Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data

BMC Bioinformatics

Volumn 18, Issue 1, 2017, Pages

  Ma, Xiaoke ^a   Liu, Zaiyi ^b   Zhang, Zhongyuan ^c   Huang, Xiaotai ^a   Tang, Wanxin ^d  

^a XIDIAN UNIVERSITY   (China)

^b GUANGDONG PROVINCIAL PEOPLE'S HOSPITAL   (China)

^c CENTRAL UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^d SICHUAN UNIVERSITY   (China)

Author keywords

Epigenetic module; Methylation; Multiple networks; Network biology

Indexed keywords

ALKYLATION; BIOINFORMATICS; DISEASES; DNA; GENES; METHYLATION; PATIENT TREATMENT;

DIFFERENTIAL NETWORK; EPIGENETIC MECHANISMS; EPIGENETIC MODULE; GENE EXPRESSION DATA; INTEGRATIVE ANALYSIS; MODEL AND ALGORITHMS; MULTIPLE NETWORKS; NETWORK BIOLOGY;

GENE EXPRESSION;

TRANSCRIPTOME;

ALGORITHM; BREAST TUMOR; DNA METHYLATION; FEMALE; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETIC EPIGENESIS; GENETICS; GENOMICS; HUMAN; METABOLISM;

ALGORITHMS; BREAST NEOPLASMS; DNA METHYLATION; EPIGENESIS, GENETIC; FEMALE; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; GENOMICS; HUMANS; TRANSCRIPTOME;

EID: 85011067014     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-017-1490-6     Document Type: Article

References (42)

1. Deaton AM, Bird A. Cpg islands and the regulation of transcription. Genes Dev. 2011; 25:1010-22.
2. Suzuki MM, Bird A. Dna methylation landscapes: provocative insights from epigenomics. Nat Rev Genet. 2008; 9:465-76.
3. Ziller MJ, Gu H, Muller F, Donaghey J, et al.Charting a dynamic dna methylation landscape of the human genome. Nature. 7463; 500:477-81.
4. Bird AP, Wolffe AP. Methylation-induced repression-belts, braces, and chromatin. Cell. 1999; 99:451-4.
5. Teschendorff AE, Jones A, Fiegl H, Sargent A, et al.Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation. Genome Med. 2012; 4(3):24.
6. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011; 12(8):529-41.
7. Meissner A, Mikkelsen TS, Gu H, Hanna J, Sivachenko A, et al.Genome-scale dna methylation maps of pluripotent and differentiated cells. Cell. 2008; 454:766-70.
8. Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, et al.Shotgun bisulphite sequencing of the arabidopsis genome reveals dna methylation patterning. Nature. 2008; 452:215-9.
9. Lister R, O'Malley RC, Tonti-Filippini J, Gregory BD, et al.Highly integrated single-base resolution maps of the epigenome in arabidopsis. Cell. 2008; 133:523-6.
10. Ball MP, Li JB, Gao Y, Lee JH, et al.Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat Biotechnol. 2009; 27:361-8.
11. Fleischer T, Frigessi A, Johnson KC, et al.Genome-wide dna methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis. Genome Biol. 2014; 15:435.
12. Hinoue T, Weisenberger DJ, Lange CP, et al.Genome-scale analysis of aberrant dna methylation in colorectal cancer. Genome Res. 2012; 22:271-82.
13. Yang X, Shao X, Gao L, Zhang S. Systematic dna methylation analysis of multiple cell lines reveals common and specific patterns within and across tissues of origin. Hum Mol Genet. 2015; 24:4374-84.
14. Varley K, Gertz J, Bowling K, et al.Dynamic dna methylation across diverse human cell lines and tissues. Genome Res. 2013; 23:555-67.
15. Gavaert O, Tibshirani R, Plevritis S. Pancancer analysis of dna methylation-driven genes using methylmix. Genome Biol. 2015; 16:17.
16. Witte T, Plass C, Gerhauser C. Pan-cancer patterns of dna methylation. Genome Med. 2014; 6:66.
17. Jones A, Teschendorff AE, Li Q, et al.Role of dna methylation and epigenetic silencing of hand2 in endometrial cancer development. PLoS Med. 2013; 10(11):1001551.
18. Kandoth C, Schultz N, Cherniack AD, Akbani R, et al.Integrated genomic characterization of endometrial carcinoma. Nature. 2013; 497(7447):67-73.
19. West J, Beck S, Wang X, Teschendorff AE. An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways. Sci Rep. 2013; 3:1630.
20. Jiao Y, Widschwendter M, Teschendorff AE. A systems-level integrative framework for genome-wide dna methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics. 2014; 30(16):2360-66.
21. Ma X, Gao L, Tan K. Modeling disease progression using dynamics of pathway connectivity. Bioinformatics. 2014; 30(16):2343-50.
22. Cantini L, Medico E, Fortunato S, Caselle M. Detection of gene communities in multi-networks reveals cancer drivers. Sci Rep. 2015; 5:17386.
23. Didier G, Brun C, Baudot A. Identifying communities from multiplex biological networks. Peer J. 2015; 3:1525.
24. Watson-Haigh NS, Kadarmideen HN, Reverter A. Pcit: an r package for weighted gene co-expression networks based on partial correlation and information theory approaches. Bioinformatics. 2010; 26:411-3.
25. Ritchie ME, Phipson B, Wu D, Hu Y, et al.limma powers differential expression analyses for rna-sequencing and microarray studies. Nucleic Acids Res. 2015; 43(7):47.
26. Benjamini Y, Hochberg Y. Controlling the false discovery rateca practical and powerful approach to multiple testing. J R Stat Soc B Methadol. 1995; 57:289-300.
27. Tibshirani R, Hastie T, Narasimhan B, Chu B. Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS. 2002; 99:6567-72.
28. Langfelder P, Horvath S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol. 2007; 1:54.
29. Therneau T. A package for survival analysis in s. version 2.38. 2009. ( http://cran.r-project.org/package=survival ). Accessed 2009.
30. Chuang HY, Lee E, Liu Y, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007; 3:140.
31. Newmann MEJ. Modularity and community structure in networks. Proc Natl Acad Sci. 2006; 103(20):8577-82.
32. Danon L, Duch J, Diaz-Guileram L, Arenas A. Comparing community structure identification. J Stat Mech. 2005; 2005(09):P09008.
33. Newman MEJ. Finding community structure in networks using the eigenvectors of matrices. Phys Rev E. 2006; 74:036104.
34. Arnedos M, Vicier C, Loi S, et al.Precision medicine for metastatic breast cancerlimitations and solutions. Nat Rev Clin Oncol. 2015; 12(12):693-704.
35. Kanehisa M, Furumichi M, Tanabe M, et al.Kegg: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017; 45:353-61.
36. Croft D, Sidiropoulos K, Garapati P, Gillespie M, et al.The reactome pathway knowledgebase. Nucleic Acids Res. 2014; 42:472-7.
37. Nishimura D. Biocarta. Biotech Softw Internet Rep. 2001; 2:112-20.
38. Ashburner M, Ball C, Blake J, Botstein D, et al.Gene ontology: tool for the unification of biology. the gene ontology consortium. Nat Genet. 2000; 25:25-9.
39. Subramanian A, Tamayo P, Mootha V, Mukherjee S, et al.Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2001; 105:15545-50.
40. Taylor I, Linding R, Warde-Farley D, Liu Y, et al.Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009; 27:199-204.
41. Chang CC, Lin CJ. Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol. 2011; 2(3):1-27.
42. Delong E, Delong D, Clarke-Pearson D. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Nat Biotechnol. 1988; 44:837-45.