sen, R. & Roeder, R. G. Enhancer-promoter communication and transcriptional regulation of Igh. Trends Immunol. 32, 532–539 (2011).
  2. Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 84–98 (2012)
  3. Zhang, Y. et al. Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations. Nature 504, 306–310 (2013).
  4. Yu, M. & Ren, B. The three-dimensional organization of mammalian genomes. Annu. Rev. Cell Dev. Biol. 33, 265–289 (2017).
  5. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
  6. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
  7. Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).
  8. Forcato, M. et al. Comparison of computational methods for Hi-C data analysis. Nat. Methods 14, 679–685 (2017).
  9. Hong, S. & Kim, D. Computational characterization of chromatin domain boundary-associated genomic elements. Nucleic Acids Res. 45, 10403–10414 (2017).
  10. Narendra, V., Bulajic, M., Dekker, J., Mazzoni, E. O. & Reinberg, D. CTCF-mediated topological boundaries during development foster appropriate gene regulation. Genes Dev. 30, 2657–2662 (2016).
  11. Merkenschlager, M. & Nora, E. P. CTCF and cohesin in genome folding and transcriptional gene regulation. Annu. Rev. Genom. Hum. Genet. 17, 17–43 (2016).
  12. Wang, X. T., Cui, W. & Peng, C. HiTAD: detecting the structural and functional hierarchies of topologically associating domains from chromatin interactions. Nucleic Acids Res. 45, e163 (2017).
  13. Pope, B. D. et al. Topologically associating domains are stable units of replication-timing regulation. Nature 515, 402–405 (2014).
  14. Lupianez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025 (2015).
  15. Taberlay, P. C. et al. Three-dimensional disorganization of the cancer genome occurs coincident with long-range genetic and epigenetic alterations. Genome Res. 26, 719–731 (2016).
  16. Yu, W., He, B. & Tan, K. Identifying topologically associating domains and subdomains by Gaussian mixture model and proportion test. Nat. Commun. 8, 535 (2017).
  17. Chen, J., Hero, A. O. 3rd & Rajapakse, I. Spectral identification of topological domains. Bioinformatics 32, 2151–2158 (2016).
  18. Haddad, N., Vaillant, C. & Jost, D. IC-Finder: inferring robustly the hierarchical organization of chromatin folding. Nucleic Acids Res.45, e81 (2017).
  19. Filippova, D., Patro, R., Duggal, G. & Kingsford, C. Identification of alternative topological domains in chromatin. Algorithms Mol. Biol. 9, 14 (2014).
  20. Weinreb, C. & Raphael, B. J. Identification of hierarchical chromatin domains. Bioinformatics 32, 1601–1609 (2016).
  21. Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
  22. Malik, L. I. & Patro, R. Rich chromatin structure prediction from Hi-C data. bioRxiv Preprint at
  23. Norton, H. K. et al. Detecting hierarchical 3-D genome domain reconfiguration with network modularity. bioRxiv Preprint at
  24. Yan, K. K., Lou, S. & Gerstein, M. MrTADFinder: A network modularity based approach to identify topologically associating domains in multiple resolutions. PLoS. Comput. Biol. 13, e1005647 (2017).
  25. Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).
  26. Flyamer, I. M. et al. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544, 110–114 (2017).
  27. Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods14, 263–266 (2017).
  28. Nagano, T. et al. Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat. Protoc. 10, 1986–2003 (2015).
  29. Li, A. & Pan, Y. Structural information and dynamical complexity of networks. IEEE Trans. Inf. Theory 62, 3290–3339 (2016).
  30. Clauset, A., Newman, M. E. & Moore, C. Finding community structure in very large networks. Phys. Rev. E Stat. Nonlin. Soft. Matter Phys. 70, 066111 (2004).
  31. Rowley, M. J. et al. Evolutionarily conserved principles predict 3D chromatin organization. Mol. Cell 67, 837–852 e7 (2017).
  32. Hou, C., Li, L., Qin, Z. S. & Corces, V. G. Gene density, transcription, and insulators contribute to the partition of the Drosophila genome into physical domains. Mol. Cell 48, 471–484 (2012).
  33. Sexton, T. et al. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472 (2012).
  34. Tang, Z. et al. CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription. Cell 163, 1611–1627 (2015).
  35. Brooks, F. P. Three great challenges for half-century-old computer science. J. ACM 50, 25–26 (2003).
  36. Shannon, C. E. The lattice theory of information. IEEE Trans. Inf. Theory 1, 105–107 (1953).
  37. Huffman, D. A. A method for the construction of minimum-redundancy codes. Proc. IRE 40, 1098–1101 (1976).

Media Contact
Company Name: Scientific Media
Contact Person: Natalie Paris
Email: Send Email
Country: United Kingdom