Abstract
Massively parallel sequencing technology opens new possibilities for epigenetic research. Many methods have been developed based on the new sequencing platforms, allowing an ultra-deep mapping of epigenetic variants in a fast and cost-effective way. However, handling millions of short reads produced by these sequencing platforms is a huge challenge for many laboratories. Thus, there is a need for the development of accurate and fast computational tools for epigenetic studies in the new era of genomic sequencing.
Modified methylation-specific digital karyotyping (MMSDK) is an improved method for genome-wide DNA methylation profiling based on the combination of traditional MSDK and Illumina/Solexa sequencing. Here, we introduce our computational tools used in the MMSDK analysis process from the experimental design to statistical analysis. We have developed a mapping process based on the in silico simulation of combined enzyme cutting and tag extraction of the reference genome. Subsequently, the 20–21 nucleotides (nt) long tags obtained by sequencing are mapped to the simulated library using an open source software Mapping and Assembly with Qualities. Our computational methods include trimming, annotation, normalization, and counting the reads to obtain digital DNA methylation profiles. We present the complete protocol and discuss some important issues that should be considered by readers, such as handling of repeat sequences, SNPs, and normalization. The core part of this protocol (mapping and annotation of tags) is suitable for any tag profiling-based methods, and it could also be modified to analyze results from other types of epigenetic studies based on massively parallel sequencing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Margulies, M., Egholm, M., Altman, W. E., et al. (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature. 437, 376–380.
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., et al. (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature. 456, 53–59.
Shendure, J., Porreca, G. J., Reppas, N. B., et al. (2005) Accurate multiplex polony sequencing of an evolved bacterial genome. Science. 309, 1728–1732.
Laird, P. W. (2010) Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genet. 11, 191–203.
Bock, C., Lengauer, T. (2008) Computational epigenetics. Bioinformatics. 24, 1–10.
Li, H., Ruan, J., Durbin, R. (2008) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome research. 18, 1851–1858.
Li, J., Gao, F., Li, N., et al. (2009) An improved method for genome wide DNA methylation profiling correlated to transcription and genomic instability in two breast cancer cell lines. BMC Genomics. 10, 223.
Hanriot, L., Keime, C., Gay, N., et al. (2008) A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome. BMC Genomics. 9, 418.
MAQ (Mapping and Assembly with Qualities). (Accessed at http://maq.sourceforge.net.)
Barski, A., Cuddapah, S., Cui, K., et al. (2007) High-resolution profiling of histone methylations in the human genome. Cell. 129, 823–837.
Mikkelsen, T. S., Ku, M., Jaffe, D. B., et al. (2007) Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature. 448, 553–560.
Lister, R., O’Malley, R. C., Tonti-Filippini, J., et al. (2008) Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell. 133, 523–536.
Pomraning, K. R., Smith, K. M., Freitag, M. (2009) Genome-wide high throughput analysis of DNA methylation in eukaryotes. Methods. 47, 142–150.
Yao, D., Ehrlich, M., Henis, Y. I., Leof, E. B. (2002) Transforming growth factor-beta receptors interact with AP2 by direct binding to beta2 subunit. Mol Biol Cell. 13, 4001–4012.
Lister, R., Pelizzola, M., Dowen, R. H., et al. (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 462, 315–322.
Ehrlich, M. (2002) DNA methylation in cancer: too much, but also too little. Oncogene. 21, 5400–5413.
Zuckermann, A., Bohdjalian, A., Deviatko, E., et al. (2002) The University of Vienna experience in heart transplantation. Clin Transpl. 229–242.
The Reference Sequence (RefSeq). (Accessed at http://www.ncbi.nlm.nih.gov/RefSeq1.)
Kerkel, K., Spadola, A., Yuan, E., et al. (2008) Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation. Nat Genet. 40, 904–908.
Gal-Yam, E. N., Egger, G., Iniguez, L., et al. (2008) Frequent switching of Polycomb repressive marks and DNA hypermethylation in the PC3 prostate cancer cell line. Proc Natl Acad Sci U S A. 105, 12979–12984.
Oda, M., Glass, J. L., Thompson, R. F., et al. (2009) High-resolution genome-wide cytosine methylation profiling with simultaneous copy number analysis and optimization for limited cell numbers. Nucleic Acids Res. 37, 3829–3839.
Benjamini, Y., Hochberg, Y. (1995) Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc B Met. 57, 289–300.
Trinklein, N. D., Karaoz, U., Wu, J., et al. (2007) Integrated analysis of experimental data sets reveals many novel promoters in 1% of the human genome. Genome Res. 17, 720–731.
Gardiner-Garden, M., Frommer, M. (1987) CpG islands in vertebrate genomes. J Mol Biol. 196, 261–282.
Irizarry, R. A., Wu, H., Feinberg, A. P. (2009) A species-generalized probabilistic model-based definition of CpG islands. Mamm Genome. 20, 674–680.
Acknowledgments
This work is supported by the project “Molecular Tools for Optimal Personalized Treatment of Breast Cancer” under the auspices of Sino-Danish Breast Cancer Research Centre, financed by the Danish “Grundforskningsfonden” and “The Natural Science Foundation of China.” Support was also received from the project “Molecular Tools for Optimal Personalized Treatment of Colorectal Cancer” (FØSU), Danish Centre for Translational Breast Cancer Research (DCTB), “A Race Against Breast Cancer” (ARABC), the OAK foundation and “Den Bøhmske Fond.”
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Li, J., Zhao, Q., Bolund, L. (2011). Computational Methods for Epigenetic Analysis: The Protocol of Computational Analysis for Modified Methylation-Specific Digital Karyotyping Based on Massively Parallel Sequencing. In: Tollefsbol, T. (eds) Epigenetics Protocols. Methods in Molecular Biology, vol 791. Humana Press. https://doi.org/10.1007/978-1-61779-316-5_23
Download citation
DOI: https://doi.org/10.1007/978-1-61779-316-5_23
Published:
Publisher Name: Humana Press
Print ISBN: 978-1-61779-315-8
Online ISBN: 978-1-61779-316-5
eBook Packages: Springer Protocols