Evidence map›Paper›PMID 26659027›Full record

ArticleScientific reports2015

CellMethy: Identification of a focal concordantly methylated pattern of CpGs revealed wide differences between normal and cancer tissues.

Fang Wang, Shaojun Zhang, Hongbo Liu, Yanjun Wei, Yihan Wang, Xiaole Han, Jianzhong Su, Dongwei Zhang, Baodong Xie, Yan Zhang

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Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Fang WangCollege of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
Shaojun ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
Hongbo LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
Yanjun WeiCollege of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
Yihan WangCollege of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
Xiaole HanCollege of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
Jianzhong SuCollege of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
Dongwei ZhangThe 2nd Affiliated Hospital, Harbin Medical University, Harbin, 150081, China.
Baodong XieThe Department of Cardiovascular Surgery, The Second Affiliated Hospital, Harbin Medical University, Harbin, 150081, China.
Yan ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA methylation patterns may serve as a key in determining cell phenotypes and functions. Adjacent CpG patterns may provide insight into methylation functional mechanisms. Some regions display different DNA methylation patterns between normal and cancer tissues, but the same average methylation level. Here, we developed a method (CellMethy) to infer a region in which all CpGs exhibit concordant methylation (CM) and to quantify the extent of CM in the region. Using simulation data, CellMethy showed high performance in discovering the concordant methylation patterns (AUC = 0.89). CellMethy was then applied to RRBS data including 11 normal tissues and 12 tumors. We found that the extent of CM exhibited wider differentials among tissues than did the average methylation levels from the CM regions, with 45% of CM regions occurring specifically in one tissue and mainly in tumors. Then, we identified CM regions through genome wide bisulfite sequencing (GWBS) data on breast cancer. Approximately 82% of CM regions revealed a significantly different extent of CM between cancer and normal tissues. CellMethy can accurately describe concordantly methylated regions, and the results suggest that CM might also serve as a stable marker of cell sub-populations.

Indexed as

Cell Line, TumorCpG IslandsDNA MethylationHep G2 CellsHumansJurkat CellsK562 CellsNeoplasms

Identifiers

PMID26659027
PMCPMC4674876

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