MethCP: Differentially Methylated Region Detection with Change Point Models

被引:6
|
作者
Gong, Boying [1 ]
Purdom, Elizabeth [1 ,2 ]
机构
[1] Univ Calif Berkeley, Div Biostat, Berkeley, CA 94720 USA
[2] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA
关键词
bisulfite sequencing; change point detection; differential methylation;
D O I
10.1089/cmb.2019.0326
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Whole-genome bisulfite sequencing (WGBS) provides a precise measure of methylation across the genome, yet presents a challenge in identifying differentially methylated regions (DMRs) between different conditions. Many methods have been developed, which focus primarily on the setting of two-group comparison. We develop a DMR detecting method MethCP for WGBS data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. For simple two-group comparison, we show that our method outperforms developed methods in accurately detecting the complete DMR on a simulated data set and an Arabidopsis data set. Moreover, we show that MethCP is capable of detecting wide regions with small effect sizes, which can be common in some settings, but existing techniques are poor in detecting such DMRs. We also demonstrate the use of MethCP for time-course data on another data set after methylation throughout seed germination in Arabidopsis.
引用
收藏
页码:458 / 471
页数:14
相关论文
共 50 条
  • [32] A differentially methylated region of the DAZ1 gene in spermatic and somatic cells
    Zuo-Xiang Li~(1
    Asian Journal of Andrology, 2006, (01) : 61 - 67
  • [33] The differentially methylated region of MEG8 is hypermethylated in patients with Temple syndrome
    Bens, Susanne
    Kolarova, Julia
    Gillessen-Kaesbach, Gabriele
    Buiting, Karin
    Beygo, Jasmin
    Caliebe, Almuth
    Ammerpohl, Ole
    Siebert, Reiner
    EPIGENOMICS, 2015, 7 (07) : 1089 - 1097
  • [34] Accelerating the detection of DNA differentially methylated regions using multiple GPUs
    Reano, Carlos
    Olanda, Ricardo
    Baydal, Elvira
    Perez, Mariano
    Orduna, Juan M.
    JOURNAL OF SUPERCOMPUTING, 2024, 80 (09): : 13386 - 13410
  • [35] Detection of β cell death in diabetes using differentially methylated circulating DNA
    Akirav, Eitan M.
    Lebastchi, Jasmin
    Galvan, Eva M.
    Henegariu, Octavian
    Akirav, Michael
    Ablamunits, Vitaly
    Lizardi, Paul M.
    Herold, Kevan C.
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2011, 108 (47) : 19018 - 19023
  • [36] Characterization of the differentially methylated region of the Impactgene that exhibits Glires-specific imprinting
    Kohji Okamura
    Richard F Wintle
    Stephen W Scherer
    Genome Biology, 9
  • [37] Increased methylation at differentially methylated region of GNAS in infants born to gestational diabetes
    Chen, Danqing
    Zhang, Aiping
    Fang, Min
    Fang, Rong
    Ge, Jiamei
    Jiang, Yuan
    Zhang, Hong
    Han, Cong
    Ye, Xiaoqun
    Huang, Hefeng
    Liu, Yun
    Dong, Minyue
    BMC MEDICAL GENETICS, 2014, 15
  • [38] A differentially methylated region of the DAZ1 gene in spermatic and somatic cells
    Li, ZX
    Ma, X
    Wang, ZH
    ASIAN JOURNAL OF ANDROLOGY, 2006, 8 (01) : 61 - 67
  • [39] Assessing genome-wide significance for the detection of differentially methylated regions
    Page, Christian M.
    Vos, Linda
    Rounge, Trine B.
    Harbo, Hanne F.
    Andreassen, Bettina K.
    STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 2018, 17 (05)
  • [40] Detection of Differentially Methylated Regions Using Bayesian Curve Credible Bands
    Park J.
    Lin S.
    Statistics in Biosciences, 2018, 10 (1) : 20 - 40