Molecular Genetics and Genomics

, Volume 293, Issue 1, pp 293–301 | Cite as

A computational method using the random walk with restart algorithm for identifying novel epigenetic factors

  • JiaRui Li
  • Lei Chen
  • ShaoPeng Wang
  • YuHang Zhang
  • XiangYin KongEmail author
  • Tao HuangEmail author
  • Yu-Dong CaiEmail author
Methods Paper


Epigenetic regulation has long been recognized as a significant factor in various biological processes, such as development, transcriptional regulation, spermatogenesis, and chromosome stabilization. Epigenetic alterations lead to many human diseases, including cancer, depression, autism, and immune system defects. Although efforts have been made to identify epigenetic regulators, it remains a challenge to systematically uncover all the components of the epigenetic regulation in the genome level using experimental approaches. The advances of constructing protein–protein interaction (PPI) networks provide an excellent opportunity to identify novel epigenetic factors computationally in the genome level. In this study, we identified potential epigenetic factors by using a computational method that applied the random walk with restart (RWR) algorithm on a protein–protein interaction (PPI) network using reported epigenetic factors as seed nodes. False positives were identified by their specific roles in the PPI network or by a low-confidence interaction and a weak functional relationship with epigenetic regulators. After filtering out the false positives, 26 candidate epigenetic factors were finally accessed. According to previous studies, 22 of these are thought to be involved in epigenetic regulation, suggesting the robustness of our method. Our study provides a novel computational approach which successfully identified 26 potential epigenetic factors, paving the way on deepening our understandings on the epigenetic mechanism.


Epigenetic regulation Epigenetic factor Random walk with restart Protein–protein interaction network 



Protein–protein interaction


The random walk with restart algorithm


Maximum function score


Maximum interaction score


Gene ontology


Kyoto encyclopedia of genes and genomes


Compliance with ethical standards


This study was supported by the National Natural Science Foundation of China (31371335, 31701151), Natural Science Foundation of Shanghai (17ZR1412500), Shanghai Sailing Program and The Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) (2016245).

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Supplementary material

438_2017_1374_MOESM1_ESM.xlsx (25 kb)
Supplementary material 1 Table S1 The 732 Ensembl IDs of the 720 genes encoding known epigenetic factors (XLSX 25 kb)
438_2017_1374_MOESM2_ESM.xlsx (211 kb)
Supplementary material 2 Table S2 The 4372 potential epigenetic factors whose probabilities were larger than 10−5 (listed in the first column) and their measurements in three tests (XLSX 211 kb)
438_2017_1374_MOESM3_ESM.xlsx (28 kb)
Supplementary material 3 Table S3 Interactions between candidate epigenetic factors and known epigenetic regulators (XLSX 27 kb)


  1. Allfrey VG, Faulkner R, Mirsky AE (1964) Acetylation and methylation of histones and their possible role in the regulation of RNA synthesis. Proc Natl Acad Sci USA 51:786–794CrossRefPubMedPubMedCentralGoogle Scholar
  2. Allis CD, Jenuwein T (2016) The molecular hallmarks of epigenetic control. Nat Rev Genet 17:487–500CrossRefPubMedGoogle Scholar
  3. Anway MD, Cupp AS, Uzumcu M, Skinner MK (2005) Epigenetic transgenerational actions of endocrine disruptors and male fertility. Science 308:1466–1469CrossRefPubMedGoogle Scholar
  4. Balakrishnan L, Stewart J, Polaczek P, Campbell JL, Bambara RA (2010) Acetylation of Dna2 endonuclease/helicase and flap endonuclease 1 by p300 promotes DNA stability by creating long flap intermediates. J Biol Chem 285:4398–4404CrossRefPubMedGoogle Scholar
  5. Berger SL, Sassone-Corsi P (2016) Metabolic signaling to chromatin. Cold Spring Harb Perspect Biol 8:a019463CrossRefPubMedGoogle Scholar
  6. Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry B, Meissner A, Wernig M, Plath K, Jaenisch R, Wagschal A, Feil R, Schreiber SL, Lander ES (2006) A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell 125:315–326CrossRefPubMedGoogle Scholar
  7. Bestor TH, Ingram VM (1983) Two DNA methyltransferases from murine erythroleukemia cells: purification, sequence specificity, and mode of interaction with DNA. Proc Natl Acad Sci USA 80:5559–5563CrossRefPubMedPubMedCentralGoogle Scholar
  8. Blaschke K, Ebata KT, Karimi MM, Zepeda-Martinez JA, Goyal P, Mahapatra S, Tam A, Laird DJ, Hirst M, Rao A, Lorincz MC, Ramalho-Santos M (2013) Vitamin C induces Tet-dependent DNA demethylation and a blastocyst-like state in ES cells. Nature 500:222–226CrossRefPubMedPubMedCentralGoogle Scholar
  9. Brownell JE, Zhou J, Ranalli T, Kobayashi R, Edmondson DG, Roth SY, Allis CD (1996) Tetrahymena histone acetyltransferase A: a homolog to yeast Gcn5p linking histone acetylation to gene activation. Cell 84:843–851CrossRefPubMedGoogle Scholar
  10. Campos EI, Smits AH, Kang YH, Landry S, Escobar TM, Nayak S, Ueberheide BM, Durocher D, Vermeulen M, Hurwitz J, Reinberg D (2015) Analysis of the histone H3.1 interactome: a suitable chaperone for the right event. Mol Cell 60:697–709CrossRefPubMedPubMedCentralGoogle Scholar
  11. Chen L, Hao Xing Z, Huang T, Shu Y, Huang G, Li H-P (2016a) Application of the shortest path algorithm for the discovery of breast cancer-related genes. Curr Bioinform 11:51–58CrossRefGoogle Scholar
  12. Chen L, Wang B, Wang S, Yang J, Hu J, Xie Z, Wang Y, Huang T, Cai YD, Xie Z (2016b) OPMSP: a computational method integrating protein interaction and sequence information for the identification of novel putative oncogenes. Protein Pept Lett 23:1081–1094CrossRefPubMedGoogle Scholar
  13. Chen L, Yang J, Huang T, Kong X, Lu L, Cai Y-D (2016c) Mining for novel tumor suppressor genes using a shortest path approach. J Biomol Struct Dyn 34:664–675CrossRefPubMedGoogle Scholar
  14. Chen L, Zhang YH, Huang T, Cai YD (2016d) Identifying novel protein phenotype annotations by hybridizing protein-protein interactions and protein sequence similarities. Mol Genet Genomics 291:913–934CrossRefPubMedGoogle Scholar
  15. Chen L, Zhang YH, Zheng M, Huang T, Cai YD (2016e) Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds. Mol Genet Genomics 291:2065–2079CrossRefPubMedGoogle Scholar
  16. Chen L, Yang J, Xing Z, Yuan F, Shu Y, Zhang Y, Kong X, Huang T, Li H, Cai Y-D (2017) An integrated method for the identification of novel genes related to oral cancer. PLoS ONE 12:e0175185CrossRefPubMedPubMedCentralGoogle Scholar
  17. Consortium GO (2015) Gene Ontology Consortium: going forward. Nucleic Acids Res 43:D1049–D1056CrossRefGoogle Scholar
  18. Depre C, Rider MH, Hue L (1998) Mechanisms of control of heart glycolysis. Eur J Biochem 258:277–290CrossRefPubMedGoogle Scholar
  19. Duro E, Lundin C, Ask K, Sanchez-Pulido L, MacArtney TJ, Toth R, Ponting CP, Groth A, Helleday T, Rouse J (2010) Identification of the MMS22L–TONSL complex that promotes homologous recombination. Mol Cell 40:632–644CrossRefPubMedGoogle Scholar
  20. Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, Heine-Suner D, Cigudosa JC, Urioste M, Benitez J, Boix-Chornet M, Sanchez-Aguilera A, Ling C, Carlsson E, Poulsen P, Vaag A, Stephan Z, Spector TD, Wu YZ, Plass C, Esteller M (2005) Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci USA 102:10604–10609CrossRefPubMedPubMedCentralGoogle Scholar
  21. Glozak MA, Seto E (2009) Acetylation/deacetylation modulates the stability of DNA replication licensing factor Cdt1. J Biol Chem 284:11446–11453CrossRefPubMedPubMedCentralGoogle Scholar
  22. Gruenbaum Y, Cedar H, Razin A (1982) Substrate and sequence specificity of a eukaryotic DNA methylase. Nature 295:620–622CrossRefPubMedGoogle Scholar
  23. Gui T, Dong X, Li R, Li Y, Wang Z (2015) Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis. J Comput Biol 22:63–71CrossRefPubMedGoogle Scholar
  24. Hansford RG, Zorov D (1998) Role of mitochondrial calcium transport in the control of substrate oxidation. Mol Cell Biochem 184:359–369CrossRefPubMedGoogle Scholar
  25. Hsu JM, Lee YC, Yu CT, Huang CY (2004) Fbx7 functions in the SCF complex regulating Cdk1-cyclin B-phosphorylated hepatoma up-regulated protein (HURP) proteolysis by a proline-rich region. J Biol Chem 279:32592–32602CrossRefPubMedGoogle Scholar
  26. Hu L, Huang T, Shi X, Lu WC, Cai YD, Chou KC (2011) Predicting functions of proteins in mouse based on weighted protein–protein interaction network and protein hybrid properties. PLoS ONE 6:e14556CrossRefPubMedPubMedCentralGoogle Scholar
  27. Huang G, Chu C, Huang T, Kong X, Zhang Y, Zhang N, Cai YD (2016) Exploring mouse protein function via multiple approaches. PLoS ONE 11:e0166580CrossRefPubMedPubMedCentralGoogle Scholar
  28. Hue L, Rider MH (1987) Role of fructose 2,6-bisphosphate in the control of glycolysis in mammalian tissues. Biochem J 245:313–324CrossRefPubMedPubMedCentralGoogle Scholar
  29. Imai S, Armstrong CM, Kaeberlein M, Guarente L (2000) Transcriptional silencing and longevity protein Sir2 is an NAD-dependent histone deacetylase. Nature 403:795–800CrossRefPubMedGoogle Scholar
  30. Ingrosso D, Cimmino A, Perna AF, Masella L, De Santo NG, De Bonis ML, Vacca M, D’Esposito M, D’Urso M, Galletti P, Zappia V (2003) Folate treatment and unbalanced methylation and changes of allelic expression induced by hyperhomocysteinaemia in patients with uraemia. Lancet 361:1693–1699CrossRefPubMedGoogle Scholar
  31. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30CrossRefPubMedPubMedCentralGoogle Scholar
  32. Kohler S, Bauer S, Horn D, Robinson PN (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82:949–958CrossRefPubMedPubMedCentralGoogle Scholar
  33. Lachner M, O’Carroll D, Rea S, Mechtler K, Jenuwein T (2001) Methylation of histone H3 lysine 9 creates a binding site for HP1 proteins. Nature 410:116–120CrossRefPubMedGoogle Scholar
  34. Li E, Zhang Y (2014) DNA methylation in mammals. Cold Spring Harb Perspect Biol 6:a019133CrossRefPubMedPubMedCentralGoogle Scholar
  35. Lin HM, Zhao L, Cheng SY (2002) Cyclin D1 is a ligand-independent co-repressor for thyroid hormone receptors. J Biol Chem 277:28733–28741CrossRefPubMedGoogle Scholar
  36. Liu L, Chen L, Zhang YH, Wei L, Cheng S, Kong X, Zheng M, Huang T, Cai YD (2017) Analysis and prediction of drug–drug interaction by minimum redundancy maximum relevance and incremental feature selection. J Biomol Struct Dyn 35:312–329CrossRefPubMedGoogle Scholar
  37. Ma Z, Chang MJ, Shah RC, Benveniste EN (2005) Interferon-gamma-activated STAT-1alpha suppresses MMP-9 gene transcription by sequestration of the coactivators CBP/p300. J Leukoc Biol 78:515–523CrossRefPubMedGoogle Scholar
  38. McClintock B (1951) Chromosome organization and genic expression. Cold Spring Harbor Sympos Quant Biol 16:13–47CrossRefGoogle Scholar
  39. Medvedeva YA, Lennartsson A, Ehsani R, Kulakovskiy IV, Vorontsov IE, Panahandeh P, Khimulya G, Kasukawa T, Consortium F, Drablos F (2015a) EpiFactors: a comprehensive database of human epigenetic factors and complexes. Database (Oxford) 2015:bav067CrossRefGoogle Scholar
  40. Medvedeva YA, Lennartsson A, Ehsani R, Kulakovskiy IV, Vorontsov IE, Panahandeh P, Khimulya G, Kasukawa T, Drabløs F (2015b) EpiFactors: a comprehensive database of human epigenetic factors and complexes. Database 2015:bav067CrossRefPubMedPubMedCentralGoogle Scholar
  41. Meehan RR, Lewis JD, McKay S, Kleiner EL, Bird AP (1989) Identification of a mammalian protein that binds specifically to DNA containing methylated CpGs. Cell 58:499–507CrossRefPubMedGoogle Scholar
  42. Muller HJ, Altenburg E (1930) The frequency of translocations produced by X-rays in Drosophila. Genetics 15:283–311PubMedPubMedCentralGoogle Scholar
  43. Nakayama J, Rice JC, Strahl BD, Allis CD, Grewal SI (2001) Role of histone H3 lysine 9 methylation in epigenetic control of heterochromatin assembly. Science 292:110–113CrossRefPubMedGoogle Scholar
  44. Ng KL, Ciou JS, Huang CH (2010) Prediction of protein functions based on function–function correlation relations. Comput Biol Med 40:300–305CrossRefPubMedGoogle Scholar
  45. Ono T, Kitaura H, Ugai H, Murata T, Yokoyama KK, Iguchi-Ariga SM, Ariga H (2000) TOK-1, a novel p21Cip1-binding protein that cooperatively enhances p21-dependent inhibitory activity toward CDK2 kinase. J Biol Chem 275:31145–31154CrossRefPubMedGoogle Scholar
  46. Piwko W, Olma MH, Held M, Bianco JN, Pedrioli PG, Hofmann K, Pasero P, Gerlich DW, Peter M (2010) RNAi-based screening identifies the Mms22L–Nfkbil2 complex as a novel regulator of DNA replication in human cells. EMBO J 29:4210–4222CrossRefPubMedPubMedCentralGoogle Scholar
  47. Ray S, Sherman CT, Lu M, Brasier AR (2002) Angiotensinogen gene expression is dependent on signal transducer and activator of transcription 3-mediated p300/cAMP response element binding protein-binding protein coactivator recruitment and histone acetyltransferase activity. Mol Endocrinol 16:824–836CrossRefPubMedGoogle Scholar
  48. Rea S, Eisenhaber F, O’Carroll D, Strahl BD, Sun ZW, Schmid M, Opravil S, Mechtler K, Ponting CP, Allis CD, Jenuwein T (2000) Regulation of chromatin structure by site-specific histone H3 methyltransferases. Nature 406:593–599CrossRefPubMedGoogle Scholar
  49. Shi Y, Lan F, Matson C, Mulligan P, Whetstine JR, Cole PA, Casero RA, Shi Y (2004) Histone demethylation mediated by the nuclear amine oxidase homolog LSD1. Cell 119:941–953CrossRefPubMedGoogle Scholar
  50. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539CrossRefPubMedGoogle Scholar
  51. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452CrossRefPubMedGoogle Scholar
  52. Taunton J, Hassig CA, Schreiber SL (1996) A mammalian histone deacetylase related to the yeast transcriptional regulator Rpd3p. Science 272:408–411CrossRefPubMedGoogle Scholar
  53. Tolga C, Çamoğlu O, Singh AK (2005) Analysis of protein–protein interaction networks using random walks. In: Proceedings of the fifth international workshop on bioinformatics. ACM, Chicago, pp 61–68Google Scholar
  54. Tsukada Y, Fang J, Erdjument-Bromage H, Warren ME, Borchers CH, Tempst P, Zhang Y (2006) Histone demethylation by a family of JmjC domain-containing proteins. Nature 439:811–816CrossRefPubMedGoogle Scholar
  55. Willbanks A, Leary M, Greenshields M, Tyminski C, Heerboth S, Lapinska K, Haskins K, Sarkar S (2016) The Evolution of Epigenetics: from prokaryotes to humans and its biological consequences. Genet Epigenet 8:25–36CrossRefPubMedPubMedCentralGoogle Scholar
  56. Yang J, Chen L, Kong X, Huang T, Cai YD (2014) Analysis of tumor suppressor genes based on gene ontology and the KEGG pathway. PLoS ONE 9:e107202CrossRefPubMedPubMedCentralGoogle Scholar
  57. Yoon YM, Baek KH, Jeong SJ, Shin HJ, Ha GH, Jeon AH, Hwang SG, Chun JS, Lee CW (2004) WD repeat-containing mitotic checkpoint proteins act as transcriptional repressors during interphase. FEBS Lett 575:23–29CrossRefPubMedGoogle Scholar
  58. Zalmas LP, Coutts AS, Helleday T, La Thangue NB (2013) E2F-7 couples DNA damage-dependent transcription with the DNA repair process. Cell Cycle 12:3037–3051CrossRefPubMedPubMedCentralGoogle Scholar
  59. Zhang J, Xing Z, Ma M, Wang N, Cai YD, Chen L, Xu X (2014) Gene ontology and KEGG enrichment analyses of genes related to age-related macular degeneration. Biomed Res Int 2014:450386PubMedPubMedCentralGoogle Scholar
  60. Zhang J, Yang J, Huang T, Shu Y, Chen L (2016) Identification of novel proliferative diabetic retinopathy related genes on protein–protein interaction network. Neurocomputing 217:63–72CrossRefGoogle Scholar
  61. Zoghbi HY, Beaudet AL (2016) Epigenetics and human disease. Cold Spring Harb Perspect Biol 8:a019497CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of Life SciencesShanghai UniversityShanghaiPeople’s Republic of China
  2. 2.College of Information EngineeringShanghai Maritime UniversityShanghaiPeople’s Republic of China
  3. 3.Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiPeople’s Republic of China

Personalised recommendations