MBRidge: An accurate and cost-effective method for profiling DNA methylome at single-base resolution

Journal of Molecular Cell Biology

Volumn 7, Issue 4, 2015, Pages 299-313

  Cai, Wanshi ^a,b   Mao, Fengbiao ^a,b   Teng, Huajing ^a,c   Cai, Tao ^d   Zhao, Fangqing ^a   Wu, Jinyu ^a,e   Sun, Zhong Sheng ^a,e  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c INSTITUTE OF ZOOLOGY   (China)

^d NATIONAL INSTITUTE OF DENTAL AND CRANIOFACIAL RESEARCH   (United States)

^e WENZHOU MEDICAL UNIVERSITY   (China)

Author keywords

DNA methylome; MB seq; ridge regression; single base resolution

Indexed keywords

LONG UNTRANSLATED RNA; MESSENGER RNA;

ARTICLE; CALIBRATION; CARCINOGENESIS; CONTROLLED STUDY; COST EFFECTIVENESS ANALYSIS; CPG ISLAND; DNA FINGERPRINTING; DNA METHYLATION; DNA SEQUENCE; FALSE POSITIVE RESULT; GENE EXPRESSION REGULATION; GENETIC ASSOCIATION; GENETIC PROCEDURES; GENOME ANALYSIS; HUMAN; HUMAN CELL; MBRIDGE; MEASUREMENT ACCURACY; OVARIAN CANCER CELL LINE; PROMOTER REGION; REGRESSION ANALYSIS; REPRODUCIBILITY; RIDGE REGRESSION; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS; T29 CELL LINE; T29H CELL LINE; TRANSCRIPTION REGULATION; BIOLOGICAL MODEL; COST BENEFIT ANALYSIS; ECONOMICS; FEMALE; GENETICS; NUCLEOTIDE SEQUENCE; OVARY TUMOR; PROCEDURES; SOFTWARE; TUMOR CELL LINE;

BASE SEQUENCE; CALIBRATION; CELL LINE, TUMOR; COST-BENEFIT ANALYSIS; DNA METHYLATION; FEMALE; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; MODELS, BIOLOGICAL; OVARIAN NEOPLASMS; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, DNA; SOFTWARE;

EID: 84939644014     PISSN: 16742788     EISSN: 17594685     Source Type: Journal    
DOI: 10.1093/jmcb/mjv037     Document Type: Article

References (62)

1. Ball, M.P., Li, J.B., Gao, Y., et al. (2009). Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat. Biotechnol. 27, 361-368.
2. Baylin, S.B., and Jones, P.A. (2011). A decade of exploring the cancer epigenome-biological and translational implications. Nat. Rev. Cancer 11, 726-734.
3. Beck, S. (2010). Taking the measure of the methylome. Nat. Biotechnol. 28, 1026-1028.
4. Bestor, T.H. (1998). The host defence function of genomic methylation patterns. Novartis Found. Symp. 214, 187-195; discussion 195-199, 228-232.
5. Bock, C. (2012). Analysing and interpreting DNA methylation data. Nat. Rev. Genet. 13, 705-719.
6. Bock, C., Tomazou, E.M., Brinkman, A.B., et al. (2010). Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat. Biotechnol. 28, 1106-1114.
7. Brenet, F., Moh, M., Funk, P., et al. (2011). DNA methylation of the first exon is tightly linked to transcriptional silencing. PLoS One 6,e14524.
8. Chai, X., Nagarajan, S., Kim, K., et al. (2013). Regulation of the boundaries of accessible chromatin. PLoS Genet. 9,e1003778.
9. Chavez, L., Jozefczuk, J., Grimm, C., et al. (2010). Computational analysis of genome-wide DNA methylation during the differentiation of human embryonic stem cells along the endodermal lineage. Genome Res. 20, 1441-1450.
10. Christensen, B.C., Houseman, E.A., Marsit, C.J., et al. (2009). Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 5, e1000602.
11. Cokus, S.J., Feng, S., Zhang, X., et al. (2008). Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452, 215-219.
12. Derrien, T., Johnson, R., Bussotti, G., et al. (2012). The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression. Genome Res. 22, 1775-1789.
13. Down, T.A., Rakyan, V.K., Turner, D.J., etal. (2008). A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat. Biotechnol. 26, 779-785.
14. Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1-22.
15. Harris, R.A., Wang, T., Coarfa, C., et al. (2010). Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat. Biotechnol. 28, 1097-1105.
16. Hoerl, A.E., and Kennard, R.W. (2000). Ridge regression: biased estimation for nonorthogonal problems. Technometrics 42, 80-86.
17. Huang, Y., Pastor, W.A., Shen, Y., et al. (2010). The behaviour of 5-hydroxymethylcytosine in bisulfite sequencing. PLoS One 5, e8888.
18. Irizarry, R.A., Ladd-Acosta, C., Wen, B., 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.
19. Jiang, L., Zhang, J., Wang, J.J., et al. (2013). Sperm, but not oocyte, DNA methylome is inherited by zebrafish early embryos. Cell 153, 773-784.
20. Jin, S.G., Kadam, S., and Pfeifer, G.P. (2010). Examination of the specificity of DNA methylation profiling techniques towards 5-methylcytosine and 5-hydroxymethylcytosine. Nucleic Acids Res. 38, e125.
21. Jones, P.A. (2012). Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13, 484-492.
22. Karnoub, A.E., and Weinberg, R.A. (2008). Ras oncogenes: split personalities. Nat. Rev. Mol. Cell Biol. 9, 517-531.
23. Kochanek, S., Renz, D., and Doerfler, W. (1993). DNA methylation in the Alu sequences of diploid and haploid primary humancells. EMBO J. 12, 1141-1151.
24. Kunde-Ramamoorthy, G., Coarfa, C., Laritsky, E., et al. (2014). Comparison and quantitative verification of mapping algorithms for whole-genome bisulfite sequencing. Nucleic Acids Res. 42, e43.
25. Laird, P.W. (2010). Principles and challenges of genomewide DNA methylation analysis. Nat. Rev. Genet. 11, 191-203.
26. Lan, X., Adams, C., Landers, M., et al. (2011). High resolution detection and analysis of CpG dinucleotides methylation using MBD-Seq technology. PLoS One 6, e22226.
27. Li, N., Ye, M., Li, Y., et al. (2010a). Whole genome DNA methylation analysis based on high throughput sequencing technology. Methods 52, 203-212.
28. Li, Y.R., Zhu, J.D., Tian, G., et al. (2010b). The DNA methylome of human peripheral blood mononuclear cells. PLoS Biol. 8,e1000533.
29. Lister, R., and Ecker, J.R. (2009). Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res. 19, 959-966.
30. Lister, R., Pelizzola, M., Dowen, R.H., et al. (2009). Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315-322.
31. Lister, R., Mukamel, E.A., Nery, J.R., et al. (2013). Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905.
32. Liu, J., Yang, G., Thompson-Lanza, J.A., et al. (2004). A genetically defined model for human ovarian cancer. Cancer Res. 64, 1655-1663.
33. Maunakea, A.K., Nagarajan, R.P., Bilenky, M., et al. (2010). Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466, 253-257.
34. McLean, C.Y., Bristor, D., Hiller, M., et al. (2010). GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495-501.
35. Meissner, A., Mikkelsen, T.S., Gu, H., et al. (2008). Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766-770.
36. Nair, S.S., Coolen, M.W., Stirzaker, C., et al. (2011). Comparison of methyl-DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain (MBD) protein capture for genome-wide DNA methylation analysis reveal CpG sequence coverage bias. Epigenetics 6, 34-44.
37. Nichol, K., and Pearson, C.E. (2002). CpG methylation modifies the genetic stability of cloned repeat sequences. Genome Res. 12, 1246-1256.
38. Pelizzola, M., Koga, Y., Urban, A.E., et al. (2008). MEDME: An experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment. Genome Res. 18, 1652-1659.
39. Pomraning, K.R., Smith, K.M., and Freitag, M. (2009). Genome-wide high throughput analysis of DNA methylation in eukaryotes. Methods 47, 142-150.
40. Riebler, A., Menigatti, M., Song, J.Z., et al. (2014). BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach. Genome Biol. 15, R35.
41. Robertson, K.D. (2005). DNA methylation and human disease. Nat. Rev. Genet. 6, 597-610.
42. Robertson, K.D., and Wolffe, A.P. (2000). DNA methylation in health and disease. Nat. Rev. Genet. 1, 11-19.
43. Robinson, M.D., McCarthy, D.J., and Smyth, G.K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140.
44. Sati, S., Ghosh, S., Jain, V., et al. (2012). Genome-wide analysis reveals distinct patterns of epigenetic features in long non-coding RNA loci. Nucleic Acids Res. 40, 10018-10031.
45. Satterlee, J.S., Schubeler, D., and Ng, H.H. (2010). Tackling the epigenome: challenges and opportunities for collaboration. Nat. Biotechnol. 28, 1039-1044.
46. Schmid, C.W. (1998). Does SINE evolution preclude Alu function? Nucleic Acids Res. 26, 4541-4550.
47. Serre, D., Lee, B.H., and Ting, A.H. (2010). MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome. Nucleic Acids Res. 38, 391-399.
48. Stevens, M., Cheng, J.B., Li, D.F., et al. (2013). Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods. Genome Res. 23, 1541-1553.
49. Takayama, S., Dhahbi, J., Roberts, A., et al. (2014). Genome methylation in D. melanogaster is found at specific short motifs and is independent of DNMT2 activity. Genome Res. 24, 821-830.
50. Wang, L., Sun, J., Wu, H., et al. (2012). Systematic assessment of reduced representation bisulfite sequencing to human blood samples: a promising method for large-sample-scale epigenomic studies. J. Biotechnol. 157, 1-6.
51. Wang, T., Liu, Q., Li, X.F., et al. (2013). RRBS-analyser: a comprehensive web server for reduced representation bisulfite sequencing data analysis. Hum. Mutat. 34, 1606-1610.
52. Wang, X., Fang, X., Yang, P., et al. (2014a). The locust genome provides insight into swarm formation and long-distance flight. Nat. Commun. 5, 2957.
53. Wang, Y., Li, G.L., Mao, F.B., et al. (2014b). Ras-induced epigenetic inactivation of the RRAD (Ras-related associated with diabetes) gene promotes glucose uptake in a human ovarian cancer model. J. Biol. Chem. 289, 14225-14238.
54. Weber, M., Davies, J.J., Wittig, D., et al. (2005). Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat. Genet. 37, 853-862.
55. Weisenberger, D.J. (2014). Characterizing DNA methylation alterations from The Cancer Genome Atlas. J. Clin. Invest. 124, 17-23.
56. White, N.M., Cabanski, C.R., Silva-Fisher, J.M., et al. (2014). Transcriptome sequencing reveals altered long intergenic non-coding RNAs in lung cancer. Genome Biol. 15, 429.
57. Xiang, H., Zhu, J.D., Chen, Q.A., et al. (2010). Single base-resolution methylome of the silkworm reveals a sparse epigenomic map. Nat. Biotechnol. 28, 516-520.
58. Xie, W., Barr, C.L., Kim, A., et al. (2012). Base-resolution analyses of sequence and parent-of-origin dependent DNA methylation in the mouse genome. Cell 148, 816-831.
59. Xie, C.Y., Yuan, J., Li, H., et al. (2014). NONCODEv4: exploring the world of long non-coding RNA genes. Nucleic Acids Res. 42,D98-D103.
60. Young, T., Mei, F., Liu, J., et al. (2005). Proteomics analysis of H-RAS-mediated oncogenic transformation in a genetically defined human ovarian cancer model. Oncogene 24, 6174-6184.
61. Zheng, Z., Advani, A., Melefors, O., et al. (2011). Titration-free 454 sequencing using Y adapters. Nat. Protoc. 6, 1367-1376.
62. Ziller, M.J., Gu, H., Muller, F., et al. (2013). Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477-481.