A computational strategy to predict DNA methylation by integrating cell-type specific 450K array data and common DNA sequence features. We developed a computational model that is trained on 14 tissues with both whole genome bisulfite sequencing and 450K array data. This model integrates information derived from the similarity of local methylation pattern between tissues, the methylation information of flanking CpG sites and the methylation tendency of flanking DNA sequences. When applying to a new sample, our model only requires input of 450K array data and avoids the need of histone modification or MeDIP-seq/MRE-seq data that are not always available, which significantly expands its applicability.

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  • (Fan et al., 2016) Computationally expanding Infinium HumanMethylation450 BeadChip array data to reveal distinct DNA methylation patterns of rheumatoid arthritis. Bioinformatics.
    PMID: 26883487


School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China; Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, USA; Department of Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA

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This project was supported by the National Natural Science Foundation of China under No. 61503061 (SCF) and grants from the Rheumatology Research Foundation, the Arthritis Foundation, and the National Institutes of Arthritis, Musculoskeletal and Skin Diseases (RO1065066).

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