title: CellMethy: Identification of a focal concordantly methylated pattern of CpGs revealed wide differences between normal and cancer tissues.
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.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
authors.forename: Fang
authors.initials: F
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
authors.forename: Shaojun
authors.initials: S
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
authors.forename: Hongbo
authors.initials: H
authors.lastname: Liu
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Wei
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
authors.forename: Yihan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
authors.forename: Xiaole
authors.initials: X
authors.lastname: Han
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
authors.forename: Jianzhong
authors.initials: J
authors.lastname: Su
authors.affiliation: The 2nd Affiliated Hospital, Harbin Medical University, Harbin, 150081, China.
authors.forename: Dongwei
authors.initials: D
authors.lastname: Zhang
authors.affiliation: The Department of Cardiovascular Surgery, The Second Affiliated Hospital, Harbin Medical University, Harbin, 150081, China.
authors.forename: Baodong
authors.initials: B
authors.lastname: Xie
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin.
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D045744:Cell Line, Tumor; D018899:CpG Islands / Q000235:genetics*; D019175:DNA Methylation / Q000235:genetics*; D056945:Hep G2 Cells; D006801:Humans; D019169:Jurkat Cells; D020014:K562 Cells; D009369:Neoplasms / Q000235:genetics*