Differential methylation analysis for bisulfite sequencing using DSS

Hao Feng , Hao Wu

Quant. Biol. ›› 2019, Vol. 7 ›› Issue (4) : 327 -334.

PDF (955KB)
Quant. Biol. ›› 2019, Vol. 7 ›› Issue (4) : 327 -334. DOI: 10.1007/s40484-019-0183-8
PROTOCOL AND TUTORIAL
PROTOCOL AND TUTORIAL

Differential methylation analysis for bisulfite sequencing using DSS

Author information +
History +
PDF (955KB)

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

Cite this article

Download citation ▾
Hao Feng, Hao Wu. Differential methylation analysis for bisulfite sequencing using DSS. Quant. Biol., 2019, 7(4): 327-334 DOI:10.1007/s40484-019-0183-8

登录浏览全文

4963

注册一个新账户 忘记密码

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

RIGHTS & PERMISSIONS

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

AI Summary AI Mindmap
PDF (955KB)

Supplementary files

QB-19183-OF-FH_suppl_1

3099

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/