Skip to main content
SpringerLink
Log in

Visualization of DNA methylation results through a GPU-based parallelization of the wavelet transform

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Different statistical approaches have been proposed last years for finding differentially methylated DNA regions, starting from the outputs of DNA methylation analysis tools. However, these approaches do not allow an interactive and flexible exploration of these regions. Additionally, they add a high computation workload when used with large datasets. In this paper, we propose a new approach consisting in the transformation of DNA methylation results into a methylation signal and the Haar wavelet transformation of that signal for the displaying of the methylation results at different scales. Additionally, we propose the parallelization of the Haar wavelet transform on the GPU, as well as the GPU-based visualization of the methylation signal. The performance evaluation results show that this is the first proposal which allows the interactive visualization of different methylation signals with different resolution levels, in such a way that it can be used to visually detect differentially methylated regions accurately, in a user-friendly and flexible way, and with a very low computational workload.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ashok V, Balakumaran T, Gowrishankar C, Vennila ILA, Kumar AN (2010) The fast haar wavelet transform for signal & image processing. Int J Comput Sci Inf Secur arXiv:abs/1002.2184

  2. de Mello V, Pulkkinen L, Lalli M, Kolehmainen M, Pihlajamki J, Uusitupa M (2014) DNA methylation in obesity and type 2 diabetes. Ann Med 46(3):103–13. https://doi.org/10.3109/07853890.2013.857259

    Article  Google Scholar 

  3. Feng H, Conneely KN, Wu H (2014) A bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res. 42(8):e69. https://doi.org/10.1093/nar/gku154

    Article  Google Scholar 

  4. Fernández L, Orduña L, Pérez M, Orduña JM (2018) A new approach for the visualization of DNA methylation results. In: Proceedings of the 18th International Conference on Mathematical Methods in Science and Engineering

  5. González C, Pérez M, Orduña JM, Chaves J, García AB (2017) On the use of binary trees for dna hydroxymethylation analysis. In: 5th International Workshop on Parallelism in Bioinformatics, As Part of ICA3PP 2017

  6. Hansen KD, Langmead B, Irizarry RA (2012) Bsmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol 13(10):R83. https://doi.org/10.1186/gb-2012-13-10-r83

    Article  Google Scholar 

  7. Hansen KD, Timp W, Bravo H, Langmead B (2011) Increased methylation variation in epigenetic domains across cancer types. Nat Genet 43(8):768–775

    Article  Google Scholar 

  8. Hebestreit K, Dugas M, Klein HU (2013) Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29(13):1647–1653. https://doi.org/10.1093/bioinformatics/btt263

    Article  Google Scholar 

  9. Krueger F, Andrews SR (2011) Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27(11):1571–1572. https://doi.org/10.1093/bioinformatics/btr167

    Article  Google Scholar 

  10. Laird PW (2010) Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genet 11:191–203. https://doi.org/10.1038/nrg2732

    Article  Google Scholar 

  11. Lee W, Morris JS (2016) Identification of differentially methylated loci using wavelet-based functional mixed models. Bioinformatics 32(5):664–672

    Article  Google Scholar 

  12. Olanda R, Pérez M, Orduña JM, Tárraga J, Dopazo J (2017) A new parallel pipeline for DNA methylation analysis of long reads datasets. BMC Bioinform 18(1):161. https://doi.org/10.1186/s12859-017-1574-3

    Article  Google Scholar 

  13. Park Y, Figueroa ME, Rozek LS, Sartor MA (2014) Methylsig: a whole genome DNA methylation analysis pipeline. Bioinformatics 30(17):2414–2422

    Article  Google Scholar 

  14. Raciti A, Nigro C, Longo M, Parrillo L, Miele C, Formisano P, Bguino F (2014) Personalized medicine and type 2 diabetes: lesson from epigenetics. Epigenomics 6(2):229–238. https://doi.org/10.2217/epi.14.10

    Article  Google Scholar 

  15. Saito Y, Tsuji J, Mituyama T (2014) Bisulfighter: accurate detection of methylated cytosines and differentially methylated regions. Nucleic Acids Res 42(6):e45

    Article  Google Scholar 

  16. Stankovic RS, Falkowski BJ (2003) The haar wavelet transform: its status and achievements. Comput Electr Eng 29(1):25–44

    Article  MATH  Google Scholar 

  17. Sun D, Xi Y, Rodriguez B, Park HJ, Tong P, Meong M, Goodell MA, Li W (2014) Moabs: model based analysis of bisulfite sequencing data. Genome Biol 15(2):R38. https://doi.org/10.1186/gb-2014-15-2-r38

    Article  Google Scholar 

  18. Tárraga J, Pérez M, Orduña JM, Duato J, Medina I, Dopazo J (2015) A parallel and sensitive software tool for methylation analysis on multicore platforms. Bioinformatics 31(19):3130. https://doi.org/10.1093/bioinformatics/btv357

    Article  Google Scholar 

  19. Wu H, Xu T, Feng H, Chen L, Li B, Yao B, Qin Z, Jin P, Conneely KN (2015) Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates. Nucleic Acids Res 43(21):e141. https://doi.org/10.1093/nar/gkv715

    Google Scholar 

  20. Xi Y, Bock C, Muller F, Sun D, Meissner A, Li W (2012) RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing. Bioinformatics 28(3):430–432

    Article  Google Scholar 

  21. Xu Z, Taylor JA, Leung YK, Ho SM, Niu L (2016) oxBS-MLE: an efficient method to estimate 5-methylcytosine and 5-hydroxymethylcytosine in paired bisulfite and oxidative bisulfite treated DNA. Bioinformatics 32(23):3667–3669

    Google Scholar 

  22. Yu M, Hon GC, Szulwach KE, Song CX, Zhang L, Kim A, Li X, Dai Q, Park B, Min JH, Jin P, Bing, He C (2012) Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome. Cell 149(6):1368–1380

    Article  Google Scholar 

  23. Zhang Y, Liu H, Lv J, Xiao X, Zhu J, Liu X, Su J, Li X, Wu Q, Wang F, Cui Y (2011) QDMR: a quantitative method for identification of differentially methylated regions by entropy. Nucleic Acids Res 39(9):e58

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Informática, Universidad de Valencia, Valencia, Spain

    Lisardo Fernández, Mariano Pérez & Juan M. Orduña

Authors
  1. Lisardo Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariano Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan M. Orduña
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan M. Orduña.

Additional information

This work has been supported by Spanish MINECO and EU ERDF programs under Grants TIN2015-66972-C5-5-R, TIN2016-81840-REDT, and TIN2016-81850-REDC.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fernández, L., Pérez, M. & Orduña, J.M. Visualization of DNA methylation results through a GPU-based parallelization of the wavelet transform. J Supercomput 75, 1496–1509 (2019). https://doi.org/10.1007/s11227-018-2670-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2670-5

Keywords

Search

Navigation