Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates

Nucleic Acids Research

Volumn 43, Issue 21, 2015, Pages

  Wu, Hao ^a   Xu, Tianlei ^b   Feng, Hao ^a   Chen, Li ^b   Li, Ben ^a   Yao, Bing ^c   Qin, Zhaohui ^a   Jin, Peng ^c   Conneely, Karen N ^c  

^a EMORY UNIVERSITY   (United States)

^b EMORY UNIVERSITY   (United States)

^c EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER SIMULATION; CPG ISLAND; DATA ANALYSIS; DIFFERENTIALLY METHYLATED REGION; DNA METHYLATION; DNA STRUCTURE; FIBROBLAST; HIPPOCAMPUS; HUMAN; HUMAN EMBRYONIC STEM CELL; INTERMETHOD COMPARISON; LIVER; PRIORITY JOURNAL; SEQUENCE ANALYSIS; STATISTICAL ANALYSIS; WHOLE GENOME BISULFITE SEQUENCING; CELL LINE; DNA SEQUENCE; GENOMICS; PROCEDURES;

HYDROGEN SULFITE; SULFITE;

CELL LINE; DATA INTERPRETATION, STATISTICAL; DNA METHYLATION; GENOMICS; HUMANS; SEQUENCE ANALYSIS, DNA; SULFITES;

EID: 84983775821     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkv715     Document Type: Article

References (36)

1. Stadler, M.B., Murr, R., Burger, L., Ivanek, R., Lienert, F., Scholer, A., van Nimwegen, E., Wirbelauer, C., Oakeley, E.J., Gaidatzis, D. et al. (2011) DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature, 480, 490-495.
2. Teschendorff, A.E., Menon, U., Gentry-Maharaj, A., Ramus, S.J., Weisenberger, D.J., Shen, H., Campan, M., Noushmehr, H., Bell, C.G., Maxwell, A.P. et al. (2010) Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res., 20, 440-446.
3. Hansen, K.D., Timp, W., Bravo, H.C., Sabunciyan, S., Langmead, B., McDonald, O.G., Wen, B., Wu, H., Liu, Y., Diep, D. et al. (2011) Increased methylation variation in epigenetic domains across cancer types. Nat. Genet., 43, 768-775.
4. Jones, P.A. (2012) Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet., 13, 484-492.
5. Schubeler, D. (2015) Function and information content of DNA methylation. Nature, 517, 321-326.
6. Lister, R., Pelizzola, M., Dowen, R.H., Hawkins, R.D., Hon, G., Tonti-Filippini, J., Nery, J.R., Lee, L., Ye, Z., Ngo, Q.M. et al. (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature, 462, 315-322.
7. 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.
8. 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.
9. Benoukraf, T., Wongphayak, S., Hadi, L.H., Wu, M. and Soong, R. (2013) GBSA: a comprehensive software for analysing whole genome bisulfite sequencing data. Nucleic Acids Res., 41, e55.
10. Hebestreit, K., Dugas, M. and Klein, H.U. (2013) Detection of Significantly Differentially Methylated Regions in Targeted Bisulfite Sequencing Data. Bioinformatics, 29, 1647-1653.
11. 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.
12. Sun, D., Xi, Y., Rodriguez, B., Park, H.J., Tong, P., Meong, M., Goodell, M.A. and Li, W. (2014) MOABS: model based analysis of bisulfite sequencing data. Genome Biol., 15, R38.
13. Stockwell, P.A., Chatterjee, A., Rodger, E.J. and Morison, I.M. (2014) DMAP: differential methylation analysis package for RRBS and WGBS data. Bioinformatics, 30, 1814-1822.
14. Park, Y., Figueroa, M.E., Rozek, L.S. and Sartor, M.A. (2014) methylSig: a whole genome DNA methylation analysis pipeline. Bioinformatics, 30, 2414-2422.
15. Saito, Y., Tsuji, J. and Mituyama, T. (2014) Bisulfighter: accurate detection of methylated cytosines and differentially methylated regions. Nucleic Acids Res., 42, e45.
16. Wu, H., Wang, C. and Wu, Z. (2013) A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics, 14, 232-243.
17. Robinson, M.D. and Smyth, G.K. (2007) Moderated statistical tests for assessing differences in tag abundance. Bioinformatics, 23, 2881-2887.
18. Zhao, L., Sun, M.A., Li, Z., Bai, X., Yu, M., Wang, M., Liang, L., Shao, X., Arnovitz, S., Wang, Q. et al. (2014) The dynamics of DNA methylation fidelity during mouse embryonic stem cell self-renewal and differentiation. Genome Res., 24, 1296-1307.
19. Lu, F., Liu, Y., Jiang, L., Yamaguchi, S. and Zhang, Y. (2014) Role of Tet proteins in enhancer activity and telomere elongation. Genes Dev., 28, 2103-2119.
20. Xie, W., Barr, C.L., Kim, A., Yue, F., Lee, A.Y., Eubanks, J., Dempster, E.L. and Ren, B. (2012) Base-resolution analyses of sequence and parent-of-origin dependent DNA methylation in the mouse genome. Cell, 148, 816-831.
21. Lee, S.T., Muench, M.O., Fomin, M.E., Xiao, J., Zhou, M., de Smith, A., Martin-Subero, J.I., Heath, S., Houseman, E.A., Roy, R. et al. (2015) Epigenetic remodeling in B-cell acute lymphoblastic leukemia occurs in two tracks and employs embryonic stem cell-like signatures. Nucleic Acids Res., 43, 2590-2602.
22. Bernstein, B.E., Stamatoyannopoulos, J.A., Costello, J.F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M.A., Beaudet, A.L., Ecker, J.R. et al. (2010) The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol., 28, 1045-1048.
23. Maher, B. (2012) ENCODE: The human encyclopaedia. Nature, 489, 46-48.
24. Shao, Z., Zhang, Y., Yuan, G.C., Orkin, S.H. and Waxman, D.J. (2012) MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets. Genome Biol., 13, R16.
25. Kent, W.J., Sugnet, C.W., Furey, T.S., Roskin, K.M., Pringle, T.H., Zahler, A.M. and Haussler, D. (2002) The human genome browser at UCSC. Genome Res., 12, 996-1006.
26. Thurman, R.E., Rynes, E., Humbert, R., Vierstra, J., Maurano, M.T., Haugen, E., Sheffield, N.C., Stergachis, A.B., Wang, H., Vernot, B. et al. (2012) The accessible chromatin landscape of the human genome. Nature, 489, 75-82.
27. Xu, T., Li, B., Zhao, M., Szulwach, K.E., Street, R.C., Lin, L., Yao, B., Zhang, F., Jin, P., Wu, H. et al. (2015) Base-resolution methylation patterns accurately predict transcription factor bindings in vivo. Nucleic Acids Res., 43, 2757-2766.
28. Razin, A. and Cedar, H. (1991) DNA methylation and gene expression. Microbiol. Rev., 55, 451-458.
29. Irizarry, R.A., Ladd-Acosta, C., Wen, B., Wu, Z., Montano, C., Onyango, P., Cui, H., Gabo, K., Rongione, M., Webster, M. et al. (2009) The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat. Genet., 41, 178-186.
30. Ernst, J. and Kellis, M. (2012) ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods, 9, 215-216.
31. Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J. et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol., 5, R80.
32. Ruppert, D., Wand, M.P. and Carroll, R.J. (2003) Semiparametric regression. Cambridge University Press, Cambridge; NY.
33. Carter, S.L., Cibulskis, K., Helman, E., McKenna, A., Shen, H., Zack, T., Laird, P.W., Onofrio, R.C., Winckler, W., Weir, B.A. et al. (2012) Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol., 30, 413-421.
34. Houseman, E.A., Accomando, W.P., Koestler, D.C., Christensen, B.C., Marsit, C.J., Nelson, H.H., Wiencke, J.K. and Kelsey, K.T. (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics, 13, 86.
35. Zheng, X., Zhao, Q., Wu, H.J., Li, W., Wang, H., Meyer, C.A., Qin, Q.A., Xu, H., Zang, C., Jiang, P. et al. (2014) MethylPurify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes. Genome Biol., 15, 419.
36. Jaffe, A.E. and Irizarry, R.A. (2014) Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol., 15, R31.