Differential methylation analysis for bisulfite sequencing using DSS

Abstract

Bisulfite sequencing (BS-seq) technology measures DNA methylation at single nucleotide resolution. A key task in BS-seq data analysis is to identify differentially methylation (DM) under different conditions. Here we provide a tutorial for BS-seq DM analysis using Bioconductor package DSS. DSS uses a beta-binomial model to characterize the sequence counts from BS-seq, and implements rigorous statistical method for hypothesis testing. It provides flexible functionalities for a variety of DM analyses.

Keywords

epigenetics / DNA methylation / bisulfite sequencing / differential methylation

References

[1] Bestor, T. H. (2000) The DNA methyltransferases of mammals. Hum. Mol. Genet., 9, 2395–2402

[2] Bird, A. (2002) DNA methylation patterns and epigenetic memory. Genes Dev., 16, 6–21

[3] Reik, W. (2007) Stability and flexibility of epigenetic gene regulation in mammalian development. Nature, 447, 425–432

[4] Li, E., Beard, C. and Jaenisch, R. (1993) Role for DNA methylation in genomic imprinting. Nature, 366, 362–365

[5] Jones, P. A. (2012) Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet., 13, 484–492

[6] Jones, P. A. and Takai, D. (2001) The role of DNA methylation in mammalian epigenetics. Science, 293, 1068–1070

[7] Baylin, S. B. (2005) DNA methylation and gene silencing in cancer. Nat. Clin. Pract. Oncol., 2, S4–S11

[8] Laird, P. W. and Jaenisch, R. (1996) The role of DNA methylation in cancer genetic and epigenetics. Annu. Rev. Genet., 30, 441–464

[9] Jones, P. A. (1996) DNA methylation errors and cancer. Cancer Res., 56, 2463–2467

[10] Feng, H., Jin, P. and Wu, H. (2018) Disease prediction by cell-free DNA methylation. Brief. Bioinform., 20, 585–597

[11] Lister, R., O’Malley, R. C., Tonti-Filippini, J., Gregory, B. D., Berry, C. C., Millar, A. H. and Ecker, J. R. (2008) Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell, 133, 523–536

[12] Zilberman, D., Gehring, M., Tran, R. K., Ballinger, T. and Henikoff, S. (2007) Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription. Nat. Genet., 39, 61–69

[13] Cokus, S. J., Feng, S., Zhang, X., Chen, Z., Merriman, B., Haudenschild, C. D., Pradhan, S., Nelson, S. F., Pellegrini, M. and Jacobsen, S. E. (2008) Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature, 452, 215–219

[14] Zemach, A., McDaniel, I. E., Silva, P. and Zilberman, D. (2010) Genome-wide evolutionary analysis of eukaryotic DNA methylation. Science, 328, 916–919

[15] Akalin, A., Kormaksson, M., Li, S., Garrett-Bakelman, F. E., Figueroa, M. E., Melnick, A. and Mason, C. E. (2012) methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol., 13, R87

[16] Hansen, K. D., Langmead, B. and Irizarry, R. A. (2012) BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol., 13, R83

[17] Hebestreit, K., Dugas, M. and Klein, H. U. (2013) Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics, 29, 1647–1653

[18] Feng, H., Conneely, K. N. and Wu, H. (2014) A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res., 42, e69

[19] Wu, H., Xu, T.L., Feng, H., Chen, L., Li, B., Yao, B., Qin, Z.H., Jin, P., and Conneely, K.N. (2015) Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates. Nucleic acids Res. 43, e141

[20] Park, Y. and Wu, H. (2016) Differential methylation analysis for BS-seq data under general experimental design. Bioinformatics, 32, 1446–1453

[21] Yu, X. and Sun, S. (2016) HMM-DM: identifying differentially methylated regions using a hidden Markov model. Stat. Appl. Genet. Mol. Biol., 15, 69–81

[22] Sun, S. and Yu, X. (2016) HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher’s exact test. Stat. Appl. Genet. Mol. Biol., 15, 55–67

[23] Assenov, Y., Müller, F., Lutsik, P., Walter, J., Lengauer, T. and Bock, C. (2014) Comprehensive analysis of DNA methylation data with RnBeads. Nat. Methods, 11, 1138–1140

[24] Smyth, G.K. (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3,1–25 .

[25] Love, M. I., Huber, W. and Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol., 15, 550

[26] Wu, H., Wang, C. and Wu, Z. (2012) A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics,

[27] Krueger, F. and Andrews, S. R. (2011) Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 27, 1571–1572

[28] Hascher, A., Haase, A.K., Hebestreit, K., Rohde, C., Klein, H.U., Rius, M., Jungen, D., Witten, A., Stoll, M., Schulze, I., (2014) DNA methyltransferase inhibition reverses epigenetically embedded phenotypes in lung cancer preferentially affecting polycomb target genes. Clin. Cancer Res., 4,814–826

[29] Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R. and Ma’ayan, A. (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics, 14, 128

[30] Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res., 44, W90–W97

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-019-0183-8.

ACKNOWLEDGEMENTS

We thank all co-authors for the three DSS papers, in particular Karen Conneely and Yongseok Park. They made important contribution for the statistical method development in DSS.

COMPIANCE WITH ETHICS GUIDLINES

The authors Hao Feng and Hao Wu declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of authors.

RIGHTS & PERMISSIONS

2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature