Base resolution methylome profiling: Considerations in platform selection, data preprocessing and analysis

Epigenomics

Volumn 7, Issue 5, 2015, Pages 813-828

  Sun, Zhifu ^a   Cunningham, Julie ^a   Slager, Susan ^a   Kocher, Jean Pierre ^a  

^a MAYO CLINIC   (United States)

Author keywords

bisulfite sequencing; differential methylation; DNA methylation; methylation 450K array; normalization; reduced representation bisulfite sequencing; study design

Indexed keywords

BISULFITE; DNA; HYDROGEN SULFITE; SULFITE;

ALGORITHM; BISULFITE SEQUENCING; COMPARATIVE STUDY; CPG ISLAND; DNA METHYLATION; EPIGENETICS; GENE CONTROL; GENE EXPRESSION; GENETIC TRANSCRIPTION; GENOTYPE; HUMAN; MICROARRAY ANALYSIS; NEXT GENERATION SEQUENCING; PHENOTYPE; PRIORITY JOURNAL; PROMOTER REGION; QUALITY CONTROL; REVIEW; BIOLOGICAL MODEL; CHEMISTRY; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN GENOME; PROCEDURES;

ALGORITHMS; CPG ISLANDS; DNA; DNA METHYLATION; EPIGENOMICS; GENOME, HUMAN; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MODELS, GENETIC; SULFITES;

EID: 84945206699     PISSN: 17501911     EISSN: 1750192X     Source Type: Journal    
DOI: 10.2217/epi.15.21     Document Type: Review

References (89)

1. Azuara D, Rodriguez-Moranta F, De Oca J, et al. Novel methylation panel for the early detection of colorectal tumors in stool DNA. Clin. Colorectal Cancer 9(3), 168-176 (2010
2. Hong L, Ahuja N. DNA methylation biomarkers of stool, and blood for early detection of colon cancer. Genet. Test. Mol. Biomarkers 17(5), 401-406 (2013
3. Imperiale TF, Ransohoff DF, Itzkowitz SH, et al. Multitarget stool DNA testing for colorectal-cancer screening. N. Engl. J. Med. 370(14), 1287-1297 (2014
4. Shiovitz S, Bertagnolli MM, Renfro LA, et al. CpG island methylator phenotype is associated with response to adjuvant irinotecan-based therapy for stage iii colon cancer. Gastroenterology 147(3), 637-645 (2014
5. Fleischhacker M, Dietrich D, Liebenberg V, Field JK, Schmidt B. The role of DNA methylation as biomarkers in the clinical management of lung cancer. Expert Rev. Respir. Med. 7(4), 363-383 (2013
6. Bock C, Tomazou EM, Brinkman AB, et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat. Biotechnol. 28(10), 1106-1114 (2010
7. Ruike Y, Imanaka Y, Sato F, Shimizu K, Tsujimoto G. Genome-wide analysis of aberrant methylation in human breast cancer cells using methyl-DNA immunoprecipitation combined with high-throughput sequencing. BMC Genomics 11, 137 (2010
8. Aberg KA, Mcclay JL, Nerella S, et al. Mbd-seq as a cost-effective approach for methylome-wide association studies: demonstration in 1500 case-control samples. Epigenomics 4(6), 605-621 (2012
9. Serre D, Lee BH, Ting AH. Mbd-isolated genome sequencing provides a high-throughput, and comprehensive survey of DNA methylation in the human genome. Nucleic Acids Res. 38(2), 391-399 (2010
10. Harris RA, Wang T, Coarfa C, et al. Comparison of sequencing-based methods to profile DNA methylation, and identification of monoallelic epigenetic modifications. Nat. Biotechnol. 28(10), 1097-1105 (2010
11. Wang L, Sun J, Wu H, et al. Systematic assessment of reduced representation bisulfite sequencing to human blood samples: a promising method for large-sample-scale epigenomic studies. J. Biotechnol. 157(1), 1-6 (2012
12. Dedeurwaerder S, Defrance M, Calonne E, Denis H, Sotiriou C, Fuks F. Evaluation of the infinium methylation 450k technology. Epigenomics 3(6), 771-784 (2011
13. Sandoval J, Heyn H, Moran S, et al. Validation of a DNA methylation microarray for 450, 000 CpG sites in the human genome. Epigenetics 6(6), 692-702 (2011
14. Sun Z, Wu Y, Ordog T, et al. Aberrant signature methylome by DNMT1 hot spot mutation in hereditary sensory, and autonomic neuropathy 1E. Epigenetics 9(8), 1184-1193 (2014
15. Gu H, Smith ZD, Bock C, Boyle P, Gnirke A, Meissner A. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 6(4), 468-481 (2011
16. Agilent Technologies: SureSelect Methyl-Seq. www.genomics.agilent.com
17. NimbleGen. www.nimblegen.com
18. Walker DL, Bhagwate AV, Baheti S, et al. DNA methylation profiling: comparison of genome-wide sequencing methods, and the Infinium Human Methylation 450 Bead Chip. Epigenomics doi: 10.2217/epi.15.64 (2015) (Epub ahead of print
19. Bibikova M, Barnes B, Tsan C, et al. High density DNA methylation array with single CpG site resolution. Genomics 98(4), 288-295 (2011
20. Wang D, Yan L, Hu Q, et al. Ima: an R package for highthroughput analysis of illumina's 450k infinium methylation data. Bioinformatics 28(5), 729-730 (2012
21. Touleimat N, Tost J. Complete pipeline for infinium® human methylation 450k beadchip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics 4(3), 325-341 (2012
22. Maksimovic J, Gordon L, Oshlack A. Swan: subsetquantile within array normalization for Illumina Infinium HumanMethylation450 Beadchips. Genome Biol. 13(6), R44 (2012
23. Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible, and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30(10), 1363-1369 (2014
24. Teschendorff AE, Marabita F, Lechner M, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29(2), 189-196 (2013
25. Davis S DP, Bilke S, Triche T Jr, Bootwalla M. Methylumi: handle Illumina methylation data. R package version 2.12.0 (2014
26. Du P, Kibbe WA, Lin SM. Lumi: a pipeline for processing Illumina microarray. Bioinformatics 24(13), 1547-1548 (2008
27. Pidsley R, Cc YW, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450k Methylation array data. BMC Genomics 14, 293 (2013
28. Marabita F, Almgren M, Lindholm ME, et al. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 beadchip platform. Epigenetics 8(3), 333-346 (2013
29. Sun Z, Chai HS, Wu Y, et al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform. BMC Med. Genomics 4, 84 (2011
30. Wu MC, Joubert BR, Kuan PF, et al. A systematic assessment of normalization approaches for the Infinium 450k Methylation platform. Epigenetics 9(2), 318-329 (2014
31. Dedeurwaerder S, Defrance M, Bizet M, Calonne E, Bontempi G, Fuks F. A comprehensive overview of Infinium HumanMethylation450 data processing. Brief. Bioinform. 15(6), 929-941 (2014
32. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8(1), 118-127 (2007
33. Huss M. Introduction into the analysis of high-throughputsequencing based epigenome data. Brief. Bioinform. 11(5), 512-523 (2010
34. Reinders J, Paszkowski J. Bisulfite methylation profiling of large genomes. Epigenomics 2(2), 209-220 (2010
35. Krueger F, Kreck B, Franke A, and rews SR. DNA methylome analysis using short bisulfite sequencing data. Nat. Methods 9(2), 145-151 (2012
36. Krueger F, and rews SR. Bismark: a flexible aligner, and methylation caller for bisulfite-seq applications. Bioinformatics 27(11), 1571-1572 (2011
37. Guo W, Fiziev P, Yan W, et al. BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data. BMC Genomics 14, 774 (2013
38. Harris EY, Ponts N, Le Roch KG, Lonardi S. Brat-bw: efficient, and accurate mapping of bisulfite-treated reads. Bioinformatics 28(13), 1795-1796 (2012
39. Kielbasa SM, Wan R, Sato K, Horton P, Frith MC. Adaptive seeds tame genomic sequence comparison. Genome Res. 21(3), 487-493 (2011
40. Pedersen B, Hsieh TF, Ibarra C, Fischer RL. Methylcoder: software pipeline for bisulfite-treated sequences. Bioinformatics 27(17), 2435-2436 (2011
41. Xi Y, Li W. BSMAP: whole-genome bisulfite sequence mapping program. BMC Bioinformatics 10, 232 (2009
42. Xi Y, Bock C, Muller F, Sun D, Meissner A, Li W. RRBSMAP: a fast, accurate, and user-friendly alignment tool for reduced representation bisulfite sequencing. Bioinformatics 28(3), 430-432 (2012
43. Babraham Bioinformatics: Trim Galore! www.bioinformatics.babraham.ac.uk
44. Mayo Clinic: SAAP-RRBS. http://bioinformaticstools.mayo.edu
45. Lin X, Sun D, Rodriguez B, et al. BSeQC: quality control of bisulfite sequencing experiments. Bioinformatics 29(24), 3227-3229 (2013
46. Wilhelm-Benartzi CS, Koestler DC, Karagas MR, et al. Review of processing, and analysis methods for DNA methylation array data. Br. J. Cancer 109(6), 1394-1402 (2013
47. Babraham Bioinformatics: FastQC. www.bioinformatics.babraham.ac.uk
48. Sun Z, Baheti S, Middha S, et al. SAAP-RRBS: streamlined analysis, and annotation pipeline for reduced representation bisulfite sequencing. Bioinformatics 28(16), 2180-2181 (2012
49. Sun S, Noviski A, Yu X. MethyQA: a pipeline for bisulfitetreated methylation sequencing quality assessment. BMC Bioinformatics 14, 259 (2013
50. Wang X, Laird PW, Hinoue T, Groshen S, Siegmund KD. Non-specific filtering of beta-distributed data. BMC Bioinformatics 15(1), 199 (2014
51. Du P, Zhang X, Huang CC, et al. Comparison of beta-value, and m-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11, 587 (2010
52. Zhuang J, Widschwendter M, Teschendorff AE. A comparison of feature selection, and classification methods in DNA methylation studies using the illumina infinium platform. BMC Bioinformatics 13, 59 (2012
53. Smyth GK. Linear models, and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004
54. Barfield RT, Kilaru V, Smith AK, Conneely KN. CpGassoc: an R function for analysis of DNA methylation microarray data. Bioinformatics 28(9), 1280-1281 (2012
55. Warden CD, Lee H, Tompkins JD, et al. COHCAP: an integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis. Nucleic Acids Res. 41(11), e117 (2013
56. Saadati M, Benner A. Statistical challenges of highdimensional methylation data. Stat. Med. 33(30), 5347-5357 (2014
57. Dolzhenko E, Smith AD. Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments. BMC Bioinformatics 15(1), 215 (2014
58. Lister R, Pelizzola M, Dowen RH, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462(7271), 315-322 (2009
59. Li Y, Zhu J, Tian G, et al. The DNA methylome of human peripheral blood mononuclear cells. PLoS Biol. 8(11), e1000533 (2010
60. Challen GA, Sun D, Jeong M, et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat. Genet. 44(1), 23-31 (2012
61. Akalin A, Kormaksson M, Li S, et al. Methylkit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13(10), R87 (2012
62. Li S, Garrett-Bakelman FE, Akalin A, et al. An optimized algorithm for detecting, and annotating regional differential methylation. BMC Bioinformatics 14(Suppl. 5), S10 (2013
63. Xu H, Podolsky RH, Ryu D, et al. A method to detect differentially methylated loci with next-generation sequencing. Genet. Epidemiol. 37(4), 377-382 (2013
64. Park Y, Figueroa ME, Rozek LS, Sartor MA. MethylSig: a whole-genome DNA methylation analysis pipeline. Bioinformatics 30(17), 2414-2422 (2014
65. Sun D, Xi Y, Rodriguez B, et al. MOABS: model based analysis of bisulfite sequencing data. Genome Biol. 15(2), R38 (2014
66. Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole-genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13(10), R83 (2012
67. Hebestreit K, Dugas M, Klein HU. Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29(13), 1647-1653 (2013
68. Jaffe AE, Murakami P, Lee H, et al. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int. J. Epidemiol. 41(1), 200-209 (2012
69. Saito Y, Tsuji J, Mituyama T. Bisulfighter: accurate detection of methylated cytosines, and differentially methylated regions. Nucleic Acids Res. 42(6), e45 (2014
70. Song Q, Decato B, Hong EE, et al. A reference methylome database, and analysis pipeline to facilitate integrative, and comparative epigenomics. PLoS ONE 8(12), e81148 (2013
71. Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping, and correcting spatially correlated p-values. Bioinformatics 28(22), 2986-2988 (2012
72. Feber A, Guilhamon P, Lechner M, et al. Using high-density DNA methylation arrays to profile copy number alterations. Genome Biol. 15(2), R30 (2014
73. Barturen G, Rueda A, Oliver JL, Hackenberg M. MethylExtract: high-quality methylation maps, and SNV calling from whole-genome bisulfite sequencing data. F1000Res 2, 217 (2013
74. Liu Y, Siegmund KD, Laird PW, Berman BP. Bis-snp: combined DNA methylation, and SNP calling for bisulfiteseq data. Genome Biol. 13(7), R61 (2012
75. Fang F, Hodges E, Molaro A, Dean M, Hannon GJ, Smith AD. Genomic landscape of human allele-specific DNA methylation. Proc. Natl Acad. Sci. USA 109(19), 7332-7337 (2012
76. Mosen-Ansorena D, Telleria N, Veganzones S, De La Orden V, Maestro ML, Aransay AM. SeqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing. BMC Genomics 15, 178 (2014
77. Chan KC, Jiang P, Chan CW, et al. Noninvasive detection of cancer-associated genome-wide hypomethylation, and copy number aberrations by plasma DNA bisulfite sequencing. Proc. Natl Acad. Sci. USA 110(47), 18761-18768 (2013
78. Guo JU, Su Y, Shin JH, et al. Distribution, recognition, and regulation of non-CpG methylation in the adult mammalian brain. Nat. Neurosci. 17(2), 215-222 (2014
79. Pinney SE. Mammalian non-CpG methylation: stem cells, and beyond. Biology (Basel) 3(4), 739-751 (2014
80. Price ME, Cotton AM, Lam LL, et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 beadchip array. Epigenetics Chromatin 6(1), 4 (2013
81. Wang J, Tang J, Lai M, Zhang H. 5-hydroxymethylcytosine, and disease. Mutat. Res. Rev. Mutat. Res. 762, 167-175 (2014
82. Yu M, Hon GC, Szulwach KE, et al. Tet-assisted bisulfite sequencing of 5-hydroxymethylcytosine. Nat. Protoc. 7(12), 2159-2170 (2012
83. Booth MJ, Ost TW, Beraldi D, et al. Oxidative bisulfite sequencing of 5-methylcytosine, and 5-hydroxymethylcytosine. Nat. Protoc. 8(10), 1841-1851 (2013
84. Harper KN, Peters BA, Gamble MV. Batch effects, and pathway analysis: two potential perils in cancer studies involving DNA methylation array analysis. Cancer Epidemiol. Biomarkers Prev. 22(6), 1052-1060 (2013
85. Buhule OD, Minster RL, Hawley NL, et al. Stratified randomization controls better for batch effects in 450k methylation analysis: a cautionary tale. Front. Genet. 5, 354 (2014
86. Hovestadt V, Remke M, Kool M, et al. Robust molecular subgrouping, and copy-number profiling of medulloblastoma from small amounts of archival tumour material using highdensity DNA methylation arrays. Acta Neuropathol. 125(6), 913-916 (2013
87. Moran S, Vizoso M, Martinez-Cardus A, et al. Validation of DNA methylation profiling in formalinfixed paraffin-embedded samples using the Infinium HumanMethylation450 microarray. Epigenetics 9(6), 829-833 (2014
88. Dumenil TD, Wockner LF, Bettington M, et al. Genomewide DNA methylation analysis of formalin-fixed paraffin embedded colorectal cancer tissue. Genes Chromosomes Cancer 53(7), 537-548 (2014
89. Wang J, Xia Y, Li L, et al. Double restriction-enzyme digestion improves the coverage, and accuracy of genomewide CpG methylation profiling by reduced representation bisulfite sequencing. BMC Genomics 14, 11 (2013