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人类基因组DNA甲基化数据分析的研究现状

DOI: 10.1360/N052015-00009, PP. 450-459

Keywords: DNA甲基化,检测技术,预测方法,差异甲基化,CpG岛

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Abstract:

作为人类基因组最为典型的表观遗传现象,DNA甲基化在多种关键生理活动中扮演重要角色.系统分析基因组尺度的DNA甲基化概况意义重大.从CpG岛等基本定义出发,阐述了高通量DNA甲基化的检测技术以及针对芯片技术与下一代测序技术的低水平数据处理方法;重点对比了基于机器学习理论对CpG位点及CpG岛甲基化水平的预测算法,以及所利用的特征对预测效果的影响与发展趋势;并对DNA差异甲基化在组织特异性、癌症等多种疾病中的计算分析进行了全面的综述.

References

[1]  11 Laird P W. Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genetics, 2010, 11: 191-203
[2]  12 Bock C. Analysing and interpreting DNA methylation data. Nat Rev Genet, 2012, 13: 705-719
[3]  30 Keshet I, Schlesinger Y, Farkash S, et al. Evidence for an instructive mechanism of de novo methylation in cancer cells. Nat Genet, 2006, 38: 149-153
[4]  31 Rauch T A, Zhong X Y, Wu X W, et al. High-resolution mapping of DNA hypermethylation and hypomethylation in lung cancer. Proc Natl Acad Sci USA, 2008, 105: 252-257
[5]  32 Bibikova M, Lin Z W, Zhou L X, et al. High-throughput DNA methylation profiling using universal bead arrays. Genome Res, 2006, 16: 383-393
[6]  33 Bibikova M, Lin Z, Zhou L, et al. High-throughput DNA methylation profiling using universal bead arrays. Genome Res, 2006, 16: 383-393
[7]  34 Brunner A L, Johnson D S, Kim S W, et al. Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res, 2009, 19: 1044-1056
[8]  35 Oda M, Glass J L, Thompson R F, et al. High-resolution genome-wide cytosine methylation profiling with simultaneous copy number analysis and optimization for limited cell numbers. Nucleic Acids Res, 2009, 37: 3829-3839
[9]  36 Berman B P, Weisenberger D J, Laird P W. Locking in on the human methylome. Nat Biotechnol, 2009, 27: 341-342
[10]  37 Rauch T, Wang Z D, Zhang X M, et al. Homeobox gene methylation in lung cancer studied by genome-wide analysis with a microarray-based methylated CpG island recovery assay. Proc Natl Acad Sci USA, 2007, 104: 5527-5532
[11]  38 Meissner A, Gnirke A, Bell G W, et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res, 2005, 33: 5868-5877
[12]  39 Hodges E, Smith A D, Kendall J, et al. High definition profiling of mammalian DNA methylation by array capture and single molecule bisulfite sequencing. Genome Res, 2009, 19: 1593-1605
[13]  40 Lister R, Pelizzola M, Dowen R H, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature, 2009, 462: 315-322
[14]  41 Lokk K, Modhukur V, Rajashekar B, et al. DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns. Genome Biol, 2014, 15: r54
[15]  42 Fortin J P, Labbe A, Lemire M, et al. Functional normalization of 450K methylation array data improves replication in large cancer studies. Genome Biol, 2014, 15: 503
[16]  43 Dedeurwaerder S, Defrance M, Bizet M, et al. A comprehensive overview of Infinium HumanMethylation450 data processing. Brief Bioinform, 2014, 15: 929-941
[17]  44 Wang D, Yan L, Hu Q, et al. IMA: an R package for high-throughput analysis of Illumina''s 450K Infinium methylation data. Bioinformatics, 2012, 28: 729-730
[18]  45 Du P, Kibbe W A, Lin S M. lumi: a pipeline for processing Illumina microarray. Bioinformatics, 2008, 24: 1547-1548
[19]  46 Kilaru V, Barfield R T, Schroeder J W, et al. MethLAB: a graphical user interface package for the analysis of array-based DNA methylation data. Epigenetics, 2012, 7: 225-229
[20]  47 Maksimovic J, Gordon L, Oshlack A. SWAN: subset-quantile within array normalization for Illumina Infinium HumanMethylation450 BeadChips. Genome Biol, 2012, 13: R44
[21]  48 Morris T J, Butcher L M, Feber A, et al. ChAMP: 450K chip analysis methylation pipeline. Bioinformatics, 2014, 30: 428-430
[22]  49 Pidsley R, Wong C C Y, Volta M, et al. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics, 2013, 14: 293
[23]  50 Triche T J, Weisenberger D J, Van Den Berg D, et al. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res, 2013, 41: e90
[24]  51 Ball M P, Li J B, Gao Y, et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat Biotechnol, 2009, 27: 361-368
[25]  52 Meissner A, Mikkelsen T S, Gu H C, et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature, 2008, 454: 766-770
[26]  53 Frith M C, Mori R, Asai K. A mostly traditional approach improves alignment of bisulfite-converted DNA. Nucleic Acids Res, 2012, 40: e100
[27]  54 Chen P Y, Cokus S J, Pellegrini M. BS Seeker: precise mapping for bisulfite sequencing. BMC Bioinformatics, 2010, 11: 203
[28]  55 Guo W L, Fiziev P, Yan W H, et al. BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data. BMC Genomics, 2013, 14: 774
[29]  56 Krueger F, Andrews S R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 2011, 27: 1571-1572
[30]  57 Harris E Y, Ponts N, Le Roch K G, Lonardi S. BRAT-BW: efficient and accurate mapping of bisulfite-treated reads. Bioinformatics, 2012, 28: 1795-1796
[31]  58 Pedersen B, Hsieh T F, Ibarra C, et al. MethylCoder: software pipeline for bisulfite-treated sequences. Bioinformatics, 2011, 27: 2435-2436
[32]  59 Bhasin M, Zhang H, Reinherz E L, et al. Prediction of methylated CpGs in DNA sequences using a support vector machine. FEBS Lett, 2005, 579: 4302-4308
[33]  60 Stevens M, Cheng J B, Li D, et al. Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods. Genome Res, 2014, 23: 1541-1553
[34]  61 Feltus F A , Lee E K, Costello J F, et al. Predicting aberrant CpG island methylation. Proc Natl Acad Sci USA, 2003, 100: 12253-12258
[35]  62 Fang F, Fan S C, Zhang X G, et al. Predicting methylation status of CpG islands in the human brain. Bioinformatics, 2006, 22: 2204-2209
[36]  63 Cokus S J, Feng S, Zhang X, et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature, 2008, 452: 215-219
[37]  64 Bock C, Halachev K, Buch J, et al. EpiGRAPH: user-friendly software for statistical analysis and prediction of (epi)genomic data. Genome Biol, 2009, 10: R14
[38]  65 Fan S C, Zhang M Q, Zhang X G. Histone methylation marks play important roles in predicting the methylation status of CpG islands. Biochem Biophys Res Commun, 2008, 374: 559-564
[39]  66 凡时财, 邹见效, 徐红兵, 等. 人类基因组CpG岛甲基化概况的预测. 科学通报, 2010, 55: 1329-1334
[40]  67 Fan S, Fang F, Zhang X, et al. Putative zinc finger protein binding sites are over-represented in the boundaries of methylation-resistant CpG islands in the human genome. PLoS One, 2007, 2: e1184
[41]  68 Baylin S B, Herman J G, Graff J R, et al. Alterations in DNA methylation: a fundamental aspect of neoplasia. Adv Cancer Res, 1998, 72: 141-196
[42]  69 Yang Y, Nephew K, Kim S. A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters. BMC Bioinformatics, 2012, 13: S15
[43]  70 Das R, Dimitrova N, Xuan Z Y, et al. Computational prediction of methylation status in human genomic sequences. Proc Natl Acad Sci USA, 2006, 103: 10713-10716
[44]  1 Wyatt G R. Occurrence of 5-methylcytosine in nucleic acids. Nature, 1950, 166: 237-238
[45]  2 Reik W, Santos F, Dean W. Mammalian epigenomics: reprogramming the genome for development and therapy. Theriogenology, 2003, 59: 21-32
[46]  3 Barlow D P. Genomic imprinting: a mammalian epigenetic discovery model. Annu Rev Genet, 2011, 45, 45: 379-403
[47]  4 Meissner A. Epigenetic modifications in pluripotent and differentiated cells. Nat Biotechnol, 2010, 28: 1079-1088
[48]  5 Jones P A, Baylin S B. The fundamental role of epigenetic events in cancer. Nat Rev Genet, 2002, 3: 415-428
[49]  6 Feinberg A P. Phenotypic plasticity and the epigenetics of human disease. Nature, 2007, 447: 433-440
[50]  7 Eckhardt F, Lewin J, Cortese R, et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat Genet, 2006, 38: 1378-1385
[51]  8 Ziller M J, Gu H C, Muller F, et al. Charting a dynamic DNA methylation landscape of the human genome. Nature, 2013, 500: 477-481
[52]  9 Fan S C, Zhang X G. Progress of bioinformatics study in DNA methylation. Prog Biochem Biophys, 2009, 36: 143-150
[53]  10 Fouse S D, Nagarajan R P, Costello J F. Genome-scale DNA methylation analysis. Epigenomics, 2010, 2: 105-117
[54]  13 Ma X T, Wang Y W, Zhang M Q, et al. DNA methylation data analysis and its application to cancer research. Epigenomics, 2013, 5: 301-316
[55]  14 Wilhelm-Benartzi C S, Koestler D C, Karagas M R, et al. Review of processing and analysis methods for DNA methylation array data. Brit J Cancer, 2013, 109: 1394-1402
[56]  15 Bird A P. CpG islands as gene markers in the vertebrate nucleus. Trends Genet, 1987, 3: 342-347
[57]  16 Tykocinski M L, Max E E. CG dinucleotide clusters in MHC genes and in 5'' demethylated genes. Nucleic Acids Res, 1984, 12: 4385-4396
[58]  17 Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol, 1987, 196: 261-282
[59]  18 Takai D, Jones P A. Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc Natl Acad Sci USA, 2002, 99: 3740-3745
[60]  19 Ponger L, Mouchiroud D. CpGProD: identifying CpG islands associated with transcription start sites in large genomic mammalian sequences. Bioinformatics, 2002, 18: 631-633
[61]  20 Hackenberg M, Previti C, Luque-Escamilla P L, et al. CpGcluster: a distance-based algorithm for CpG-island detection. BMC Bioinformatics, 2006, 7: 446
[62]  21 Bock C, Walter J, Paulsen M, et al. CpG island mapping by epigenome prediction. PLoS Comput Biol, 2007, 3: e110
[63]  22 Su J Z, Zhang Y, Lv J, et al. CpG_MI: a novel approach for identifying functional CpG islands in mammalian genomes. Nucleic Acids Research, 2010, 38: e6
[64]  23 Chuang L Y, Yang C H, Lin M C, et al. CpGPAP: CpG island predictor analysis platform. BMC Genetics, 2012, 13: 13
[65]  24 Wu H, Caffo B, Jaffee H A, et al. Redefining CpG islands using hidden Markov models. Biostatistics, 2010, 11: 499-514
[66]  25 Hatada I, Hayashizaki Y, Hirotsune S, et al. A genomic scanning method for higher organisms using restriction sites as landmarks. Proc Natl Acad Sci USA, 1991, 88: 9523-9527
[67]  26 Ohgane J, Aikawa J I, Ogura A, et al. Analysis of CpG islands of trophoblast giant cells by restriction landmark genomic scanning. Dev Genet, 1998, 22: 132-140
[68]  27 Toyota M, Ho C, Ahuja N, et al. Identification of differentially methylated sequences in colorectal cancer by methylated CpG island amplification. Cancer Res, 1999, 59: 2307-2312
[69]  28 Khulan B, Thompson R F, Ye K, et al. Comparative isoschizomer profiling of cytosine methylation: the HELP assay. Genome Res, 2006, 16: 1046-1055
[70]  29 Weber M, Hellmann I, Stadler M B, et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet, 2007, 39: 457-466
[71]  71 Zheng X Q, Zhao Q, Wu H J, et al. MethylPurify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes. Genome Biol, 2014, 15: 419
[72]  72 Bird A. DNA methylation patterns and epigenetic memory. Genes Dev, 2002, 16: 6-21
[73]  73 VanderKraats N D, Hiken J F, Decker K F, et al. Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes. Nucleic Acids Res, 2013, 41: 6816-6827
[74]  74 Su J Z, Yan H D, Wei Y J, et al. CpG_MPs: identification of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data. Nucleic Acids Res, 2013, 41: e41
[75]  75 Byun H M, Siegmund K D, Pan F, et al. Epigenetic profiling of somatic tissues from human autopsy specimens identifies tissue- and individual-specific DNA methylation patterns. Hum Mol Genet, 2009, 18: 4808-4817
[76]  76 Nagae G, Isagawa T, Shiraki N, et al. Tissue-specific demethylation in CpG-poor promoters during cellular differentiation. Hum Mol Genet, 2011, 20: 2710-2721
[77]  77 Fernandez A F, Assenov Y, Martin-Subero J I, et al. A DNA methylation fingerprint of 1628 human samples. Genome Res, 2012, 22: 407-419
[78]  78 Illingworth R S, Bird A P. CpG islands—‘A rough guide’. FEBS Lett, 2009, 583: 1713-1720
[79]  79 Zhang Y, Liu H B, Lv J, et al. QDMR: a quantitative method for identification of differentially methylated regions by entropy. Nucleic Acids Res, 2011, 39: e58
[80]  80 Li D, Zhang B, Xing X, et al. Combining MeDIP-seq and MRE-seq to investigate genome-wide CpG methylation. Methods, 2015, 72: 29-40
[81]  81 Jones P A, Baylin S B. The epigenomics of cancer. Cell, 2007, 128: 683-692
[82]  82 Esteller M. Molecular origins of cancer: epigenetics in cancer. New Engl J Med, 2008, 358: 1148-1159
[83]  83 Turcan S, Rohle D, Goenka A, et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature, 2012, 483: 479-483
[84]  84 Desplats P, Spencer B, Coffee E, et al. a-synuclein sequesters Dnmt1 from the nucleus: a novel mechanism for epigenetic alterations in Lewy body diseases. J Biol Chem, 2011, 286: 9031-9037
[85]  85 Javierre B M, Fernandez A F, Richter J, et al. Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus. Genome Res, 2010, 20: 170-179
[86]  86 Nakano K, Whitaker J W, Boyle D L, et al. DNA methylome signature in rheumatoid arthritis. Ann Rheum Dis, 2013, 72: 110-117
[87]  87 Assenov Y, Muller F, Lutsik P, et al. Comprehensive analysis of DNA methylation data with RnBeads. Nat Methods, 2014, 11: 1138-1140
[88]  88 Akalin A, Kormaksson M, Li S, et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol, 2012, 13: R87

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