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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

Abstract

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.

Keywords

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

Abbreviations

PPI

Protein–protein interaction

RWR

The random walk with restart algorithm

MFS

Maximum function score

MIS

Maximum interaction score

GO

Gene ontology

KEGG

Kyoto encyclopedia of genes and genomes

Notes

Compliance with ethical standards

Funding

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)

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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

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