Alhudaj: CpG islands Detection Tool in Mammalian Genome Using C++

Osamah Alrouwab, Awatef Allafi, Hayat AboSheta, Yasmeen Tantoush, Dheba Mansour, Mahmoud Gargotti

Abstract


One of the unique combinations in the mammalian genome, that revolutionized concepts in the fields of genetics and molecular pathology is what is termed the CpG islands. However, the accurate and rapid determination of CpG islands for DNA sequences remains experimentally and computationally challenging. The main goal for this project to design an offline, cross-platform CpG islands detection tool. The Algorithm implemented for this study was the traditional sliding window algorithm by using the C++ programming language. Three datasets were used for evaluating the performance of the application. The ANK1 gene, SPTB gene, and RET gene sequence files were obtained from NCBI. In this study, the highest CGIs were reported in ANK1 (ankyrin 1) Gene which scored 13 successive islands whereas the lowest score was reported in RET (ret proto-oncogene) Gene which shows only 6 islands. Generally, the program fulfills the boundaries limits as expected. We strongly recommend for further work, the implementation of other algorithms in addition to the sliding window algorithm such as Hidden Markov Model (HMM).

Keywords


ANK1 gene, CpG islands, Hidden Markov Model (HMM), RET gene, sliding window algorithm.

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References


P. Takacs, “Philosophical, Historical, and Empirical Investigations into the Concept of Biological Fitness.” The Florida State University, 2017.

E. Balzano, F. Pelliccia, and S. Giunta, “Genome (in) stability at tandem repeats,” 2020.

I. M. Bonapace and F. Macchi, “UHRF1 coordinates DNA methylation and histone post-translational modifications in colon cancer.”

N. Aluru, S. I. Karchner, K. S. Krick, W. Zhu, and J. Liu, “Role of DNA methylation in altered gene expression patterns in adult zebrafish (Danio rerio) exposed to 3, 3’, 4, 4’, 5-pentachlorobiphenyl (PCB 126),” Environ. epigenetics, vol. 4, no. 1, p. dvy005, 2018.

M. L. Brandi, S. K. Agarwal, N. D. Perrier, K. E. Lines, G. D. Valk, and R. V Thakker, “Multiple endocrine neoplasia type 1: latest insights,” Endocr. Rev., vol. 42, no. 2, pp. 133–170, 2021.

V. V Khrustalev, T. A. Khrustaleva, N. Sharma, and R. Giri, “Mutational pressure in Zika virus: local ADAR-editing areas associated with pauses in translation and replication,” Front. Cell. Infect. Microbiol., vol. 7, p. 44, 2017.

A. Unnikrishnan, W. M. Freeman, J. Jackson, J. D. Wren, H. Porter, and A. Richardson, “The role of DNA methylation in epigenetics of aging,” Pharmacol. Ther., vol. 195, pp. 172–185, 2019.

S. Campuzano, M. Pedrero, P. Yánez‐Sedeño, and J. M. Pingarrón, “Advances in electrochemical (bio) sensing targeting epigenetic modifications of nucleic acids,” Electroanalysis, vol. 31, no. 10, pp. 1816–1832, 2019.

J. E. DeNizio, B. J. Dow, J. C. Serrano, U. Ghanty, A. C. Drohat, and R. M. Kohli, “TET-TDG Active DNA Demethylation at CpG and Non-CpG Sites,” J. Mol. Biol., vol. 433, no. 8, p. 166877, 2021.

D. S. Dunican, H. K. Mjoseng, L. Duthie, I. M. Flyamer, W. A. Bickmore, and R. R. Meehan, “Bivalent promoter hypermethylation in cancer is linked to the H327me3/H3K4me3 ratio in embryonic stem cells,” BMC Biol., vol. 18, no. 1, pp. 1–21, 2020.

L. Alsøe et al., “Uracil accumulation and mutagenesis dominated by cytosine deamination in CpG dinucleotides in mice lacking UNG and SMUG1,” Sci. Rep., vol. 7, no. 1, pp. 1–14, 2017.

S. Branciamore, Z.-X. Chen, A. D. Riggs, and S. N. Rodin, “CpG island clusters and pro-epigenetic selection for CpGs in protein-coding exons of HOX and other transcription factors,” Proc. Natl. Acad. Sci., vol. 107, no. 35, pp. 15485–15490, 2010.

V. Vymetalkova, P. Vodicka, S. Vodenkova, S. Alonso, and R. Schneider-Stock, “DNA methylation and chromatin modifiers in colorectal cancer,” Mol. Aspects Med., vol. 69, pp. 73–92, 2019.

R. A. Tahir, D. A. Zheng, A. Nazir, and H. Qing, “A review of computational algorithms for CpG islands detection,” J. Biosci., vol. 44, no. 6, pp. 1–11, 2019.

M. Gardiner-Garden and M. Frommer, “CpG islands in vertebrate genomes,” J. Mol. Biol., vol. 196, no. 2, pp. 261–282, 1987.

K. Kowal, A. Tkaczyk, T. Ząbek, M. Pierzchała, and B. Ślaska, “Comparative analysis of CpG sites and islands distributed in mitochondrial DNA of model organisms,” Animals, vol. 10, no. 4, p. 665, 2020.

D. Yalcin, “Sequential, Spatial and Functional Disposition of CpG Islands.” The University of Nebraska-Lincoln, 2020.

M. J. Pajares, C. Palanca-Ballester, R. Urtasun, E. Alemany-Cosme, A. Lahoz, and J. Sandoval, “Methods for analysis of specific DNA methylation status,” Methods, vol. 187, pp. 3–12, 2021.

P. A. Jones and S. B. Baylin, “The fundamental role of epigenetic events in cancer,” Nat. Rev. Genet., vol. 3, no. 6, pp. 415–428, 2002.

W. S. Park et al., “Hypermethylation of the RUNX3 gene in hepatocellular carcinoma,” Exp. Mol. Med., vol. 37, no. 4, pp. 276–281, 2005.

A. Bird, “DNA methylation patterns and epigenetic memory,” Genes Dev., vol. 16, no. 1, pp. 6–21, 2002.

E. Ratti and F. Boem, “Junk or Functional DNA? ENCODE and the Function Controversy.”

Y. Yu, T. Jia, and X. Chen, “The ‘how’and ‘where’of plant micro RNA s,” New Phytol., vol. 216, no. 4, pp. 1002–1017, 2017.

Z. Fan, B. Yue, X. Zhang, L. Du, and Z. Jian, “CpGIScan: an ultrafast tool for CpG islands identification from genome sequence,” Curr. Bioinform., vol. 12, no. 2, pp. 181–184, 2017.

K.-B. Kim, “CpG islands detector: a window-based CpG island search tool,” Genomics Inform., vol. 8, no. 1, pp. 58–61, 2010.

L.-Y. Chuang, C.-H. Yang, M.-C. Lin, and C.-H. Yang, “CpGPAP: CpG island predictor analysis platform,” BMC Genet., vol. 13, no. 1, pp. 1–9, 2012.




DOI: http://dx.doi.org/10.52155/ijpsat.v29.1.3618

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