Literature DB >> 27323952

Methods for identifying differentially methylated regions for sequence- and array-based data.

Dao-Peng Chen, Ying-Chao Lin, Cathy S J Fann.   

Abstract

DNA methylation is one of the most important epigenetic mechanisms, and participates in the pathogenic processes of many diseases. Differentially methylated regions (DMRs) in the genome have been reported and implicated in a number of different diseases, tissues and cell types, and are associated with gene expression levels. Therefore, identification of DMRs is one of the most critical and fundamental issues in dissecting the disease etiologies. Based on bisulfite conversion, advances in sequence- and array-based technologies have helped investigators study genome-wide DNA methylation. Many methods have been developed to detect DMRs, and they have revolutionized our understanding of DNA methylation and provided new insights into its role in diverse biological functions. According to data and region types, we discuss various methods in detecting DMRs, their utility and limitations comprehensively. We recommend using a few of the methods in the same data and region type to detect DMRs because they could be complementary to one another.
© The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  DNA methylation; Illumina 450k methylation array; bisulfite sequencing; differentially methylated regions

Mesh:

Year:  2016        PMID: 27323952     DOI: 10.1093/bfgp/elw018

Source DB:  PubMed          Journal:  Brief Funct Genomics        ISSN: 2041-2649            Impact factor:   4.241


  12 in total

1.  coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes.

Authors:  Lissette Gomez; Gabriel J Odom; Juan I Young; Eden R Martin; Lizhong Liu; Xi Chen; Anthony J Griswold; Zhen Gao; Lanyu Zhang; Lily Wang
Journal:  Nucleic Acids Res       Date:  2019-09-26       Impact factor: 16.971

2.  DDT exposure during pregnancy and DNA methylation alterations in female offspring in the Child Health and Development Study.

Authors:  Hui-Chen Wu; Barbara A Cohn; Piera M Cirillo; Regina M Santella; Mary Beth Terry
Journal:  Reprod Toxicol       Date:  2019-02-26       Impact factor: 3.143

3.  Aclust2.0: a revamped unsupervised R tool for Infinium methylation beadchips data analyses.

Authors:  Oladele A Oluwayiose; Haotian Wu; Feng Gao; Andrea A Baccarelli; Tamar Sofer; J Richard Pilsner
Journal:  Bioinformatics       Date:  2022-10-14       Impact factor: 6.931

4.  Detect differentially methylated regions using non-homogeneous hidden Markov model for methylation array data.

Authors:  Linghao Shen; Jun Zhu; Shuo-Yen Robert Li; Xiaodan Fan
Journal:  Bioinformatics       Date:  2017-12-01       Impact factor: 6.937

5.  Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm.

Authors:  Nicole White; Miles Benton; Daniel Kennedy; Andrew Fox; Lyn Griffiths; Rodney Lea; Kerrie Mengersen
Journal:  PLoS One       Date:  2017-09-28       Impact factor: 3.240

6.  RnBeads 2.0: comprehensive analysis of DNA methylation data.

Authors:  Fabian Müller; Michael Scherer; Yassen Assenov; Pavlo Lutsik; Jörn Walter; Thomas Lengauer; Christoph Bock
Journal:  Genome Biol       Date:  2019-03-14       Impact factor: 13.583

Review 7.  Cell-Free DNA Methylation Profiling Analysis-Technologies and Bioinformatics.

Authors:  Jinyong Huang; Liang Wang
Journal:  Cancers (Basel)       Date:  2019-11-06       Impact factor: 6.639

8.  Battle of epigenetic proportions: comparing Illumina's EPIC methylation microarrays and TruSeq targeted bisulfite sequencing.

Authors:  Jonathan A Heiss; Kasey J Brennan; Andrea A Baccarelli; Martha M Téllez-Rojo; Guadalupe Estrada-Gutiérrez; Robert O Wright; Allan C Just
Journal:  Epigenetics       Date:  2019-09-05       Impact factor: 4.528

9.  DNA methylation changes in endometrium and correlation with gene expression during the transition from pre-receptive to receptive phase.

Authors:  Viktorija Kukushkina; Vijayachitra Modhukur; Marina Suhorutšenko; Maire Peters; Reedik Mägi; Nilufer Rahmioglu; Agne Velthut-Meikas; Signe Altmäe; Francisco J Esteban; Jaak Vilo; Krina Zondervan; Andres Salumets; Triin Laisk-Podar
Journal:  Sci Rep       Date:  2017-06-20       Impact factor: 4.379

10.  Validation of differentially methylated microRNAs identified from an epigenome-wide association study; Sanger and next generation sequencing approaches.

Authors:  Laura J Smyth; Alexander P Maxwell; Katherine A Benson; Jill Kilner; Gareth J McKay; Amy Jayne McKnight
Journal:  BMC Res Notes       Date:  2018-10-29
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.