Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi

Bioinformatics

Volumn 33, Issue 4, 2017, Pages 558-560

  Fortin, Jean Philippe ^a   Triche, Timothy J ^b   Hansen, Kasper D ^a,c  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^c JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA METHYLATION; DNA MICROARRAY; DNA SEQUENCE; EPIGENETICS; HUMAN; PROCEDURES; SOFTWARE;

DNA METHYLATION; EPIGENOMICS; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SEQUENCE ANALYSIS, DNA; SOFTWARE;

EID: 85016984334     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw691     Document Type: Article

References (15)

1. Aryee, M.J. et al. (2014) Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics, 30, 1363-1369.
2. Bakulski, K.M. et al. (2016) DNA methylation of cord blood cell types: applications for mixed cell birth studies. Epigenetics, 11, 354-362.
3. Bibikova, M. et al. (2011) High density DNA methylation array with single CpG site resolution. Genomics, 98, 288-295.
4. Fortin, J.P. and Hansen, K.D. (2015) Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data. Genome Biol., 16, 180.
5. Fortin, J.P. et al. (2014a) Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol., 15, 503.
6. Fortin, J.P. et al. (2014b) shinymethyl: interactive quality control of illumina 450k DNA methylation arrays in R. F1000Research, 3, 175.
7. Gentleman, R.C. et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol., 5, R80.
8. Guintivano, J. et al. (2013) A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics, 8, 290-302.
9. Houseman, E.A. et al. (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform., 13, 86.
10. Huber, W. et al. (2015) Orchestrating high-throughput genomic analysis with bioconductor. Nat. Methods, 12, 115-121.
11. Jaffe, A.E. and Irizarry, R.A. (2014) Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol., 15, R31.
12. Maksimovic, J. et al. (2012) SWAN: Subset quantile Within-Array Normalization for Illumina Infinium HumanMethylation450 BeadChips. Genome Biol., 13, R44.
13. Moran, S. et al. (2016) Validation of a DNA methylation microarray for 850, 000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics, 8, 389-399.
14. Reinius, L.E. et al. (2012) Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. Plos One, 7, e41361.
15. Triche, T.J. et al. (2013) Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res., 41, e90.