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Volume: 7, Issue: 1, Jan-Feb, 2019
DOI: 10.7324/JABB.2019.70114

Mini Review

Computational-based approaches in epigenetic research: Insights from computational tools, mathematical models, and machine learning methods

Roghayeh Ghorbani, Reza Shokri-Gharelo

  Author Affiliations


Understudying epigenetics underlying mechanisms is essential. Studies have indicated functional roles of epigenetic events, including DNA methylation and histone modifications, in human disease, stem cell growth and development, aging, response to environmental stresses, and species evolution. High-throughput sequencing techniques alongside routine experimental approaches have rapidly produced a bulk of data that the major part of them remained unprocessed. To analyze, interpret, and process of these data, availability of efficient computational methods is critical. Epigenetic data analysis is complex and difficult because these data contain a multi-layer set of information. The methods implemented for this purpose must be able to handle massive data of experimental works and process the epigenetic layers. Furthermore, the methods must be capable of integrating multiple modifications and their combination effects on chromatin conformational structure and consequently the expression network of genes. In this study, we briefly reviewed challenges in the way of the computational epigenetics, the latest reported methods, and significant biological results derived from applying computational-based methods on epigenetic data.


Bioinformatics, Computational epigenetics, Data analysis, DNA methylation, Epigenomics.

Citation: Ghorbani R, Shokri-Gharelo R. Computational-based approaches in epigenetic research: Insights from computational tools, mathematical models, and machine learning methods. J App Biol Biotech. 2018. Online first

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.


1. Handel A E, Ebers G C, and Ramagopalan S V. Epigenetics: molecular mechanisms and implications for disease. Trends in molecular medicine. 2010; 16 (1): 7-16. https://doi.org/10.1016/j.molmed.2009.11.003

2. Chinnusamy V and Zhu J-K. Epigenetic regulation of stress responses in plants. Current opinion in plant biology. 2009; 12 (2): 133-139. https://doi.org/10.1016/j.pbi.2008.12.006

3. Weinhold B. Epigenetics: the science of change. Environmental health perspectives. 2006; 114 (3): A160. https://doi.org/10.1289/ehp.114-a160

4. Lima R, Hayashi D, Lima K, Gomes N, et al. The Role of Epigenetics in the Etiology of Obesity: A Review. J Clin Epigenet. 2017; 3 (4): 41.

5. Allis C D and Jenuwein T. The molecular hallmarks of epigenetic control. Nature Reviews Genetics. 2016. https://doi.org/10.1038/nrg.2016.59

6. Buck M J and Lieb J D. ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics. 2004; 83 (3): 349-360. https://doi.org/10.1016/j.ygeno.2003.11.004

7. Jacinto F V, Ballestar E, and Esteller M. Methyl-DNA immunoprecipitation (MeDIP): hunting down the DNA methylome. Biotechniques. 2008; 44 (1): 35. https://doi.org/10.2144/000112708

8. Park P J. ChIP–seq: advantages and challenges of a maturing technology. Nature Reviews Genetics. 2009; 10 (10): 669-680. https://doi.org/10.1038/nrg2641

9. Schones D E and Zhao K. Genome-wide approaches to studying chromatin modifications. Nature Reviews Genetics. 2008; 9 (3): 179-191. https://doi.org/10.1038/nrg2270

10. Hajkova P, El-Maarri O, Engemann S, Oswald J, et al. DNA-methylation analysis by the bisulfite-assisted genomic sequencing method. DNA methylation protocols. 2002; 143-154.

11. Tollefsbol T O, Epigenetics protocols. Vol. 287. 2004: Springer Science & Business Media. https://doi.org/10.1385/1592598285

12. Lim S J, Tan T W, and Tong J C. Computational Epigenetics: the new scientific paradigm. Bioinformation. 2010; 4 (7): 331. https://doi.org/10.6026/97320630004331

13. Robinson M D and Pelizzola M. Computational epigenomics: challenges and opportunities. Frontiers in genetics. 2015; 6. https://doi.org/10.3389/fgene.2015.00088

14. Flensburg C, Kinkel S A, Keniry A, Blewitt M E, et al. A comparison of control samples for ChIP-seq of histone modifications. Frontiers in genetics. 2014; 5. https://doi.org/10.3389/fgene.2014.00329

15. Robinson M D, Kahraman A, Law C W, Lindsay H, et al. Statistical methods for detecting differentially methylated loci and regions. Frontiers in genetics. 2014; 5. https://doi.org/10.3389/fgene.2014.00324

16. Osella M, Riba A, Testori A, Corà D, et al. Interplay of microRNA and epigenetic regulation in the human regulatory network. Frontiers in genetics. 2014; 5. https://doi.org/10.3389/fgene.2014.00345

17. Mensaert K, Van Criekinge W, Thas O, Schuuring E, et al. Mining for viral fragments in methylation enriched sequencing data. Frontiers in genetics. 2015; 6 16. https://doi.org/10.3389/fgene.2015.00016

18. Bock C and Lengauer T. Computational epigenetics. Bioinformatics. 2008; 24 (1): 1-10. https://doi.org/10.1093/bioinformatics/btm546

19. Barazandeh A, Mohammadabadi M, Ghaderi-Zefrehei M, and Nezamabadi-Pour H. Genome-wide analysis of CpG islands in some livestock genomes and their relationship with genomic features. Czech Journal of Animal Science. 2016; 61 (11): 487-495. https://doi.org/10.17221/78/2015-CJAS

20. Hackenberg M, Barturen G, Carpena P, Luque-Escamilla P L, et al. Prediction of CpG-island function: CpG clustering vs. sliding-window methods. BMC genomics. 2010; 11 (1): 327. https://doi.org/10.1186/1471-2164-11-327

21. Barazandeh A, Mohammadabadi M, Ghaderi-Zefrehei M, and Nezamabadipour H. Predicting CpG Islands and Their Relationship with Genomic Feature in Cattle by Hidden Markov Model Algorithm. Iranian Journal of Applied Animal Science. 2016; 6 (3): 571-579.

22. Su J, Zhang Y, Lv J, Liu H, et al. CpG_MI: a novel approach for identifying functional CpG islands in mammalian genomes. Nucleic acids research. 2009; 38 (1): e6-e6. https://doi.org/10.1093/nar/gkp882

23. Gardiner-Garden M and Frommer M. CpG islands in vertebrate genomes. Journal of molecular biology. 1987; 196 (2): 261-282. https://doi.org/10.1016/0022-2836(87)90689-9

24. Marchevsky A M, Tsou J A, and Laird-Offringa I A. Classification of individual lung cancer cell lines based on DNA methylation markers: use of linear discriminant analysis and artificial neural networks. The Journal of Molecular Diagnostics. 2004; 6 (1): 28-36. https://doi.org/10.1016/S1525-1578(10)60488-6

25. Das R, Dimitrova N, Xuan Z, Rollins R A, et al. Computational prediction of methylation status in human genomic sequences. Proceedings of the National Academy of Sciences. 2006; 103 (28): 10713-10716. https://doi.org/10.1073/pnas.0602949103

26. Bhasin M, Zhang H, Reinherz E L, and Reche P A. Prediction of methylated CpGs in DNA sequences using a support vector machine. FEBS letters. 2005; 579 (20): 4302-4308. https://doi.org/10.1016/j.febslet.2005.07.002

27. Chen H, Xue Y, Huang N, Yao X, et al. MeMo: a web tool for prediction of protein methylation modifications. Nucleic acids research. 2006; 34 (suppl_2): W249-W253.

28. Takai D and Jones P A. Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proceedings of the national academy of sciences. 2002; 99 (6): 3740-3745. https://doi.org/10.1073/pnas.052410099

29. Weber M, Hellmann I, Stadler M B, Ramos L, et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nature genetics. 2007; 39 (4): 457-466. https://doi.org/10.1038/ng1990

30. Yamada Y, Watanabe H, Miura F, Soejima H, et al. A comprehensive analysis of allelic methylation status of CpG islands on human chromosome 21q. Genome research. 2004; 14 (2): 247-266. https://doi.org/10.1101/gr.1351604

31. Bock C, Walter J, Paulsen M, and Lengauer T. CpG island mapping by epigenome prediction. PLoS computational biology. 2007; 3 (6): e110. https://doi.org/10.1371/journal.pcbi.0030110

32. Bhutani N, Burns D M, and Blau H M. DNA demethylation dynamics. Cell. 2011; 146 (6): 866-872. https://doi.org/10.1016/j.cell.2011.08.042

33. Wu H and Zhang Y. Charting oxidized methylcytosines at base resolution. Nature Structural and Molecular Biology. 2015; 22 (9): 656. https://doi.org/10.1038/nsmb.3071

34. Kroeze L I, van der Reijden B A, and Jansen J H. 5-Hydroxymethylcytosine: An epigenetic mark frequently deregulated in cancer. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer. 2015; 1855 (2): 144-154. https://doi.org/10.1016/j.bbcan.2015.01.001

35. Guo J U, Su Y, Zhong C, Ming G-l, et al. Emerging roles of TET proteins and 5-hydroxymethylcytosines in active DNA demethylation and beyond. Cell cycle. 2011; 10 (16): 2662-2668. https://doi.org/10.4161/cc.10.16.17093

36. Drew H R, Wing R M, Takano T, Broka C, et al. Structure of a B-DNA dodecamer: conformation and dynamics. Proceedings of the National Academy of Sciences. 1981; 78 (4): 2179-2183. https://doi.org/10.1073/pnas.78.4.2179

37. Lercher L, McDonough M A, El-Sagheer A H, Thalhammer A, et al. Structural insights into how 5-hydroxymethylation influences transcription factor binding. Chemical Communications. 2014; 50 (15): 1794-1796. https://doi.org/10.1039/C3CC48151D

38. Krawczyk K, Demharter S, Knapp B, Deane C M, et al. In silico structural modeling of multiple epigenetic marks on DNA. Bioinformatics. 2017; 34 (1): 41-48. https://doi.org/10.1093/bioinformatics/btx516

39. Dodd I B, Micheelsen M A, Sneppen K, and Thon G. Theoretical analysis of epigenetic cell memory by nucleosome modification. Cell. 2007; 129 (4): 813-822. https://doi.org/10.1016/j.cell.2007.02.053

40. Sch\übeler D, MacAlpine D M, Scalzo D, Wirbelauer C, et al. The histone modification pattern of active genes revealed through genome-wide chromatin analysis of a higher eukaryote. Genes & development. 2004; 18 (11): 1263-1271. https://doi.org/10.1101/gad.1198204

41. Roh T-Y, Cuddapah S, and Zhao K. Active chromatin domains are defined by acetylation islands revealed by genome-wide mapping. Genes & development. 2005; 19 (5): 542-552. https://doi.org/10.1101/gad.1272505

42. Xu H, Wei C-L, Lin F, and Sung W-K. An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data. Bioinformatics. 2008; 24 (20): 2344-2349. https://doi.org/10.1093/bioinformatics/btn402

43. Won K-J, Chepelev I, Ren B, and Wang W. Prediction of regulatory elements in mammalian genomes using chromatin signatures. BMC bioinformatics. 2008; 9 (1): 547. https://doi.org/10.1186/1471-2105-9-547

44. Kouskoumvekaki I, Hansen N T, Björkling F, Vadlamudi S, et al. Prediction of pH-dependent aqueous solubility of Histone Deacetylase (HDAC) inhibitors. SAR and QSAR in Environmental Research. 2008; 19 (1-2): 167-177. https://doi.org/10.1080/10629360701843367

45. Thurman R E, Day N, Noble W S, and Stamatoyannopoulos J A. Identification of higher-order functional domains in the human ENCODE regions. Genome research. 2007; 17 (6): 917-927. https://doi.org/10.1101/gr.6081407

46. Benveniste D, Sonntag H-J, Sanguinetti G, and Sproul D. Transcription factor binding predicts histone modifications in human cell lines. Proceedings of the National Academy of Sciences. 2014; 111 (37): 13367-13372. https://doi.org/10.1073/pnas.1412081111

47. Juvale D C, Kulkarni V V, Deokar H S, Wagh N K, et al. 3D-QSAR of histone deacetylase inhibitors: hydroxamate analogues. Organic & biomolecular chemistry. 2006; 4 (15): 2858-2868. https://doi.org/10.1039/b606365a

48. Lin Y-C, Lin J-H, Chou C-W, Chang Y-F, et al. Statins increase p21 through inhibition of histone deacetylase activity and release of promoter-associated HDAC1/2. Cancer research. 2008; 68 (7): 2375-2383. https://doi.org/10.1158/0008-5472.CAN-07-5807

49. Roudbar M A, Mohammadabadi M, and Salmani V. Epigenetics: A new Challenge In Animal Breeding. Genetics in the Third Millennium. 2014; 12 (4): 3900-3914.

50. Strahl B D and Allis C D. The language of covalent histone modifications. Nature. 2000; 403 (6765): 41. https://doi.org/10.1038/47412

51. Hon G, Ren B, and Wang W. ChromaSig: a probabilistic approach to finding common chromatin signatures in the human genome. PLoS computational biology. 2008; 4 (10): e1000201. https://doi.org/10.1371/journal.pcbi.1000201

52. Ernst J and Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nature methods. 2012; 9 (3): 215. https://doi.org/10.1038/nmeth.1906

53. Zhou J and Troyanskaya O G. Global quantitative modeling of chromatin factor interactions. PLoS computational biology. 2014; 10 (3): e1003525. https://doi.org/10.1371/journal.pcbi.1003525

54. Bock C, Walter J, Paulsen M, and Lengauer T. Inter-individual variation of DNA methylation and its implications for large-scale epigenome mapping. Nucleic acids research. 2008; 36 (10): e55-e55. https://doi.org/10.1093/nar/gkn122

55. Adorján P, Distler J, Lipscher E, Model F, et al. Tumour class prediction and discovery by microarray-based DNA methylation analysis. Nucleic acids research. 2002; 30 (5): e21-e21. https://doi.org/10.1093/nar/30.5.e21

56. Weisenberger D J, Siegmund K D, Campan M, Young J, et al. CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer. Nature genetics. 2006; 38 (7): 787-793. https://doi.org/10.1038/ng1834

57. Spivakov M and Fisher A G. Epigenetic signatures of stem-cell identity. Nature Reviews Genetics. 2007; 8 (4): 263-271. https://doi.org/10.1038/nrg2046

58. Walker E, Ohishi M, Davey R E, Zhang W, et al. Prediction and testing of novel transcriptional networks regulating embryonic stem cell self-renewal and commitment. Cell stem cell. 2007; 1 (1): 71-86. https://doi.org/10.1016/j.stem.2007.04.002

59. Ringrose L, Rehmsmeier M, Dura J-M, and Paro R. Genome-wide prediction of Polycomb/Trithorax response elements in Drosophila melanogaster. Developmental cell. 2003; 5 (5): 759-771. https://doi.org/10.1016/S1534-5807(03)00337-X

60. Khanam Irin A, G\ündel M, and Hofmann-Apitius M. Computational modelling approaches on epigenetic factors in neurodegenerative and autoimmune diseases and their mechanistic analysis. Journal of immunology research. 2015; 2015.

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