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Items: 1 to 20 of 112

1.

Identification of differentially methylated loci using wavelet-based functional mixed models.

Lee W, Morris JS.

Bioinformatics. 2016 Mar 1;32(5):664-72. doi: 10.1093/bioinformatics/btv659. Epub 2015 Nov 11.

2.

pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data.

Sun H, Wang Y, Chen Y, Li Y, Wang S.

Bioinformatics. 2017 Jun 15;33(12):1765-1772. doi: 10.1093/bioinformatics/btx064.

3.

Methyl-Analyzer--whole genome DNA methylation profiling.

Xin Y, Ge Y, Haghighi FG.

Bioinformatics. 2011 Aug 15;27(16):2296-7. doi: 10.1093/bioinformatics/btr356. Epub 2011 Jun 17.

4.

Genome-wide DNA methylation analysis reveals loci that distinguish different types of adipose tissue in obese individuals.

Macartney-Coxson D, Benton MC, Blick R, Stubbs RS, Hagan RD, Langston MA.

Clin Epigenetics. 2017 May 3;9:48. doi: 10.1186/s13148-017-0344-4. eCollection 2017.

5.

Global analysis of methylation profiles from high resolution CpG data.

Zhao N, Bell DA, Maity A, Staicu AM, Joubert BR, London SJ, Wu MC.

Genet Epidemiol. 2015 Feb;39(2):53-64. doi: 10.1002/gepi.21874. Epub 2014 Dec 23.

6.

Higher order methylation features for clustering and prediction in epigenomic studies.

Kapourani CA, Sanguinetti G.

Bioinformatics. 2016 Sep 1;32(17):i405-i412. doi: 10.1093/bioinformatics/btw432.

PMID:
27587656
7.

Epigenomic profiling of DNA methylation in paired prostate cancer versus adjacent benign tissue.

Geybels MS, Zhao S, Wong CJ, Bibikova M, Klotzle B, Wu M, Ostrander EA, Fan JB, Feng Z, Stanford JL.

Prostate. 2015 Dec;75(16):1941-50. doi: 10.1002/pros.23093. Epub 2015 Sep 18.

8.

Efficient detection of differentially methylated regions using DiMmeR.

Almeida D, Skov I, Silva A, Vandin F, Tan Q, Röttger R, Baumbach J.

Bioinformatics. 2017 Feb 15;33(4):549-551. doi: 10.1093/bioinformatics/btw657.

PMID:
27794558
9.

DBCAT: database of CpG islands and analytical tools for identifying comprehensive methylation profiles in cancer cells.

Kuo HC, Lin PY, Chung TC, Chao CM, Lai LC, Tsai MH, Chuang EY.

J Comput Biol. 2011 Aug;18(8):1013-7. doi: 10.1089/cmb.2010.0038. Epub 2011 Jan 8.

PMID:
21214365
10.

DM-BLD: differential methylation detection using a hierarchical Bayesian model exploiting local dependency.

Wang X, Gu J, Hilakivi-Clarke L, Clarke R, Xuan J.

Bioinformatics. 2017 Jan 15;33(2):161-168. doi: 10.1093/bioinformatics/btw596. Epub 2016 Sep 11.

11.

A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data.

Wang H, He C, Kushwaha G, Xu D, Qiu J.

BMC Bioinformatics. 2016 Jan 11;17 Suppl 1:7. doi: 10.1186/s12859-015-0850-3.

12.

Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands.

Dai W, Teodoridis JM, Graham J, Zeller C, Huang TH, Yan P, Vass JK, Brown R, Paul J.

BMC Bioinformatics. 2008 Aug 8;9:337. doi: 10.1186/1471-2105-9-337.

13.

Genome-wide DNA methylome variation in two genetically distinct chicken lines using MethylC-seq.

Li J, Li R, Wang Y, Hu X, Zhao Y, Li L, Feng C, Gu X, Liang F, Lamont SJ, Hu S, Zhou H, Li N.

BMC Genomics. 2015 Oct 23;16:851. doi: 10.1186/s12864-015-2098-8.

14.

Elucidating the landscape of aberrant DNA methylation in hepatocellular carcinoma.

Song MA, Tiirikainen M, Kwee S, Okimoto G, Yu H, Wong LL.

PLoS One. 2013;8(2):e55761. doi: 10.1371/journal.pone.0055761. Epub 2013 Feb 20.

15.

Genome-wide DNA methylation profiling using Infinium® assay.

Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R, Gunderson KL.

Epigenomics. 2009 Oct;1(1):177-200. doi: 10.2217/epi.09.14.

16.

Comparison of methyl-DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain (MBD) protein capture for genome-wide DNA methylation analysis reveal CpG sequence coverage bias.

Nair SS, Coolen MW, Stirzaker C, Song JZ, Statham AL, Strbenac D, Robinson MD, Clark SJ.

Epigenetics. 2011 Jan;6(1):34-44. doi: 10.4161/epi.6.1.13313. Epub 2011 Jan 1.

PMID:
20818161
17.

Large-scale comparative epigenomics reveals hierarchical regulation of non-CG methylation in Arabidopsis.

Zhang Y, Harris CJ, Liu Q, Liu W, Ausin I, Long Y, Xiao L, Feng L, Chen X, Xie Y, Chen X, Zhan L, Feng S, Li JJ, Wang H, Zhai J, Jacobsen SE.

Proc Natl Acad Sci U S A. 2018 Jan 30;115(5):E1069-E1074. doi: 10.1073/pnas.1716300115. Epub 2018 Jan 16.

18.

DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data.

Gaspar JM, Hart RP.

BMC Bioinformatics. 2017 Nov 29;18(1):528. doi: 10.1186/s12859-017-1909-0.

19.

Predicting methylation status of CpG islands in the human brain.

Fang F, Fan S, Zhang X, Zhang MQ.

Bioinformatics. 2006 Sep 15;22(18):2204-9. Epub 2006 Jul 12.

PMID:
16837523
20.

Linking the epigenome to the genome: correlation of different features to DNA methylation of CpG islands.

Wrzodek C, Büchel F, Hinselmann G, Eichner J, Mittag F, Zell A.

PLoS One. 2012;7(4):e35327. doi: 10.1371/journal.pone.0035327. Epub 2012 Apr 30.

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